Yulia Tsvetkov


2023

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KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding
Shangbin Feng | Zhaoxuan Tan | Wenqian Zhang | Zhenyu Lei | Yulia Tsvetkov
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

With the advent of pre-trained language models (LMs), increasing research efforts have been focusing on infusing commonsense and domain-specific knowledge to prepare LMs for downstream tasks. These works attempt to leverage knowledge graphs, the de facto standard of symbolic knowledge representation, along with pre-trained LMs. While existing approaches leverage external knowledge, it remains an open question how to jointly incorporate knowledge graphs represented in varying contexts — from local (e.g., sentence), document-level, to global knowledge, to enable knowledge-rich and interpretable exchange across contexts. In addition, incorporating varying contexts can especially benefit long document understanding tasks that leverage pre-trained LMs, typically bounded by the input sequence length. In light of these challenges, we propose KALM, a language model that jointly leverages knowledge in local, document-level, and global contexts for long document understanding. KALM firstly encodes long documents and knowledge graphs into the three knowledge-aware context representations. KALM then processes each context with context-specific layers. These context-specific layers are followed by a ContextFusion layer that facilitates knowledge exchange to derive an overarching document representation. Extensive experiments demonstrate that KALM achieves state-of-the-art performance on three long document understanding tasks across 6 datasets/settings. Further analyses reveal that the three knowledge-aware contexts are complementary and they all contribute to model performance, while the importance and information exchange patterns of different contexts vary on different tasks and datasets.

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SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control
Xiaochuang Han | Sachin Kumar | Yulia Tsvetkov
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Despite the growing success of diffusion models in continuous-valued domains (e.g., images), similar efforts for discrete domains such as text have yet to match the performance of autoregressive language models. In this work, we present SSD-LM—a diffusion-based language model with two key design choices. First, SSD-LM is semi-autoregressive, iteratively generating blocks of text, allowing for flexible output length at decoding time while enabling local bidirectional context updates. Second, it is simplex-based, performing diffusion on the natural vocabulary space rather than a learned latent space, allowing us to incorporate classifier guidance and modular control using off-the-shelf classifiers without any adaptation. We evaluate SSD-LM on unconstrained text generation benchmarks, and show that it matches or outperforms strong autoregressive GPT-2 models across standard quality and diversity metrics, while vastly outperforming diffusion-based baselines. On controlled text generation, SSD-LM also outperforms competitive baselines, with an extra advantage in modularity.

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From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
Shangbin Feng | Chan Young Park | Yuhan Liu | Yulia Tsvetkov
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Language models (LMs) are pretrained on diverse data sources—news, discussion forums, books, online encyclopedias. A significant portion of this data includes facts and opinions which, on one hand, celebrate democracy and diversity of ideas, and on the other hand are inherently socially biased. Our work develops new methods to (1) measure media biases in LMs trained on such corpora, along social and economic axes, and (2) measure the fairness of downstream NLP models trained on top of politically biased LMs. We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks. Our findings reveal that pretrained LMs do have political leanings which reinforce the polarization present in pretraining corpora, propagating social biases into hate speech predictions and media biases into misinformation detectors. We discuss the implications of our findings for NLP research and propose future directions to mitigate unfairness.

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On the Blind Spots of Model-Based Evaluation Metrics for Text Generation
Tianxing He | Jingyu Zhang | Tianle Wang | Sachin Kumar | Kyunghyun Cho | James Glass | Yulia Tsvetkov
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this work, we explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics: stress tests with synthetic data. Basically, we design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. We examine a range of recently proposed evaluation metrics based on pretrained language models, for the tasks of open-ended generation, translation, and summarization. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics. For example, we find that BERTScore is confused by truncation errors in summarization, and MAUVE (built on top of GPT-2) is insensitive to errors at the beginning or middle of generations. Further, we investigate the reasons behind these blind spots and suggest practical workarounds for a more reliable evaluation of text generation. We have released our code and data at https://github.com/cloudygoose/blindspot_nlg.

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Understanding In-Context Learning via Supportive Pretraining Data
Xiaochuang Han | Daniel Simig | Todor Mihaylov | Yulia Tsvetkov | Asli Celikyilmaz | Tianlu Wang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In-context learning (ICL) improves language models’ performance on a variety of NLP tasks by simply demonstrating a handful of examples at inference time. It is not well understood why ICL ability emerges, as the model has never been specifically trained on such demonstrations. Unlike prior work that explores implicit mechanisms behind ICL, we study ICL via investigating the pretraining data. Specifically, we first adapt an iterative, gradient-based approach to find a small subset of pretraining data that supports ICL. We observe that a continued pretraining on this small subset significantly improves the model’s ICL ability, by up to 18%. We then compare the supportive subset constrastively with random subsets of pretraining data and discover: (1) The supportive pretraining data to ICL do not have a higher domain relevance to downstream tasks. (2) The supportive pretraining data have a higher mass of rarely occurring, long-tail tokens. (3) The supportive pretraining data are challenging examples where the information gain from long-range context is below average, indicating learning to incorporate difficult long-range context encourages ICL. Our work takes a first step towards understanding ICL via analyzing instance-level pretraining data. Our insights have a potential to enhance the ICL ability of language models by actively guiding the construction of pretraining data in the future.

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Minding Language Models’ (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker
Melanie Sclar | Sachin Kumar | Peter West | Alane Suhr | Yejin Choi | Yulia Tsvetkov
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Theory of Mind (ToM)—the ability to reason about the mental states of other people—is a key element of our social intelligence. Yet, despite their ever more impressive performance, large-scale neural language models still lack basic theory of mind capabilities out-of-the-box. We posit that simply scaling up models will not imbue them with theory of mind due to the inherently symbolic and implicit nature of the phenomenon, and instead investigate an alternative: can we design a decoding-time algorithm that enhances theory of mind of off-the-shelf neural language models without explicit supervision? We present SymbolicToM, a plug-and-play approach to reason about the belief states of multiple characters in reading comprehension tasks via explicit symbolic representation. More concretely, our approach tracks each entity’s beliefs, their estimation of other entities’ beliefs, and higher-order levels of reasoning, all through graphical representations, allowing for more precise and interpretable reasoning than previous approaches. Empirical results on the well-known ToMi benchmark (Le et al., 2019) demonstrate that SymbolicToM dramatically enhances off-the-shelf neural networks’ theory of mind in a zero-shot setting while showing robust out-of-distribution performance compared to supervised baselines. Our work also reveals spurious patterns in existing theory of mind benchmarks, emphasizing the importance of out-of-distribution evaluation and methods that do not overfit a particular dataset.

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LEXPLAIN: Improving Model Explanations via Lexicon Supervision
Orevaoghene Ahia | Hila Gonen | Vidhisha Balachandran | Yulia Tsvetkov | Noah A. Smith
Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)

Model explanations that shed light on the model’s predictions are becoming a desired additional output of NLP models, alongside their predictions. Challenges in creating these explanations include making them trustworthy and faithful to the model’s predictions. In this work, we propose a novel framework for guiding model explanations by supervising them explicitly. To this end, our method, LEXplain, uses task-related lexicons to directly supervise model explanations. This approach consistently improves the model’s explanations without sacrificing performance on the task, as we demonstrate on sentiment analysis and toxicity detection. Our analyses show that our method also demotes spurious correlations (i.e., with respect to African American English dialect) when performing the task, improving fairness.

