Papers by Shenghuan He
A Flexible Multi-Task Model for BERT Serving (2022.acl-short)
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| Challenge: | a proposed BERT-based multi-task framework is suitable for iterative and incremental development of the tasks. |
| Approach: | They propose an efficient BERT-based multi-task framework that is suitable for iterative and incremental development of the tasks. |
| Outcome: | The proposed framework achieves 99.6% of performance of the full fine-tuning method while reducing up to two thirds of its overhead. |
Masked Conditional Random Fields for Sequence Labeling (2021.naacl-main)
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| Challenge: | Conditional Random Fields (CRF) based neural models are among the most performant for sequence labeling problems, but they can sometimes generate illegal sequences of tags. |
| Approach: | They propose a conditional random field-based model that imposes restrictions on candidate paths during both training and decoding phases. |
| Outcome: | The proposed method improves on existing CRF models with near zero additional cost. |