4 accepted papers
The grammatical error correction (GEC) task aims to detect and correct grammatical errors in text to enhance its accuracy and readability. Current GEC methods primarily rely on grammatical labels for syntactic information, often overlooking the inherent usage patterns of language. In this work, we e…
Recent studies have shown that integrating constructional information can improve the performance of pre-trained language models (PLMs) in natural language understanding. However, exploration into leveraging constructional information to enhance generative language models for natural language genera…
Natural language understanding (NLU) is an essential branch of natural language processing, which relies on representations generated by pre-trained language models (PLMs). However, PLMs primarily focus on acquiring lexico-semantic information, while they may be unable to adequately handle the meani…
Grammatical Error Correction (GEC) has been broadly applied in automatic correction and proofreading system recently. However, it is still immature in Chinese GEC due to limited high-quality data from native speakers in terms of category and scale. In this paper, we present FCGEC, a fine-grained cor…