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Unsupervised Keyphrase Extraction via Interpretable Neural Networks
Rishabh Joshi | Vidhisha Balachandran | Emily Saldanha | Maria Glenski | Svitlana Volkova | Yulia Tsvetkov
Findings of the Association for Computational Linguistics: EACL 2023

Keyphrase extraction aims at automatically extracting a list of “important” phrases representing the key concepts in a document. Prior approaches for unsupervised keyphrase extraction resorted to heuristic notions of phrase importance via embedding clustering or graph centrality, requiring extensive domain expertise. Our work presents a simple alternative approach which defines keyphrases as document phrases that are salient for predicting the topic of the document. To this end, we propose INSPECT—an approach that uses self-explaining models for identifying influential keyphrases in a document by measuring the predictive impact of input phrases on the downstream task of the document topic classification. We show that this novel method not only alleviates the need for ad-hoc heuristics but also achieves state-of-the-art results in unsupervised keyphrase extraction in four datasets across two domains: scientific publications and news articles.

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On the Zero-Shot Generalization of Machine-Generated Text Detectors
Xiao Pu | Jingyu Zhang | Xiaochuang Han | Yulia Tsvetkov | Tianxing He
Findings of the Association for Computational Linguistics: EMNLP 2023

The rampant proliferation of large language models, fluent enough to generate text indistinguishable from human-written language, gives unprecedented importance to the detection of machine-generated text. This work is motivated by an important research question: How will the detectors of machine-generated text perform on outputs of a new generator, that the detectors were not trained on? We begin by collecting generation data from a wide range of LLMs, and train neural detectors on data from each generator and test its performance on held-out generators. While none of the detectors can generalize to all generators, we observe a consistent and interesting pattern that the detectors trained on data from a medium-size LLM can zero-shot generalize to the larger version. As a concrete application, we demonstrate that robust detectors can be built on an ensemble of training data from medium-sized models.

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TalkUp: Paving the Way for Understanding Empowering Language
Lucille Njoo | Chan Park | Octavia Stappart | Marvin Thielk | Yi Chu | Yulia Tsvetkov
Findings of the Association for Computational Linguistics: EMNLP 2023

Empowering language is important in many real-world contexts, from education to workplace dynamics to healthcare. Though language technologies are growing more prevalent in these contexts, empowerment has seldom been studied in NLP, and moreover, it is inherently challenging to operationalize because of its implicit nature. This work builds from linguistic and social psychology literature to explore what characterizes empowering language. We then crowdsource a novel dataset of Reddit posts labeled for empowerment, reasons why these posts are empowering to readers, and the social relationships between posters and readers. Our preliminary analyses show that this dataset, which we call TalkUp, can be used to train language models that capture empowering and disempowering language. More broadly, TalkUp provides an avenue to explore implication, presuppositions, and how social context influences the meaning of language.

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Toward Human Readable Prompt Tuning: Kubrick’s The Shining is a good movie, and a good prompt too?
Weijia Shi | Xiaochuang Han | Hila Gonen | Ari Holtzman | Yulia Tsvetkov | Luke Zettlemoyer
Findings of the Association for Computational Linguistics: EMNLP 2023

Large language models can perform downstream tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. Such prompts are typically hand engineered, but can also be learned with gradient-based methods from labeled data. However, it is underexplored what factors make the prompts effective, especially when the prompts are in natural language. In this paper, we investigate common attributes shared by effective prompts in classification problems. We first propose a human readable prompt tuning method (FluentPrompt) based on Langevin dynamics that incorporates a fluency constraint to find a distribution of effective and fluent prompts. Our analysis reveals that effective prompts are topically related to the task domain and calibrate the prior probability of output labels. Based on these findings, we also propose a method for generating prompts using only unlabeled data, outperforming strong baselines by an average of 7.0% accuracy across three tasks.

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BotPercent: Estimating Bot Populations in Twitter Communities
Zhaoxuan Tan | Shangbin Feng | Melanie Sclar | Herun Wan | Minnan Luo | Yejin Choi | Yulia Tsvetkov
Findings of the Association for Computational Linguistics: EMNLP 2023

Twitter bot detection is vital in combating misinformation and safeguarding the integrity of social media discourse. While malicious bots are becoming more and more sophisticated and personalized, standard bot detection approaches are still agnostic to social environments (henceforth, communities) the bots operate at. In this work, we introduce community-specific bot detection, estimating the percentage of bots given the context of a community. Our method—BotPercent—is an amalgamation of Twitter bot detection datasets and feature-, text-, and graph-based models, adjusted to a particular community on Twitter. We introduce an approach that performs confidence calibration across bot detection models, which addresses generalization issues in existing community-agnostic models targeting individual bots and leads to more accurate community-level bot estimations. Experiments demonstrate that BotPercent achieves state-of-the-art performance in community-level Twitter bot detection across both balanced and imbalanced class distribution settings, presenting a less biased estimator of Twitter bot populations within the communities we analyze. We then analyze bot rates in several Twitter groups, including users who engage with partisan news media, political communities in different countries, and more. Our results reveal that the presence of Twitter bots is not homogeneous, but exhibiting a spatial-temporal distribution with considerable heterogeneity that should be taken into account for content moderation and social media policy making. The implementation of BotPercent is available at https://github.com/TamSiuhin/BotPercent.

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FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge
Shangbin Feng | Vidhisha Balachandran | Yuyang Bai | Yulia Tsvetkov
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Evaluating the factual consistency of automatically generated summaries is essential for the progress and adoption of reliable summarization systems. Despite recent advances, existing factuality evaluation models are not robust, being especially prone to entity and relation errors in new domains. We propose FactKB—a simple new approach to factuality evaluation that is generalizable across domains, in particular with respect to entities and relations. FactKB is based on language models pretrained using facts extracted from external knowledge bases. We introduce three types of complementary factuality pretraining objectives based on entity-specific facts, facts extracted from auxiliary knowledge about entities, and facts constructed compositionally through knowledge base walks. The resulting factuality evaluation model achieves state-of-the-art performance on two in-domain news summarization benchmarks as well as on three out-of-domain scientific literature datasets. Further analysis of FactKB shows improved ability to detect erroneous entities and relations in summaries and is robust and easily generalizable across domains.

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Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models
Orevaoghene Ahia | Sachin Kumar | Hila Gonen | Jungo Kasai | David Mortensen | Noah Smith | Yulia Tsvetkov
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Language models have graduated from being research prototypes to commercialized products offered as web APIs, and recent works have highlighted the multilingual capabilities of these products. The API vendors charge their users based on usage, more specifically on the number of “tokens” processed or generated by the underlying language models. What constitutes a token, however, is training data and model dependent with a large variance in the number of tokens required to convey the same information in different languages. In this work, we analyze the effect of this non-uniformity on the fairness of an API’s pricing policy across languages. We conduct a systematic analysis of the cost and utility of OpenAI’s language model API on multilingual benchmarks in 22 typologically diverse languages. We show evidence that speakers of a large number of the supported languages are overcharged while obtaining poorer results. These speakers tend to also come from regions where the APIs are less affordable, to begin with. Through these analyses, we aim to increase transparency around language model APIs’ pricing policies and encourage the vendors to make them more equitable.

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GlobalBench: A Benchmark for Global Progress in Natural Language Processing
Yueqi Song | Simran Khanuja | Pengfei Liu | Fahim Faisal | Alissa Ostapenko | Genta Winata | Alham Aji | Samuel Cahyawijaya | Yulia Tsvetkov | Antonios Anastasopoulos | Graham Neubig
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Despite the major advances in NLP, significant disparities in NLP system performance across languages still exist. Arguably, these are due to uneven resource allocation and sub-optimal incentives to work on less resourced languages. To track and further incentivize the global development of equitable language technology, we introduce GlobalBench. Prior multilingual benchmarks are static and have focused on a limited number of tasks and languages. In contrast, GlobalBench is an ever-expanding collection that aims to dynamically track progress on all NLP datasets in all languages. Rather than solely measuring accuracy, GlobalBench also tracks the estimated per-speaker utility and equity of technology across all languages, providing a multi-faceted view of how language technology is serving people of the world. Furthermore, GlobalBench is designed to identify the most under-served languages, and rewards research efforts directed towards those languages. At present, the most under-served languages are the ones with a relatively high population, but nonetheless overlooked by composite multilingual benchmarks (like Punjabi, Portuguese, and Wu Chinese). Currently, GlobalBench covers 966 datasets in 190 languages, and has 1,128 system submissions spanning 62 languages.

