Qi Chen


2020

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MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
Hai Hu | Qi Chen | Kyle Richardson | Atreyee Mukherjee | Lawrence S. Moss | Sandra Kuebler
Proceedings of the Society for Computation in Linguistics 2020

2019

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Natural Language Inference with Monotonicity
Hai Hu | Qi Chen | Larry Moss
Proceedings of the 13th International Conference on Computational Semantics - Short Papers

This paper describes a working system which performs natural language inference using polarity-marked parse trees. The system handles all of the instances of monotonicity inference in the FraCaS data set. Except for the initial parse, it is entirely deterministic. It handles multi-premise arguments, and the kind of inference performed is essentially “logical”, but it goes beyond what is representable in first-order logic. In any case, the system works on surface forms rather than on representations of any kind.

2018

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Detecting Free Translation in Parallel Corpora from Attention Scores
Qi Chen | Oi Yee Kwong | Jingbo Zhu
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
Chen Shi | Qi Chen | Lei Sha | Sujian Li | Xu Sun | Houfeng Wang | Lintao Zhang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The lack of labeled data is one of the main challenges when building a task-oriented dialogue system. Existing dialogue datasets usually rely on human labeling, which is expensive, limited in size, and in low coverage. In this paper, we instead propose our framework auto-dialabel to automatically cluster the dialogue intents and slots. In this framework, we collect a set of context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling. Experimental results show that our framework can promote human labeling cost to a great extent, achieve good intent clustering accuracy (84.1%), and provide reasonable and instructive slot labeling results.