Papers by Nianwen Si
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech (2021.acl-long)
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| Challenge: | Existing work regards dropped pronoun recovery and conversational discourse parsing as two separate tasks and tackles them separately. |
| Approach: | They propose a neural model for dropped pronoun recovery and conversational discourse parsing in Chinese conversational speech. |
| Outcome: | The proposed model outperforms the state-of-the-art models on a new dataset . the proposed model is based on linguistic and semantic information from Chinese conversational speech . |
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields (2020.findings-emnlp)
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| Challenge: | Existing approaches to recover dropped pronouns ignore the dependencies between pronounes in neighboring utterances. |
| Approach: | They propose a framework that combines Transformer network and General Conditional Random Fields to model the dependencies between pronouns in neighboring utterances. |
| Outcome: | The proposed framework outperforms state-of-the-art models on three Chinese conversation datasets showing that it captures the dependencies between pronouns in neighboring utterances. |
Recovering dropped pronouns in Chinese conversations via modeling their referents (N19-1)
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| Challenge: | Pronouns are often dropped in conversational genres as their referents can be easily understood from context. |
| Approach: | They propose an end-to-end neural network model to recover dropped pronouns in conversational data. |
| Outcome: | The proposed model improves on three different conversational genres. |
FCGCL: Fine- and Coarse-Granularity Contrastive Learning for Speech Translation (2022.findings-emnlp)
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| Challenge: | Existing methods to perform implicit knowledge transfer from machine translation to ST model are difficult because of the task complexity and data scarcity. |
| Approach: | They recommend a method which conducts explicit knowledge transfer from MT to ST model by fine and coarse granularity contrastive learning. |
| Outcome: | The proposed method improves the performance of the end-to-end speech translation model on all 8 languages. |