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Mitigating Societal Harms in Large Language Models
Sachin Kumar | Vidhisha Balachandran | Lucille Njoo | Antonios Anastasopoulos | Yulia Tsvetkov
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Numerous recent studies have highlighted societal harms that can be caused by language technologies deployed in the wild. While several surveys, tutorials, and workshops have discussed the risks of harms in specific contexts – e.g., detecting and mitigating gender bias in NLP models – no prior work has developed a unified typology of technical approaches for mitigating harms of language generation models. Our tutorial is based on a survey we recently wrote that proposes such a typology. We will provide an overview of potential social issues in language generation, including toxicity, social biases, misinformation, factual inconsistency, and privacy violations. Our primary focus will be on how to systematically identify risks, and how eliminate them at various stages of model development, from data collection, to model development, to inference/language generation. Through this tutorial, we aim to equip NLP researchers and engineers with a suite of practical tools for mitigating safety risks from pretrained language generation models.

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Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey
Sachin Kumar | Vidhisha Balachandran | Lucille Njoo | Antonios Anastasopoulos | Yulia Tsvetkov
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Recent advances in the capacity of large language models to generate human-like text have resulted in their increased adoption in user-facing settings. In parallel, these improvements have prompted a heated discourse around the risks of societal harms they introduce, whether inadvertent or malicious. Several studies have explored these harms and called for their mitigation via development of safer, fairer models. Going beyond enumerating the risks of harms, this work provides a survey of practical methods for addressing potential threats and societal harms from language generation models. We draw on several prior works’ taxonomies of language model risks to present a structured overview of strategies for detecting and ameliorating different kinds of risks/harms of language generators. Bridging diverse strands of research, this survey aims to serve as a practical guide for both LM researchers and practitioners, with explanations of different strategies’ motivations, their limitations, and open problems for future research.

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Understanding Ethics in NLP Authoring and Reviewing
Luciana Benotti | Karën Fort | Min-Yen Kan | Yulia Tsvetkov
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts

With NLP research now quickly being transferred into real-world applications, it is important to be aware of and think through the consequences of our scientific investigation. Such ethical considerations are important in both authoring and reviewing. This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues and review common considerations that recur in NLP research. The methodology is interactive and participatory, including case studies and working in groups. Importantly, the participants will be co-building the tutorial outcomes and will be working to create further tutorial materials to share as public outcomes.

2022

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Threat Scenarios and Best Practices to Detect Neural Fake News
Artidoro Pagnoni | Martin Graciarena | Yulia Tsvetkov
Proceedings of the 29th International Conference on Computational Linguistics

In this work, we discuss different threat scenarios from neural fake news generated by state-of-the-art language models. Through our experiments, we assess the performance of generated text detection systems under these threat scenarios. For each scenario, we also identify the minimax strategy for the detector that minimizes its worst-case performance. This constitutes a set of best practices that practitioners can rely on. In our analysis, we find that detectors are prone to shortcut learning (lack of out-of-distribution generalization) and discuss approaches to mitigate this problem and improve detectors more broadly. Finally, we argue that strong detectors should be released along with new generators.

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Speaker Information Can Guide Models to Better Inductive Biases: A Case Study On Predicting Code-Switching
Alissa Ostapenko | Shuly Wintner | Melinda Fricke | Yulia Tsvetkov
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural language processing (NLP) models trained on people-generated data can be unreliable because, without any constraints, they can learn from spurious correlations that are not relevant to the task. We hypothesize that enriching models with speaker information in a controlled, educated way can guide them to pick up on relevant inductive biases. For the speaker-driven task of predicting code-switching points in English–Spanish bilingual dialogues, we show that adding sociolinguistically-grounded speaker features as prepended prompts significantly improves accuracy. We find that by adding influential phrases to the input, speaker-informed models learn useful and explainable linguistic information. To our knowledge, we are the first to incorporate speaker characteristics in a neural model for code-switching, and more generally, take a step towards developing transparent, personalized models that use speaker information in a controlled way.

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Gendered Mental Health Stigma in Masked Language Models
Inna Lin | Lucille Njoo | Anjalie Field | Ashish Sharma | Katharina Reinecke | Tim Althoff | Yulia Tsvetkov
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In this work, we investigate gendered mental health stigma in masked language models. In doing so, we operationalize mental health stigma by developing a framework grounded in psychology research: we use clinical psychology literature to curate prompts, then evaluate the models’ propensity to generate gendered words. We find that masked language models capture societal stigma about gender in mental health: models are consistently more likely to predict female subjects than male in sentences about having a mental health condition (32% vs. 19%), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. Furthermore, we find that different models capture dimensions of stigma differently for men and women, associating stereotypes like anger, blame, and pity more with women with mental health conditions than with men. In showing the complex nuances of models’ gendered mental health stigma, we demonstrate that context and overlapping dimensions of identity are important considerations when assessing computational models’ social biases.

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Gradient-based Constrained Sampling from Language Models
Sachin Kumar | Biswajit Paria | Yulia Tsvetkov
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Large pretrained language models are successful at generating fluent text but are notoriously hard to controllably sample from. In this work, we study constrained sampling from such language models, i.e., generating text that satisfies user-defined constraints, while maintaining fluency and model’s performance in a downstream task. We propose MuCoLa—a sampling procedure that combines the log-likelihood of the language model with arbitrary (differentiable) constraints in a single energy function, and then generates samples in a non-autoregressive manner. Specifically, it initializes the entire output sequence with noise and follows a Markov chain defined by Langevin Dynamics using the gradients of this energy. We evaluate MuCoLa on text generation with soft and hard constraints as well as their combinations, obtaining significant improvements over competitive baselines for toxicity avoidance, sentiment control, and keyword-guided generation.

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Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation
Melanie Sclar | Peter West | Sachin Kumar | Yulia Tsvetkov | Yejin Choi
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

We present Referee, a novel framework for sentence summarization that can be trained reference-free (i.e., requiring no gold summaries for supervision), while allowing direct control for compression ratio. Our work is the first to demonstrate that reference-free, controlled sentence summarization is feasible via the conceptual framework of Symbolic Knowledge Distillation (West et al., 2022), where latent knowledge in pre-trained language models is distilled via explicit examples sampled from the teacher models, further purified with three types of filters: length, fidelity, and Information Bottleneck. Moreover, we uniquely propose iterative distillation of knowledge, where student models from the previous iteration of distillation serve as teacher models in the next iteration. Starting off from a relatively modest set of GPT3-generated summaries, we demonstrate how iterative knowledge distillation can lead to considerably smaller, but better summarizers with sharper controllability. A useful by-product of this iterative distillation process is a high-quality dataset of sentence-summary pairs with varying degrees of compression ratios. Empirical results demonstrate that the final student models vastly outperform the much larger GPT3-Instruct model in terms of the controllability of compression ratios, without compromising the quality of resulting summarization.

