Papers by Jingsong Yu
Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020.findings-emnlp)
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| Challenge: | Spatial domain queries have unique properties making them more challenging for language understanding than common conversational queries. |
| Approach: | They propose a language understanding framework for spatial domain queries that jointly learns the intent detection and entity linking tasks on a voice assistant service. |
| Outcome: | The proposed framework outperforms baseline methods with a significant margin. |
Position Offset Label Prediction for Grammatical Error Correction (2022.coling-1)
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| Challenge: | Experimental results show that our proposed POL-Pc framework improves baseline models and yields consistent performance gain over various data augmentation methods. |
| Approach: | They propose a position offset label prediction subtask to integrate correction editing operations into a unified framework. |
| Outcome: | The proposed model outperforms baseline models on Chinese, English and Japanese datasets by a wide margin. |
Unsupervised Context Rewriting for Open Domain Conversation (D19-1)
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| Challenge: | Existing approaches to model conversation context have drawbacks, such as lack of coreferences and long dependency. |
| Approach: | They propose a context rewriting method which explicitly rewrites the last utterance by considering context history. |
| Outcome: | The proposed method outperforms baselines in terms of rewriting quality, multi-turn response generation, and end-to-end retrieval-based chatbots. |
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior (D19-58)
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| Challenge: | Recent studies indicate that the current machine reading comprehension systems suffer from weak robustness against adversarial samples. |
| Approach: | They propose to take sentence syntax as the leverage in the answer predicting process and exploit the syntactic elements of a question to improve the generalization and robustness of MRC models. |
| Outcome: | The proposed method improves generalization and robustness against adversarial samples, with performance well-maintained. |