Jason Naradowsky


2023

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Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models
Qiang Zhang | Jason Naradowsky | Yusuke Miyao
Findings of the Association for Computational Linguistics: ACL 2023

Existing dialogue models may encounter scenarios which are not well-represented in the training data, and as a result generate responses that are unnatural, inappropriate, or unhelpful. We propose the “Ask an Expert” framework in which the model is trained with access to an “expert” which it can consult at each turn. Advice is solicited via a structured dialogue with the expert, and the model is optimized to selectively utilize (or ignore) it given the context and dialogue history. In this work the expert takes the form of an LLM.We evaluate this framework in a mental health support domain, where the structure of the expert conversation is outlined by pre-specified prompts which reflect a reasoning strategy taught to practitioners in the field. Blenderbot models utilizing “Ask an Expert” show quality improvements across all expert sizes, including those with fewer parameters than the dialogue model itself. Our best model provides a ~10% improvement over baselines, approaching human-level scores on “engingingness” and “helpfulness” metrics.

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Mind the Gap Between Conversations for Improved Long-Term Dialogue Generation
Qiang Zhang | Jason Naradowsky | Yusuke Miyao
Findings of the Association for Computational Linguistics: EMNLP 2023

Knowing how to end and resume conversations over time is a natural part of communication, allowing for discussions to span weeks, months, or years. The duration of gaps between conversations dictates which topics are relevant and which questions to ask, and dialogue systems which do not explicitly model time may generate responses that are unnatural. In this work we explore the idea of making dialogue models aware of time, and present GapChat, a multi-session dialogue dataset in which the time between each session varies. While the dataset is constructed in real-time, progress on events in speakers’ lives is simulated in order to create realistic dialogues occurring across a long timespan. We expose time information to the model and compare different representations of time and event progress. In human evaluation we show that time-aware models perform better in metrics that judge the relevance of the chosen topics and the information gained from the conversation.

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Fiction-Writing Mode: An Effective Control for Human-Machine Collaborative Writing
Wenjie Zhong | Jason Naradowsky | Hiroya Takamura | Ichiro Kobayashi | Yusuke Miyao
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

We explore the idea of incorporating concepts from writing skills curricula into human-machine collaborative writing scenarios, focusing on adding writing modes as a control for text generation models. Using crowd-sourced workers, we annotate a corpus of narrative text paragraphs with writing mode labels. Classifiers trained on this data achieve an average accuracy of ~87% on held-out data. We fine-tune a set of large language models to condition on writing mode labels, and show that the generated text is recognized as belonging to the specified mode with high accuracy. To study the ability of writing modes to provide fine-grained control over generated text, we devise a novel turn-based text reconstruction game to evaluate the difference between the generated text and the author’s intention. We show that authors prefer text suggestions made by writing mode-controlled models on average 61.1% of the time, with satisfaction scores 0.5 higher on a 5-point ordinal scale. When evaluated by humans, stories generated via collaboration with writing mode-controlled models achieve high similarity with the professionally written target story. We conclude by identifying the most common mistakes found in the generated stories.

2022

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Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem
Qiang Zhang | Jason Naradowsky | Yusuke Miyao
Findings of the Association for Computational Linguistics: ACL 2022

We introduce the task of implicit offensive text detection in dialogues, where a statement may have either an offensive or non-offensive interpretation, depending on the listener and context. We argue that reasoning is crucial for understanding this broader class of offensive utterances, and release SLIGHT, a dataset to support research on this task. Experiments using the data show that state-of-the-art methods of offense detection perform poorly when asked to detect implicitly offensive statements, achieving only ∼ 11% accuracy. In contrast to existing offensive text detection datasets, SLIGHT features human-annotated chains of reasoning which describe the mental process by which an offensive interpretation can be reached from each ambiguous statement. We explore the potential for a multi-hop reasoning approach by utilizing existing entailment models to score the probability of these chains, and show that even naive reasoning models can yield improved performance in most situations. Analysis of the chains provides insight into the human interpretation process and emphasizes the importance of incorporating additional commonsense knowledge.