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Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling
Vidhisha Balachandran | Hannaneh Hajishirzi | William Cohen | Yulia Tsvetkov
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Abstractive summarization models often generate inconsistent summaries containing factual errors or hallucinated content. Recent works focus on correcting factual errors in generated summaries via post-editing. Such correction models are trained using adversarial non-factual summaries constructed using heuristic rules for injecting errors. However, generating non-factual summaries using heuristics often does not generalize well to actual model errors. In this work, we propose to generate hard, representative synthetic examples of non-factual summaries through infilling language models. With this data, we train a more robust fact-correction model to post-edit the summaries to improve factual consistency. Through quantitative and qualitative experiments on two popular summarization datasets— CNN/DM and XSum—we show that our approach vastly outperforms prior methods in correcting erroneous summaries. Our model—FactEdit—improves factuality scores by over ~11 points on CNN/DM and over ~31 points on XSum on average across multiple summarization models, producing more factual summaries while maintaining competitive summarization quality.

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Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media
Chan Young Park | Julia Mendelsohn | Anjalie Field | Yulia Tsvetkov
Findings of the Association for Computational Linguistics: EMNLP 2022

NLP research on public opinion manipulation campaigns has primarily focused on detecting overt strategies such as fake news and disinformation. However, information manipulation in the ongoing Russia-Ukraine war exemplifies how governments and media also employ more nuanced strategies. We release a new dataset, VoynaSlov, containing 38M+ posts from Russian media outlets on Twitter and VKontakte, as well as public activity and responses, immediately preceding and during the 2022 Russia-Ukraine war. We apply standard and recently-developed NLP models on VoynaSlov to examine agenda setting, framing, and priming, several strategies underlying information manipulation, and reveal variation across media outlet control, social media platform, and time. Our examination of these media effects and extensive discussion of current approaches’ limitations encourage further development of NLP models for understanding information manipulation in emerging crises, as well as other real-world and interdisciplinary tasks.

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Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts
Miguel Ballesteros | Yulia Tsvetkov | Cecilia O. Alm
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts

2021

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Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks
Jimin Sun | Hwijeen Ahn | Chan Young Park | Yulia Tsvetkov | David R. Mortensen
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Much work in cross-lingual transfer learning explored how to select better transfer languages for multilingual tasks, primarily focusing on typological and genealogical similarities between languages. We hypothesize that these measures of linguistic proximity are not enough when working with pragmatically-motivated tasks, such as sentiment analysis. As an alternative, we introduce three linguistic features that capture cross-cultural similarities that manifest in linguistic patterns and quantify distinct aspects of language pragmatics: language context-level, figurative language, and the lexification of emotion concepts. Our analyses show that the proposed pragmatic features do capture cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology. We further corroborate the effectiveness of pragmatically-driven transfer in the downstream task of choosing transfer languages for cross-lingual sentiment analysis.

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StructSum: Summarization via Structured Representations
Vidhisha Balachandran | Artidoro Pagnoni | Jay Yoon Lee | Dheeraj Rajagopal | Jaime Carbonell | Yulia Tsvetkov
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Abstractive text summarization aims at compressing the information of a long source document into a rephrased, condensed summary. Despite advances in modeling techniques, abstractive summarization models still suffer from several key challenges: (i) layout bias: they overfit to the style of training corpora; (ii) limited abstractiveness: they are optimized to copying n-grams from the source rather than generating novel abstractive summaries; (iii) lack of transparency: they are not interpretable. In this work, we propose a framework based on document-level structure induction for summarization to address these challenges. To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models. Our framework complements standard encoder-decoder summarization models by augmenting them with rich structure-aware document representations based on implicitly learned (latent) structures and externally-derived linguistic (explicit) structures. We show that our summarization framework, trained on the CNN/DM dataset, improves the coverage of content in the source documents, generates more abstractive summaries by generating more novel n-grams, and incorporates interpretable sentence-level structures, while performing on par with standard baselines.

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A Survey of Race, Racism, and Anti-Racism in NLP
Anjalie Field | Su Lin Blodgett | Zeerak Waseem | Yulia Tsvetkov
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Despite inextricable ties between race and language, little work has considered race in NLP research and development. In this work, we survey 79 papers from the ACL anthology that mention race. These papers reveal various types of race-related bias in all stages of NLP model development, highlighting the need for proactive consideration of how NLP systems can uphold racial hierarchies. However, persistent gaps in research on race and NLP remain: race has been siloed as a niche topic and remains ignored in many NLP tasks; most work operationalizes race as a fixed single-dimensional variable with a ground-truth label, which risks reinforcing differences produced by historical racism; and the voices of historically marginalized people are nearly absent in NLP literature. By identifying where and how NLP literature has and has not considered race, especially in comparison to related fields, our work calls for inclusion and racial justice in NLP research practices.

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Machine Translation into Low-resource Language Varieties
Sachin Kumar | Antonios Anastasopoulos | Shuly Wintner | Yulia Tsvetkov
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

State-of-the-art machine translation (MT) systems are typically trained to generate “standard” target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language. Such varieties are often low-resource, and hence do not benefit from contemporary NLP solutions, MT included. We propose a general framework to rapidly adapt MT systems to generate language varieties that are close to, but different from, the standard target language, using no parallel (source–variety) data. This also includes adaptation of MT systems to low-resource typologically-related target languages. We experiment with adapting an English–Russian MT system to generate Ukrainian and Belarusian, an English–Norwegian Bokmål system to generate Nynorsk, and an English–Arabic system to generate four Arabic dialects, obtaining significant improvements over competitive baselines.

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Controlling Dialogue Generation with Semantic Exemplars
Prakhar Gupta | Jeffrey Bigham | Yulia Tsvetkov | Amy Pavel
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Dialogue systems pretrained with large language models generate locally coherent responses, but lack fine-grained control over responses necessary to achieve specific goals. A promising method to control response generation is exemplar-based generation, in which models edit exemplar responses that are retrieved from training data, or hand-written to strategically address discourse-level goals, to fit new dialogue contexts. We present an Exemplar-based Dialogue Generation model, EDGE, that uses the semantic frames present in exemplar responses to guide response generation. We show that controlling dialogue generation based on the semantic frames of exemplars improves the coherence of generated responses, while preserving semantic meaning and conversation goals present in exemplar responses.

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Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
Artidoro Pagnoni | Vidhisha Balachandran | Yulia Tsvetkov
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically generated summaries. Due to the lack of common benchmarks, these metrics cannot be compared. Moreover, all these methods treat factuality as a binary concept and fail to provide deeper insights on the kinds of inconsistencies made by different systems. To address these limitations, we devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems for the CNN/DM and XSum datasets. Through these annotations we identify the proportion of different categories of factual errors and benchmark factuality metrics, showing their correlation with human judgement as well as their specific strengths and weaknesses.

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Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs
Monisha Jegadeesan | Sachin Kumar | John Wieting | Yulia Tsvetkov
Proceedings of the 1st Workshop on Multilingual Representation Learning

We present a novel technique for zero-shot paraphrase generation. The key contribution is an end-to-end multilingual paraphrasing model that is trained using translated parallel corpora to generate paraphrases into “meaning spaces” – replacing the final softmax layer with word embeddings. This architectural modification, plus a training procedure that incorporates an autoencoding objective, enables effective parameter sharing across languages for more fluent monolingual rewriting, and facilitates fluency and diversity in the generated outputs. Our continuous-output paraphrase generation models outperform zero-shot paraphrasing baselines when evaluated on two languages using a battery of computational metrics as well as in human assessment.

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SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers
Dheeraj Rajagopal | Vidhisha Balachandran | Eduard H Hovy | Yulia Tsvetkov
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We introduce SelfExplain, a novel self-explaining model that explains a text classifier’s predictions using phrase-based concepts. SelfExplain augments existing neural classifiers by adding (1) a globally interpretable layer that identifies the most influential concepts in the training set for a given sample and (2) a locally interpretable layer that quantifies the contribution of each local input concept by computing a relevance score relative to the predicted label. Experiments across five text-classification datasets show that SelfExplain facilitates interpretability without sacrificing performance. Most importantly, explanations from SelfExplain show sufficiency for model predictions and are perceived as adequate, trustworthy and understandable by human judges compared to existing widely-used baselines.

