Jason Naradowsky


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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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.

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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.

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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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)

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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)

2015

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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

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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

2012

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

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