Papers by Bei Hui
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation (2020.acl-main)
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| Challenge: | In encoder-decoder neural models, multiple encoders are used to represent contextual information in addition to the individual sentence. |
| Approach: | They propose to use multiple context encoders to encode the individual sentences in document-level neural machine translation (NMT) They propose a noisy dropout setup and a single-encoder approach to encode context sentences. |
| Outcome: | The proposed approach encodes the context and the current sentence without contexts. |
Shallow-to-Deep Training for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Experimental results show that deep training is 1:4 faster than training from scratch. |
| Approach: | They propose a shallow-to-deep training method that learns deep models by stacking shallow models. |
| Outcome: | The proposed method is 1:4 faster than training from scratch and achieves BLEU scores of 30:33 and 43:29 on two translation tasks. |
FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction (2024.lrec-main)
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| Challenge: | Document-level Relation Extraction (DocRE) aims to identify relation labels between entities within a document. |
| Approach: | They propose to fuse constituency and dependency syntax into DocRE to exploit the rich syntax information in the document. |
| Outcome: | The proposed method is able to identify relation labels between entities within a document and is scalable. |