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Evaluating the Morphosyntactic Well-formedness of Generated Texts
Adithya Pratapa | Antonios Anastasopoulos | Shruti Rijhwani | Aditi Chaudhary | David R. Mortensen | Graham Neubig | Yulia Tsvetkov
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L’AMBRE – a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.

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Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation
Prakhar Gupta | Yulia Tsvetkov | Jeffrey Bigham
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Efficient Test Time Adapter Ensembling for Low-resource Language Varieties
Xinyi Wang | Yulia Tsvetkov | Sebastian Ruder | Graham Neubig
Findings of the Association for Computational Linguistics: EMNLP 2021

Adapters are light-weight modules that allow parameter-efficient fine-tuning of pretrained models. Specialized language and task adapters have recently been proposed to facilitate cross-lingual transfer of multilingual pretrained models (Pfeiffer et al., 2020b). However, this approach requires training a separate language adapter for every language one wishes to support, which can be impractical for languages with limited data. An intuitive solution is to use a related language adapter for the new language variety, but we observe that this solution can lead to sub-optimal performance. In this paper, we aim to improve the robustness of language adapters to uncovered languages without training new adapters. We find that ensembling multiple existing language adapters makes the fine-tuned model significantly more robust to other language varieties not included in these adapters. Building upon this observation, we propose Entropy Minimized Ensemble of Adapters (EMEA), a method that optimizes the ensemble weights of the pretrained language adapters for each test sentence by minimizing the entropy of its predictions. Experiments on three diverse groups of language varieties show that our method leads to significant improvements on both named entity recognition and part-of-speech tagging across all languages.

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Detecting Community Sensitive Norm Violations in Online Conversations
Chan Young Park | Julia Mendelsohn | Karthik Radhakrishnan | Kinjal Jain | Tushar Kanakagiri | David Jurgens | Yulia Tsvetkov
Findings of the Association for Computational Linguistics: EMNLP 2021

Online platforms and communities establish their own norms that govern what behavior is acceptable within the community. Substantial effort in NLP has focused on identifying unacceptable behaviors and, recently, on forecasting them before they occur. However, these efforts have largely focused on toxicity as the sole form of community norm violation. Such focus has overlooked the much larger set of rules that moderators enforce. Here, we introduce a new dataset focusing on a more complete spectrum of community norms and their violations in the local conversational and global community contexts. We introduce a series of models that use this data to develop context- and community-sensitive norm violation detection, showing that these changes give high performance.

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Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates
Xiaochuang Han | Yulia Tsvetkov
Findings of the Association for Computational Linguistics: EMNLP 2021

Among the most critical limitations of deep learning NLP models are their lack of interpretability, and their reliance on spurious correlations. Prior work proposed various approaches to interpreting the black-box models to unveil the spurious correlations, but the research was primarily used in human-computer interaction scenarios. It still remains underexplored whether or how such model interpretations can be used to automatically “unlearn” confounding features. In this work, we propose influence tuning—a procedure that leverages model interpretations to update the model parameters towards a plausible interpretation (rather than an interpretation that relies on spurious patterns in the data) in addition to learning to predict the task labels. We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data, significantly outperforming baseline methods that use adversarial training.

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Improving Span Representation for Domain-adapted Coreference Resolution
Nupoor Gandhi | Anjalie Field | Yulia Tsvetkov
Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference

Recent work has shown fine-tuning neural coreference models can produce strong performance when adapting to different domains. However, at the same time, this can require a large amount of annotated target examples. In this work, we focus on supervised domain adaptation for clinical notes, proposing the use of concept knowledge to more efficiently adapt coreference models to a new domain. We develop methods to improve the span representations via (1) a retrofitting loss to incentivize span representations to satisfy a knowledge-based distance function and (2) a scaffolding loss to guide the recovery of knowledge from the span representation. By integrating these losses, our model is able to improve our baseline precision and F-1 score. In particular, we show that incorporating knowledge with end-to-end coreference models results in better performance on the most challenging, domain-specific spans.

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Simple and Efficient ways to Improve REALM
Vidhisha Balachandran | Ashish Vaswani | Yulia Tsvetkov | Niki Parmar
Proceedings of the 3rd Workshop on Machine Reading for Question Answering

Dense retrieval has been shown to be effective for Open Domain Question Answering, surpassing sparse retrieval methods like BM25. One such model, REALM, (Guu et al., 2020) is an end-to-end dense retrieval system that uses MLM based pretraining for improved downstream QA performance. However, the current REALM setup uses limited resources and is not comparable in scale to more recent systems, contributing to its lower performance. Additionally, it relies on noisy supervision for retrieval during fine-tuning. We propose REALM++, where we improve upon the training and inference setups and introduce better supervision signal for improving performance, without any architectural changes. REALM++ achieves ~5.5% absolute accuracy gains over the baseline while being faster to train. It also matches the performance of large models which have 3x more parameters demonstrating the efficiency of our setup.

2020

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Demoting Racial Bias in Hate Speech Detection
Mengzhou Xia | Anjalie Field | Yulia Tsvetkov
Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media

In the task of hate speech detection, there exists a high correlation between African American English (AAE) and annotators’ perceptions of toxicity in current datasets. This bias in annotated training data and the tendency of machine learning models to amplify it cause AAE text to often be mislabeled as abusive/offensive/hate speech (high false positive rate) by current hate speech classifiers. Here, we use adversarial training to mitigate this bias. Experimental results on one hate speech dataset and one AAE dataset suggest that our method is able to reduce the false positive rate for AAE text with only a minimal compromise on the performance of hate speech classification.

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LTIatCMU at SemEval-2020 Task 11: Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification
Sopan Khosla | Rishabh Joshi | Ritam Dutt | Alan W Black | Yulia Tsvetkov
Proceedings of the Fourteenth Workshop on Semantic Evaluation

In this paper we describe our submission for the task of Propaganda Span Identification in news articles. We introduce a BERT-BiLSTM based span-level propaganda classification model that identifies which token spans within the sentence are indicative of propaganda. The ”multi-granular” model incorporates linguistic knowledge at various levels of text granularity, including word, sentence and document level syntactic, semantic and pragmatic affect features, which significantly improve model performance, compared to its language-agnostic variant. To facilitate better representation learning, we also collect a corpus of 10k news articles, and use it for fine-tuning the model. The final model is a majority-voting ensemble which learns different propaganda class boundaries by leveraging different subsets of incorporated knowledge.

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Unsupervised Discovery of Implicit Gender Bias
Anjalie Field | Yulia Tsvetkov
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Despite their prevalence in society, social biases are difficult to identify, primarily because human judgements in this domain can be unreliable. We take an unsupervised approach to identifying gender bias against women at a comment level and present a model that can surface text likely to contain bias. Our main challenge is forcing the model to focus on signs of implicit bias, rather than other artifacts in the data. Thus, our methodology involves reducing the influence of confounds through propensity matching and adversarial learning. Our analysis shows how biased comments directed towards female politicians contain mixed criticisms, while comments directed towards other female public figures focus on appearance and sexualization. Ultimately, our work offers a way to capture subtle biases in various domains without relying on subjective human judgements.

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On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment
Zirui Wang | Zachary C. Lipton | Yulia Tsvetkov
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Modern multilingual models are trained on concatenated text from multiple languages in hopes of conferring benefits to each (positive transfer), with the most pronounced benefits accruing to low-resource languages. However, recent work has shown that this approach can degrade performance on high-resource languages, a phenomenon known as negative interference. In this paper, we present the first systematic study of negative interference. We show that, contrary to previous belief, negative interference also impacts low-resource languages. While parameters are maximally shared to learn language-universal structures, we demonstrate that language-specific parameters do exist in multilingual models and they are a potential cause of negative interference. Motivated by these observations, we also present a meta-learning algorithm that obtains better cross-lingual transferability and alleviates negative interference, by adding language-specific layers as meta-parameters and training them in a manner that explicitly improves shared layers’ generalization on all languages. Overall, our results show that negative interference is more common than previously known, suggesting new directions for improving multilingual representations.

