Alice Lai


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Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
Alice Lai | Joel Tetreault
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

To date there has been very little work on assessing discourse coherence methods on real-world data. To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. We show that neural models, including two that we introduce here (SentAvg and ParSeq), tend to perform best. We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains.


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Learning to Predict Denotational Probabilities For Modeling Entailment
Alice Lai | Julia Hockenmaier
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and SNLI.

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Natural Language Inference from Multiple Premises
Alice Lai | Yonatan Bisk | Julia Hockenmaier
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We define a novel textual entailment task that requires inference over multiple premise sentences. We present a new dataset for this task that minimizes trivial lexical inferences, emphasizes knowledge of everyday events, and presents a more challenging setting for textual entailment. We evaluate several strong neural baselines and analyze how the multiple premise task differs from standard textual entailment.


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Illinois-LH: A Denotational and Distributional Approach to Semantics
Alice Lai | Julia Hockenmaier
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young | Alice Lai | Micah Hodosh | Julia Hockenmaier
Transactions of the Association for Computational Linguistics, Volume 2

We propose to use the visual denotations of linguistic expressions (i.e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference. To compute these denotational similarities, we construct a denotation graph, i.e. a subsumption hierarchy over constituents and their denotations, based on a large corpus of 30K images and 150K descriptive captions.