Yulia Tsvetkov


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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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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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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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

2020

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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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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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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

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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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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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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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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.

2019

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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.

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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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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.

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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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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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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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.

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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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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.

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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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)

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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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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

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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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

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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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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)

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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