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Automatic Extraction of Rules Governing Morphological Agreement
Aditi Chaudhary | Antonios Anastasopoulos | Adithya Pratapa | David R. Mortensen | Zaid Sheikh | Yulia Tsvetkov | Graham Neubig
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Creating a descriptive grammar of a language is an indispensable step for language documentation and preservation. However, at the same time it is a tedious, time-consuming task. In this paper, we take steps towards automating this process by devising an automated framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. We focus on extracting rules describing agreement, a morphosyntactic phenomenon at the core of the grammars of many of the world’s languages. We apply our framework to all languages included in the Universal Dependencies project, with promising results. Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data. We confirm this finding with human expert evaluations of the rules that our framework produces, which have an average accuracy of 78%. We release an interface demonstrating the extracted rules at https://neulab.github.io/lase/

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Fortifying Toxic Speech Detectors Against Veiled Toxicity
Xiaochuang Han | Yulia Tsvetkov
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Modern toxic speech detectors are incompetent in recognizing disguised offensive language, such as adversarial attacks that deliberately avoid known toxic lexicons, or manifestations of implicit bias. Building a large annotated dataset for such veiled toxicity can be very expensive. In this work, we propose a framework aimed at fortifying existing toxic speech detectors without a large labeled corpus of veiled toxicity. Just a handful of probing examples are used to surface orders of magnitude more disguised offenses. We augment the toxic speech detector’s training data with these discovered offensive examples, thereby making it more robust to veiled toxicity while preserving its utility in detecting overt toxicity.

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Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions
Xiaochuang Han | Byron C. Wallace | Yulia Tsvetkov
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Modern deep learning models for NLP are notoriously opaque. This has motivated the development of methods for interpreting such models, e.g., via gradient-based saliency maps or the visualization of attention weights. Such approaches aim to provide explanations for a particular model prediction by highlighting important words in the corresponding input text. While this might be useful for tasks where decisions are explicitly influenced by individual tokens in the input, we suspect that such highlighting is not suitable for tasks where model decisions should be driven by more complex reasoning. In this work, we investigate the use of influence functions for NLP, providing an alternative approach to interpreting neural text classifiers. Influence functions explain the decisions of a model by identifying influential training examples. Despite the promise of this approach, influence functions have not yet been extensively evaluated in the context of NLP, a gap addressed by this work. We conduct a comparison between influence functions and common word-saliency methods on representative tasks. As suspected, we find that influence functions are particularly useful for natural language inference, a task in which ‘saliency maps’ may not have clear interpretation. Furthermore, we develop a new quantitative measure based on influence functions that can reveal artifacts in training data.

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Balancing Training for Multilingual Neural Machine Translation
Xinyi Wang | Yulia Tsvetkov | Graham Neubig
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

When training multilingual machine translation (MT) models that can translate to/from multiple languages, we are faced with imbalanced training sets: some languages have much more training data than others. Standard practice is to up-sample less resourced languages to increase representation, and the degree of up-sampling has a large effect on the overall performance. In this paper, we propose a method that instead automatically learns how to weight training data through a data scorer that is optimized to maximize performance on all test languages. Experiments on two sets of languages under both one-to-many and many-to-one MT settings show our method not only consistently outperforms heuristic baselines in terms of average performance, but also offers flexible control over the performance of which languages are optimized.

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A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity Rewards
Zi-Yi Dou | Sachin Kumar | Yulia Tsvetkov
Proceedings of the Fourth Workshop on Neural Generation and Translation

Cross-lingual text summarization aims at generating a document summary in one language given input in another language. It is a practically important but under-explored task, primarily due to the dearth of available data. Existing methods resort to machine translation to synthesize training data, but such pipeline approaches suffer from error propagation. In this work, we propose an end-to-end cross-lingual text summarization model. The model uses reinforcement learning to directly optimize a bilingual semantic similarity metric between the summaries generated in a target language and gold summaries in a source language. We also introduce techniques to pre-train the model leveraging monolingual summarization and machine translation objectives. Experimental results in both English–Chinese and English–German cross-lingual summarization settings demonstrate the effectiveness of our methods. In addition, we find that reinforcement learning models with bilingual semantic similarity as rewards generate more fluent sentences than strong baselines.

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Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues
Tanmay Parekh | Emily Ahn | Yulia Tsvetkov | Alan W Black
Proceedings of the 24th Conference on Computational Natural Language Learning

Code-switching is a ubiquitous phenomenon in multilingual communities. Natural language technologies that wish to communicate like humans must therefore adaptively incorporate code-switching techniques when they are deployed in multilingual settings. To this end, we propose a Hindi-English human-machine dialogue system that elicits code-switching conversations in a controlled setting. It uses different code-switching agent strategies to understand how users respond and accommodate to the agent’s language choice. Through this system, we collect and release a new dataset CommonDost, comprising of 439 human-machine multilingual conversations. We adapt pre-defined metrics to discover linguistic accommodation from users to agents. Finally, we compare these dialogues with Spanish-English dialogues collected in a similar setting, and analyze the impact of linguistic and socio-cultural factors on code-switching patterns across the two language pairs.

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What Code-Switching Strategies are Effective in Dialog Systems?
Emily Ahn | Cecilia Jimenez | Yulia Tsvetkov | Alan W Black
Proceedings of the Society for Computation in Linguistics 2020

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Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods
Maria Ryskina | Ella Rabinovich | Taylor Berg-Kirkpatrick | David Mortensen | Yulia Tsvetkov
Proceedings of the Society for Computation in Linguistics 2020

2019

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Entity-Centric Contextual Affective Analysis
Anjalie Field | Yulia Tsvetkov
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored. We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples. We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses. We ultimately use our method to examine differences in portrayals of men and women.

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Measuring Bias in Contextualized Word Representations
Keita Kurita | Nidhi Vyas | Ayush Pareek | Alan W Black | Yulia Tsvetkov
Proceedings of the First Workshop on Gender Bias in Natural Language Processing

Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks. Since they are optimized to capture the statistical properties of training data, they tend to pick up on and amplify social stereotypes present in the data as well. In this study, we (1) propose a template-based method to quantify bias in BERT; (2) show that this method obtains more consistent results in capturing social biases than the traditional cosine based method; and (3) conduct a case study, evaluating gender bias in a downstream task of Gender Pronoun Resolution. Although our case study focuses on gender bias, the proposed technique is generalizable to unveiling other biases, including in multiclass settings, such as racial and religious biases.

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CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology
Aditi Chaudhary | Elizabeth Salesky | Gayatri Bhat | David R. Mortensen | Jaime Carbonell | Yulia Tsvetkov
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper presents the submission by the CMU-01 team to the SIGMORPHON 2019 task 2 of Morphological Analysis and Lemmatization in Context. This task requires us to produce the lemma and morpho-syntactic description of each token in a sequence, for 107 treebanks. We approach this task with a hierarchical neural conditional random field (CRF) model which predicts each coarse-grained feature (eg. POS, Case, etc.) independently. However, most treebanks are under-resourced, thus making it challenging to train deep neural models for them. Hence, we propose a multi-lingual transfer training regime where we transfer from multiple related languages that share similar typology.

