Will Monroe


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Simultaneous Translation and Paraphrase for Language Education
Stephen Mayhew | Klinton Bicknell | Chris Brust | Bill McDowell | Will Monroe | Burr Settles
Proceedings of the Fourth Workshop on Neural Generation and Translation

We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE). Given a prompt in one language, the goal is to generate a diverse set of correct translations that language learners are likely to produce. This is motivated by the need to create and maintain large, high-quality sets of acceptable translations for exercises in a language-learning application, and synthesizes work spanning machine translation, MT evaluation, automatic paraphrasing, and language education technology. We developed a novel corpus with unique properties for five languages (Hungarian, Japanese, Korean, Portuguese, and Vietnamese), and report on the results of a shared task challenge which attracted 20 teams to solve the task. In our meta-analysis, we focus on three aspects of the resulting systems: external training corpus selection, model architecture and training decisions, and decoding and filtering strategies. We find that strong systems start with a large amount of generic training data, and then fine-tune with in-domain data, sampled according to our provided learner response frequencies.


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Generating Bilingual Pragmatic Color References
Will Monroe | Jennifer Hu | Andrew Jong | Christopher Potts
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Contextual influences on language often exhibit substantial cross-lingual regularities; for example, we are more verbose in situations that require finer distinctions. However, these regularities are sometimes obscured by semantic and syntactic differences. Using a newly-collected dataset of color reference games in Mandarin Chinese (which we release to the public), we confirm that a variety of constructions display the same sensitivity to contextual difficulty in Chinese and English. We then show that a neural speaker agent trained on bilingual data with a simple multitask learning approach displays more human-like patterns of context dependence and is more pragmatically informative than its monolingual Chinese counterpart. Moreover, this is not at the expense of language-specific semantic understanding: the resulting speaker model learns the different basic color term systems of English and Chinese (with noteworthy cross-lingual influences), and it can identify synonyms between the two languages using vector analogy operations on its output layer, despite having no exposure to parallel data.


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Adversarial Learning for Neural Dialogue Generation
Jiwei Li | Will Monroe | Tianlin Shi | Sébastien Jean | Alan Ritter | Dan Jurafsky
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We apply adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning problem where we jointly train two systems: a generative model to produce response sequences, and a discriminator—analagous to the human evaluator in the Turing test— to distinguish between the human-generated dialogues and the machine-generated ones. In this generative adversarial network approach, the outputs from the discriminator are used to encourage the system towards more human-like dialogue. Further, we investigate models for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines

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Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding
Will Monroe | Robert X.D. Hawkins | Noah D. Goodman | Christopher Potts
Transactions of the Association for Computational Linguistics, Volume 5

We present a model of pragmatic referring expression interpretation in a grounded communication task (identifying colors from descriptions) that draws upon predictions from two recurrent neural network classifiers, a speaker and a listener, unified by a recursive pragmatic reasoning framework. Experiments show that this combined pragmatic model interprets color descriptions more accurately than the classifiers from which it is built, and that much of this improvement results from combining the speaker and listener perspectives. We observe that pragmatic reasoning helps primarily in the hardest cases: when the model must distinguish very similar colors, or when few utterances adequately express the target color. Our findings make use of a newly-collected corpus of human utterances in color reference games, which exhibit a variety of pragmatic behaviors. We also show that the embedded speaker model reproduces many of these pragmatic behaviors.


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Deep Reinforcement Learning for Dialogue Generation
Jiwei Li | Will Monroe | Alan Ritter | Dan Jurafsky | Michel Galley | Jianfeng Gao
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Learning to Generate Compositional Color Descriptions
Will Monroe | Noah D. Goodman | Christopher Potts
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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Text to 3D Scene Generation with Rich Lexical Grounding
Angel Chang | Will Monroe | Manolis Savva | Christopher Potts | Christopher D. Manning
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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Word Segmentation of Informal Arabic with Domain Adaptation
Will Monroe | Spence Green | Christopher D. Manning
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)