Papers by Lvxiaowei Xu
Enhancing Language Representation with Constructional Information for Natural Language Understanding (2023.acl-long)
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| Challenge: | Recent advances in natural language processing focus on acquiring lexico-semantic information. |
| Approach: | They propose a construction grammar which highlights the pairings of form and meaning to enrich language representation. |
| Outcome: | The proposed model is superior to existing models on a variety of NLU tasks. |
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction (2022.findings-emnlp)
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| Challenge: | grammatical error correction (GEC) is a complex task that requires high-quality data from native speakers. |
| Approach: | They propose a human-annotated corpus to detect, identify and correct grammatical errors in Chinese examinations. |
| Outcome: | The proposed model outperforms other models in low-resource settings, but there is a significant gap between the models and humans that encourages future models to bridge it. |
CxGGEC: Construction-Guided Grammatical Error Correction (2025.acl-long)
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| Challenge: | Current GEC methods rely on grammatical labels for syntactic information, often overlooking the inherent usage patterns of language. |
| Approach: | They propose to use construction grammar to capture underlying language patterns and guide corrections by decoding construction tokens into their original forms and correcting erroneous tokens. |
| Outcome: | The proposed model captures underlying language patterns and corrects erroneous construction tokens on English and Chinese benchmarks. |
CoELM: Construction-Enhanced Language Modeling (2024.acl-long)
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| Challenge: | Recent studies show that integrating constructional information can improve the performance of pre-trained language models. |
| Approach: | They propose a construction-Enhanced language model that embeds constructional semantics into language models for natural language generation. |
| Outcome: | The proposed model outperforms existing models on various benchmarks. |