Jean Maillard


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Conversational Semantic Parsing
Armen Aghajanyan | Jean Maillard | Akshat Shrivastava | Keith Diedrick | Michael Haeger | Haoran Li | Yashar Mehdad | Veselin Stoyanov | Anuj Kumar | Mike Lewis | Sonal Gupta
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the representation, the session-based properties such as co-reference resolution and context carryover are processed downstream in a pipelined system. In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session. We release a new session-based, compositional task-oriented parsing dataset of 20k sessions consisting of 60k utterances. Unlike Dialog State Tracking Challenges, the queries in the dataset have compositional forms. We propose a new family of Seq2Seq models for the session-based parsing above, which also set state-of-the-art in ATIS, SNIPS, TOP and DSTC2. Notably, we improve the best known results on DSTC2 by up to 5 points for slot-carryover.

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Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models
Vesna G. Djokic | Jean Maillard | Luana Bulat | Ekaterina Shutova
Transactions of the Association for Computational Linguistics, Volume 8

Recent years have seen a growing interest within the natural language processing (NLP) community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying semantic models to decode brain activity patterns associated with the meaning of individual words, and, more recently, this approach has been extended to sentences and larger text fragments. Our work is the first to investigate metaphor processing in the brain in this context. We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences. Our results suggest that compositional models and word embeddings are able to capture differences in the processing of literal and metaphoric sentences, providing support for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension.


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Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models
Vesna Djokic | Jean Maillard | Luana Bulat | Ekaterina Shutova
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent work shows that distributional semantic models can be used to decode patterns of brain activity associated with individual words and sentence meanings. However, it is yet unclear to what extent such models can be used to study and decode fMRI patterns associated with specific aspects of semantic composition such as the negation function. In this paper, we apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs. Our results show reduced decoding (correlation) of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing. This supports behavioral and brain imaging studies, suggesting that negation involves reduced access to aspects of the affirmative mental representation. The results pave the way for testing alternate semantic models of negation against human semantic processing in the brain.


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Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing
Jean Maillard | Stephen Clark
Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP

Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines which use (externally provided) syntax trees to drive the composition order. This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model.


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Black Holes and White Rabbits: Metaphor Identification with Visual Features
Ekaterina Shutova | Douwe Kiela | Jean Maillard
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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RELPRON: A Relative Clause Evaluation Data Set for Compositional Distributional Semantics
Laura Rimell | Jean Maillard | Tamara Polajnar | Stephen Clark
Computational Linguistics, Volume 42, Issue 4 - December 2016


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Learning Adjective Meanings with a Tensor-Based Skip-Gram Model
Jean Maillard | Stephen Clark
Proceedings of the Nineteenth Conference on Computational Natural Language Learning


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A Type-Driven Tensor-Based Semantics for CCG
Jean Maillard | Stephen Clark | Edward Grefenstette
Proceedings of the EACL 2014 Workshop on Type Theory and Natural Language Semantics (TTNLS)