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A Dynamic Strategy Coach for Effective Negotiation
Yiheng Zhou | He He | Alan W Black | Yulia Tsvetkov
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Negotiation is a complex activity involving strategic reasoning, persuasion, and psychology. An average person is often far from an expert in negotiation. Our goal is to assist humans to become better negotiators through a machine-in-the-loop approach that combines machine’s advantage at data-driven decision-making and human’s language generation ability. We consider a bargaining scenario where a seller and a buyer negotiate the price of an item for sale through a text-based dialogue. Our negotiation coach monitors messages between them and recommends strategies in real time to the seller to get a better deal (e.g., “reject the proposal and propose a price”, “talk about your personal experience with the product”). The best strategy largely depends on the context (e.g., the current price, the buyer’s attitude). Therefore, we first identify a set of negotiation strategies, then learn to predict the best strategy in a given dialogue context from a set of human-human bargaining dialogues. Evaluation on human-human dialogues shows that our coach increases the profits of the seller by almost 60%.

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Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts
Luke Breitfeller | Emily Ahn | David Jurgens | Yulia Tsvetkov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Microaggressions are subtle, often veiled, manifestations of human biases. These uncivil interactions can have a powerful negative impact on people by marginalizing minorities and disadvantaged groups. The linguistic subtlety of microaggressions in communication has made it difficult for researchers to analyze their exact nature, and to quantify and extract microaggressions automatically. Specifically, the lack of a corpus of real-world microaggressions and objective criteria for annotating them have prevented researchers from addressing these problems at scale. In this paper, we devise a general but nuanced, computationally operationalizable typology of microaggressions based on a small subset of data that we have. We then create two datasets: one with examples of diverse types of microaggressions recollected by their targets, and another with gender-based microaggressions in public conversations on social media. We introduce a new, more objective, criterion for annotation and an active-learning based procedure that increases the likelihood of surfacing posts containing microaggressions. Finally, we analyze the trends that emerge from these new datasets.

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Topics to Avoid: Demoting Latent Confounds in Text Classification
Sachin Kumar | Shuly Wintner | Noah A. Smith | Yulia Tsvetkov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. In this work, we observe this limitation with respect to the task of native language identification. We find that standard text classifiers which perform well on the test set end up learning topical features which are confounds of the prediction task (e.g., if the input text mentions Sweden, the classifier predicts that the author’s native language is Swedish). We propose a method that represents the latent topical confounds and a model which “unlearns” confounding features by predicting both the label of the input text and the confound; but we train the two predictors adversarially in an alternating fashion to learn a text representation that predicts the correct label but is less prone to using information about the confound. We show that this model generalizes better and learns features that are indicative of the writing style rather than the content.

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A Margin-based Loss with Synthetic Negative Samples for Continuous-output Machine Translation
Gayatri Bhat | Sachin Kumar | Yulia Tsvetkov
Proceedings of the 3rd Workshop on Neural Generation and Translation

Neural models that eliminate the softmax bottleneck by generating word embeddings (rather than multinomial distributions over a vocabulary) attain faster training with fewer learnable parameters. These models are currently trained by maximizing densities of pretrained target embeddings under von Mises-Fisher distributions parameterized by corresponding model-predicted embeddings. This work explores the utility of margin-based loss functions in optimizing such models. We present syn-margin loss, a novel margin-based loss that uses a synthetic negative sample constructed from only the predicted and target embeddings at every step. The loss is efficient to compute, and we use a geometric analysis to argue that it is more consistent and interpretable than other margin-based losses. Empirically, we find that syn-margin provides small but significant improvements over both vMF and standard margin-based losses in continuous-output neural machine translation.

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Learning to Generate Word- and Phrase-Embeddings for Efficient Phrase-Based Neural Machine Translation
Chan Young Park | Yulia Tsvetkov
Proceedings of the 3rd Workshop on Neural Generation and Translation

Neural machine translation (NMT) often fails in one-to-many translation, e.g., in the translation of multi-word expressions, compounds, and collocations. To improve the translation of phrases, phrase-based NMT systems have been proposed; these typically combine word-based NMT with external phrase dictionaries or with phrase tables from phrase-based statistical MT systems. These solutions introduce a significant overhead of additional resources and computational costs. In this paper, we introduce a phrase-based NMT model built upon continuous-output NMT, in which the decoder generates embeddings of words or phrases. The model uses a fertility module, which guides the decoder to generate embeddings of sequences of varying lengths. We show that our model learns to translate phrases better, performing on par with state of the art phrase-based NMT. Since our model does not resort to softmax computation over a huge vocabulary of phrases, its training time is about 112x faster than the baseline.

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Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings
Thomas Manzini | Lim Yao Chong | Alan W Black | Yulia Tsvetkov
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Online texts - across genres, registers, domains, and styles - are riddled with human stereotypes, expressed in overt or subtle ways. Word embeddings, trained on these texts, perpetuate and amplify these stereotypes, and propagate biases to machine learning models that use word embeddings as features. In this work, we propose a method to debias word embeddings in multiclass settings such as race and religion, extending the work of (Bolukbasi et al., 2016) from the binary setting, such as binary gender. Next, we propose a novel methodology for the evaluation of multiclass debiasing. We demonstrate that our multiclass debiasing is robust and maintains the efficacy in standard NLP tasks.

2018

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Style Transfer Through Back-Translation
Shrimai Prabhumoye | Yulia Tsvetkov | Ruslan Salakhutdinov | Alan W Black
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Style transfer is the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context. This paper introduces a new method for automatic style transfer. We first learn a latent representation of the input sentence which is grounded in a language translation model in order to better preserve the meaning of the sentence while reducing stylistic properties. Then adversarial generation techniques are used to make the output match the desired style. We evaluate this technique on three different style transformations: sentiment, gender and political slant. Compared to two state-of-the-art style transfer modeling techniques we show improvements both in automatic evaluation of style transfer and in manual evaluation of meaning preservation and fluency.

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Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies
Anjalie Field | Doron Kliger | Shuly Wintner | Jennifer Pan | Dan Jurafsky | Yulia Tsvetkov
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Amidst growing concern over media manipulation, NLP attention has focused on overt strategies like censorship and “fake news”. Here, we draw on two concepts from political science literature to explore subtler strategies for government media manipulation: agenda-setting (selecting what topics to cover) and framing (deciding how topics are covered). We analyze 13 years (100K articles) of the Russian newspaper Izvestia and identify a strategy of distraction: articles mention the U.S. more frequently in the month directly following an economic downturn in Russia. We introduce embedding-based methods for cross-lingually projecting English frames to Russian, and discover that these articles emphasize U.S. moral failings and threats to the U.S. Our work offers new ways to identify subtle media manipulation strategies at the intersection of agenda-setting and framing.

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Proceedings of the Second Workshop on Subword/Character LEvel Models
Manaal Faruqui | Hinrich Schütze | Isabel Trancoso | Yulia Tsvetkov | Yadollah Yaghoobzadeh
Proceedings of the Second Workshop on Subword/Character LEvel Models

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RtGender: A Corpus for Studying Differential Responses to Gender
Rob Voigt | David Jurgens | Vinodkumar Prabhakaran | Dan Jurafsky | Yulia Tsvetkov
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Socially Responsible NLP
Yulia Tsvetkov | Vinodkumar Prabhakaran | Rob Voigt
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

As language technologies have become increasingly prevalent, there is a growing awareness that decisions we make about our data, methods, and tools are often tied up with their impact on people and societies. This tutorial will provide an overview of real-world applications of language technologies and the potential ethical implications associated with them. We will discuss philosophical foundations of ethical research along with state of the art techniques. Through this tutorial, we intend to provide the NLP researcher with an overview of tools to ensure that the data, algorithms, and models that they build are socially responsible. These tools will include a checklist of common pitfalls that one should avoid (e.g., demographic bias in data collection), as well as methods to adequately mitigate these issues (e.g., adjusting sampling rates or de-biasing through regularization). The tutorial is based on a new course on Ethics and NLP developed at Carnegie Mellon University.

