Papers by Qiaolin Xia
Incorporating Glosses into Neural Word Sense Disambiguation (P18-1)
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| Challenge: | Existing neural networks for Word Sense Disambiguation rely on labeled data and lexical knowledge. |
| Approach: | They propose a gloss-augmented WSD neural network which integrates context and glosses of the target word into a unified framework. |
| Outcome: | The proposed model outperforms the state-of-the-art systems on several English all-words WSD datasets. |
Robust Navigation with Language Pretraining and Stochastic Sampling (D19-1)
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Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah A. Smith, Yejin Choi
| Challenge: | Existing methods to learn visual representations and action decoding schemes are limited to previously unseen instructions and environments. |
| Approach: | They propose a stochastic sampling scheme to reduce the gap between the expert actions in training and sampled actions in test to correct its own mistakes. |
| Outcome: | The proposed methods achieve 6% absolute gain over the previous best results on the Room-to-Room benchmark. |
Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention (D18-1)
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| Challenge: | Existing models for Word Sense Disambiguation use labeled data, but lack gloss knowledge. |
| Approach: | They propose a co-attention mechanism to generate co-dependent representations for context and gloss . they propose to incorporate gloss knowledge into neural networks for Word Sense Disambiguation . |
| Outcome: | The proposed model achieves state-of-the-art results on standard English all-words WSD datasets. |