Yaohan He
2022
CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification
Keyu Pu
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Hongyi Liu
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Yixiao Yang
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Jiangzhou Ji
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Wenyi Lv
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Yaohan He
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
This paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition. What is challenging in this task is detecting semantically ambiguous and complex entities in short and low-context settings. Our team (CMB AI Lab) propose a two-stage method to recognize the named entities: first, a model based on biaffine layer is built to predict span boundaries, and then a span classification model based on pooling layer is built to predict semantic tags of the spans. The basic pre-trained models we choose are XLM-RoBERTa and mT5. The evaluation result of our approach achieves an F1 score of 84.62 on sub-task 13, which ranks the third on the learder board.
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing
Runxin Sun
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Shizhu He
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Chong Zhu
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Yaohan He
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Jinlong Li
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Jun Zhao
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Kang Liu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Text-to-SQL aims to parse natural language questions into SQL queries, which is valuable in providing an easy interface to access large databases. Previous work has observed that leveraging lexico-logical alignments is very helpful to improve parsing performance. However, current attention-based approaches can only model such alignments at the token level and have unsatisfactory generalization capability. In this paper, we propose a new approach to leveraging explicit lexico-logical alignments. It first identifies possible phrase-level alignments and injects them as additional contexts to guide the parsing procedure. Experimental results on \textsc{Squall} show that our approach can make better use of such alignments and obtains an absolute improvement of 3.4% compared with the current state-of-the-art.
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Co-authors
- Keyu Pu 1
- Hongyi Liu 1
- Yixiao Yang 1
- Jiangzhou Ji 1
- Wenyi Lv 1
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