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Native Language Cognate Effects on Second Language Lexical Choice
Ella Rabinovich | Yulia Tsvetkov | Shuly Wintner
Transactions of the Association for Computational Linguistics, Volume 6

We present a computational analysis of cognate effects on the spontaneous linguistic productions of advanced non-native speakers. Introducing a large corpus of highly competent non-native English speakers, and using a set of carefully selected lexical items, we show that the lexical choices of non-natives are affected by cognates in their native language. This effect is so powerful that we are able to reconstruct the phylogenetic language tree of the Indo-European language family solely from the frequencies of specific lexical items in the English of authors with various native languages. We quantitatively analyze non-native lexical choice, highlighting cognate facilitation as one of the important phenomena shaping the language of non-native speakers.

2017

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Incorporating Dialectal Variability for Socially Equitable Language Identification
David Jurgens | Yulia Tsvetkov | Dan Jurafsky
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Language identification (LID) is a critical first step for processing multilingual text. Yet most LID systems are not designed to handle the linguistic diversity of global platforms like Twitter, where local dialects and rampant code-switching lead language classifiers to systematically miss minority dialect speakers and multilingual speakers. We propose a new dataset and a character-based sequence-to-sequence model for LID designed to support dialectal and multilingual language varieties. Our model achieves state-of-the-art performance on multiple LID benchmarks. Furthermore, in a case study using Twitter for health tracking, our method substantially increases the availability of texts written by underrepresented populations, enabling the development of “socially inclusive” NLP tools.

2016

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Morphological Inflection Generation Using Character Sequence to Sequence Learning
Manaal Faruqui | Yulia Tsvetkov | Graham Neubig | Chris Dyer
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning
Yulia Tsvetkov | Sunayana Sitaram | Manaal Faruqui | Guillaume Lample | Patrick Littell | David Mortensen | Alan W Black | Lori Levin | Chris Dyer
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Proceedings of the Workshop on Multilingual and Cross-lingual Methods in NLP
Dipanjan Das | Chris Dyer | Manaal Faruqui | Yulia Tsvetkov
Proceedings of the Workshop on Multilingual and Cross-lingual Methods in NLP

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Problems With Evaluation of Word Embeddings Using Word Similarity Tasks
Manaal Faruqui | Yulia Tsvetkov | Pushpendre Rastogi | Chris Dyer
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

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Correlation-based Intrinsic Evaluation of Word Vector Representations
Yulia Tsvetkov | Manaal Faruqui | Chris Dyer
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

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Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning
Yulia Tsvetkov | Manaal Faruqui | Wang Ling | Brian MacWhinney | Chris Dyer
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Not All Contexts Are Created Equal: Better Word Representations with Variable Attention
Wang Ling | Yulia Tsvetkov | Silvio Amir | Ramón Fermandez | Chris Dyer | Alan W Black | Isabel Trancoso | Chu-Cheng Lin
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Evaluation of Word Vector Representations by Subspace Alignment
Yulia Tsvetkov | Manaal Faruqui | Wang Ling | Guillaume Lample | Chris Dyer
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Sparse Overcomplete Word Vector Representations
Manaal Faruqui | Yulia Tsvetkov | Dani Yogatama | Chris Dyer | Noah A. Smith
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Lexicon Stratification for Translating Out-of-Vocabulary Words
Yulia Tsvetkov | Chris Dyer
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Constraint-Based Models of Lexical Borrowing
Yulia Tsvetkov | Waleed Ammar | Chris Dyer
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Augmenting Translation Models with Simulated Acoustic Confusions for Improved Spoken Language Translation
Yulia Tsvetkov | Florian Metze | Chris Dyer
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Identification of Multiword Expressions by Combining Multiple Linguistic Information Sources
Yulia Tsvetkov | Shuly Wintner
Computational Linguistics, Volume 40, Issue 2 - June 2014

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The CMU Machine Translation Systems at WMT 2014
Austin Matthews | Waleed Ammar | Archna Bhatia | Weston Feely | Greg Hanneman | Eva Schlinger | Swabha Swayamdipta | Yulia Tsvetkov | Alon Lavie | Chris Dyer
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Metaphor Detection with Cross-Lingual Model Transfer
Yulia Tsvetkov | Leonid Boytsov | Anatole Gershman | Eric Nyberg | Chris Dyer
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Augmenting English Adjective Senses with Supersenses
Yulia Tsvetkov | Nathan Schneider | Dirk Hovy | Archna Bhatia | Manaal Faruqui | Chris Dyer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification. Results show that accuracy for automatic adjective type classification is high, but synsets are considerably more difficult to classify, even for trained human annotators. We release the manually annotated data, the classifier, and the induced supersense labeling of 12,304 WordNet adjective synsets.

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A Unified Annotation Scheme for the Semantic/Pragmatic Components of Definiteness
Archna Bhatia | Mandy Simons | Lori Levin | Yulia Tsvetkov | Chris Dyer | Jordan Bender
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present a definiteness annotation scheme that captures the semantic, pragmatic, and discourse information, which we call communicative functions, associated with linguistic descriptions such as “a story about my speech”, “the story”, “every time I give it”, “this slideshow”. A survey of the literature suggests that definiteness does not express a single communicative function but is a grammaticalization of many such functions, for example, identifiability, familiarity, uniqueness, specificity. Our annotation scheme unifies ideas from previous research on definiteness while attempting to remove redundancy and make it easily annotatable. This annotation scheme encodes the communicative functions of definiteness rather than the grammatical forms of definiteness. We assume that the communicative functions are largely maintained across languages while the grammaticalization of this information may vary. One of the final goals is to use our semantically annotated corpora to discover how definiteness is grammaticalized in different languages. We release our annotated corpora for English and Hindi, and sample annotations for Hebrew and Russian, together with an annotation manual.

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Automatic Classification of Communicative Functions of Definiteness
Archna Bhatia | Chu-Cheng Lin | Nathan Schneider | Yulia Tsvetkov | Fatima Talib Al-Raisi | Laleh Roostapour | Jordan Bender | Abhimanu Kumar | Lori Levin | Mandy Simons | Chris Dyer
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Cross-Lingual Metaphor Detection Using Common Semantic Features
Yulia Tsvetkov | Elena Mukomel | Anatole Gershman
Proceedings of the First Workshop on Metaphor in NLP

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Identifying the L1 of non-native writers: the CMU-Haifa system
Yulia Tsvetkov | Naama Twitto | Nathan Schneider | Noam Ordan | Manaal Faruqui | Victor Chahuneau | Shuly Wintner | Chris Dyer
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

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Generating English Determiners in Phrase-Based Translation with Synthetic Translation Options
Yulia Tsvetkov | Chris Dyer | Lori Levin | Archna Bhatia
Proceedings of the Eighth Workshop on Statistical Machine Translation

2011

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Identification of Multi-word Expressions by Combining Multiple Linguistic Information Sources
Yulia Tsvetkov | Shuly Wintner
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Automatic Acquisition of Parallel Corpora from Websites with Dynamic Content
Yulia Tsvetkov | Shuly Wintner
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Parallel corpora are indispensable resources for a variety of multilingual natural language processing tasks. This paper presents a technique for fully automatic construction of constantly growing parallel corpora. We propose a simple and effective dictionary-based algorithm to extract parallel document pairs from a large collection of articles retrieved from the Internet, potentially containing manually translated texts. This algorithm was implemented and tested on Hebrew-English parallel texts. With properly selected thresholds, precision of 100% can be obtained.

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Extraction of Multi-word Expressions from Small Parallel Corpora
Yulia Tsvetkov | Shuly Wintner
Coling 2010: Posters

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