Papers by Lejian Liao
Modularized Interaction Network for Named Entity Recognition (2021.acl-long)
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| Challenge: | Named Entity Recognition (NER) models focus on word-level information, while segment-based models focus only on word level information. |
| Approach: | They propose a Modularized Interaction Network (MIN) model which utilizes both word-level information and segment-level dependencies. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on three NER benchmark datasets. |
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction (D18-1)
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| Challenge: | Existing methods to extract genre-specific and genre-agnostic features require great human effort. |
| Approach: | They propose to use two encoders to explicitly extract genre-specific and genre-agnostic features. |
| Outcome: | The proposed approach outperforms the state-of-the-art by 1.7% on three distinct genres. |
To be Closer: Learning to Link up Aspects with Opinions (2021.emnlp-main)
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| Challenge: | Dependency parsers are not designed for capturing interaction between opinion words and aspect words. |
| Approach: | They propose to learn an aspect-centric tree structure to shorten distance between aspects and opinion words. |
| Outcome: | The proposed model outperforms baselines on five aspect-based sentiment datasets. |