2020

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Machine Translation System Selection from Bandit Feedback
Jason Naradowsky | Xuan Zhang | Kevin Duh
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

2018

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A Structured Variational Autoencoder for Contextual Morphological Inflection
Lawrence Wolf-Sonkin | Jason Naradowsky | Sabrina J. Mielke | Ryan Cotterell
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Statistical morphological inflectors are typically trained on fully supervised, type-level data. One remaining open research question is the following: How can we effectively exploit raw, token-level data to improve their performance? To this end, we introduce a novel generative latent-variable model for the semi-supervised learning of inflection generation. To enable posterior inference over the latent variables, we derive an efficient variational inference procedure based on the wake-sleep algorithm. We experiment on 23 languages, using the Universal Dependencies corpora in a simulated low-resource setting, and find improvements of over 10% absolute accuracy in some cases.

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Hypothesis Only Baselines in Natural Language Inference
Adam Poliak | Jason Naradowsky | Aparajita Haldar | Rachel Rudinger | Benjamin Van Durme
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.

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Gender Bias in Coreference Resolution
Rachel Rudinger | Jason Naradowsky | Brian Leonard | Benjamin Van Durme
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these “Winogender schemas,” we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics.

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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
Daniela Gerz | Ivan Vulić | Edoardo Ponti | Jason Naradowsky | Roi Reichart | Anna Korhonen
Transactions of the Association for Computational Linguistics, Volume 6

Neural architectures are prominent in the construction of language models (LMs). However, word-level prediction is typically agnostic of subword-level information (characters and character sequences) and operates over a closed vocabulary, consisting of a limited word set. Indeed, while subword-aware models boost performance across a variety of NLP tasks, previous work did not evaluate the ability of these models to assist next-word prediction in language modeling tasks. Such subword-level informed models should be particularly effective for morphologically-rich languages (MRLs) that exhibit high type-to-token ratios. In this work, we present a large-scale LM study on 50 typologically diverse languages covering a wide variety of morphological systems, and offer new LM benchmarks to the community, while considering subword-level information. The main technical contribution of our work is a novel method for injecting subword-level information into semantic word vectors, integrated into the neural language modeling training, to facilitate word-level prediction. We conduct experiments in the LM setting where the number of infrequent words is large, and demonstrate strong perplexity gains across our 50 languages, especially for morphologically-rich languages. Our code and data sets are publicly available.

2017

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Break it Down for Me: A Study in Automated Lyric Annotation
Lucas Sterckx | Jason Naradowsky | Bill Byrne | Thomas Demeester | Chris Develder
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA is to rephrase the original text in a more easily understandable manner. However, in ALA the system must often include additional information to clarify niche terminology and abstract concepts. To stimulate research on this task, we release a large collection of crowdsourced annotations for song lyrics. We analyze the performance of translation and retrieval models on this task, measuring performance with both automated and human evaluation. We find that each model captures a unique type of information important to the task.

2016

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UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound
James Goodman | Andreas Vlachos | Jason Naradowsky
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing
James Goodman | Andreas Vlachos | Jason Naradowsky
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Matrix and Tensor Factorization Methods for Natural Language Processing
Guillaume Bouchard | Jason Naradowsky | Sebastian Riedel | Tim Rocktäschel | Andreas Vlachos
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Tutorial Abstracts

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WOLFE: An NLP-friendly Declarative Machine Learning Stack
Sameer Singh | Tim Rocktäschel | Luke Hewitt | Jason Naradowsky | Sebastian Riedel
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

2012

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Improving NLP through Marginalization of Hidden Syntactic Structure
Jason Naradowsky | Sebastian Riedel | David Smith
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Grammarless Parsing for Joint Inference
Jason Naradowsky | Tim Vieira | David Smith
Proceedings of COLING 2012

2011

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A Discriminative Model for Joint Morphological Disambiguation and Dependency Parsing
John Lee | Jason Naradowsky | David A. Smith
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Unsupervised Bilingual Morpheme Segmentation and Alignment with Context-rich Hidden Semi-Markov Models
Jason Naradowsky | Kristina Toutanova
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2009

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Polylingual Topic Models
David Mimno | Hanna M. Wallach | Jason Naradowsky | David A. Smith | Andrew McCallum
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing