no code implementations • CCL 2020 • Qinan Fan, Cunliang Kong, Liner Yang, Erhong Yang
释义生成任务是指为一个目标词生成相应的释义。前人研究中文释义生成任务时未考虑目标词的上下文, 本文首次在中文释义生成任务中使用了目标词的上下文信息, 并提出了一个基于BERT与柱搜索的释义生成模型。本文构建了包含上下文的CWN中文数据集用于开展实验, 除了BLEU指标之外, 还使用语义相似度作为额外的自动评价指标, 实验结果显示本文模型在中文CWN数据集和英文Oxford数据集上均有显著提升, 人工评价结果也与自动评价结果一致。最后, 本文对生成实例进行了深入分析。
1 code implementation • 2 Dec 2024 • Zhu Liu, Cunliang Kong, Ying Liu, Maosong Sun
In this paper, we propose a novel graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner.
1 code implementation • 3 Mar 2024 • Zhu Liu, Cunliang Kong, Ying Liu, Maosong Sun
This is in contrast to models with discriminative objectives, such as mask language modeling, where the higher layers obtain better lexical semantics.
no code implementations • 26 Feb 2024 • Jingsi Yu, Cunliang Kong, Liner Yang, Meishan Zhang, Lin Zhu, Yujie Wang, Haozhe Lin, Maosong Sun, Erhong Yang
Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching. Existing SPS parsers rely heavily on textbook corpora for training, lacking cross-domain capability. To overcome this constraint, this paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework.
no code implementations • 21 Feb 2024 • Luming Lu, Jiyuan An, Yujie Wang, Liner Yang, Cunliang Kong, Zhenghao Liu, Shuo Wang, Haozhe Lin, Mingwei Fang, Yaping Huang, Erhong Yang
This paper presents the first text-to-CQL task that aims to automate the translation of natural language into CQL.
1 code implementation • 21 Feb 2024 • Meng Xu, Shuo Wang, Liner Yang, Haoyu Wang, Zhenghao Liu, Cunliang Kong, Yun Chen, Yang Liu, Maosong Sun, Erhong Yang
We evaluate several representative multilingual LLMs on the proposed OMGEval, which we believe will provide a valuable reference for the community to further understand and improve the multilingual capability of LLMs.
no code implementations • 26 Nov 2022 • Jinran Nie, Liner Yang, Yun Chen, Cunliang Kong, Junhui Zhu, Erhong Yang
Compared with potential solutions, our approach fuses the representations of the word complexity levels into the model to get better control of lexical complexity.
no code implementations • CCL 2022 • Jiaxin Yuan, Cunliang Kong, Chenhui Xie, Liner Yang, Erhong Yang
To the best of our knowledge, it is the largest dataset of the Chinese definition generation task.
1 code implementation • 23 Apr 2022 • Cunliang Kong, Xuezhi Fang, Liner Yang, Yun Chen, Erhong Yang
Since traditional dictionaries present word senses as discrete items in predefined inventories, they fall short of flexibility, which is required in providing specific meanings of words in particular contexts.
1 code implementation • SemEval (NAACL) 2022 • Cunliang Kong, Yujie Wang, Ruining Chong, Liner Yang, Hengyuan Zhang, Erhong Yang, Yaping Huang
This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack, achieving 1st on Italian, 2nd on Spanish and Russian, and 3rd on English and French.
2 code implementations • ACL 2022 • Cunliang Kong, Yun Chen, Hengyuan Zhang, Liner Yang, Erhong Yang
We demonstrate that the framework can generate relevant, simple definitions for the target words through automatic and manual evaluations on English and Chinese datasets.
1 code implementation • 30 Dec 2021 • Yingying Wang, Cunliang Kong, Liner Yang, Yijun Wang, Xiaorong Lu, Renfen Hu, Shan He, Zhenghao Liu, Yun Chen, Erhong Yang, Maosong Sun
This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction.
no code implementations • 12 Oct 2020 • Cunliang Kong, Liner Yang, Tianzuo Zhang, Qinan Fan, Zhenghao Liu, Yun Chen, Erhong Yang
We demonstrate the effectiveness of this approach on zero-shot definition generation.
1 code implementation • 16 May 2019 • Liner Yang, Cunliang Kong, Yun Chen, Yang Liu, Qinan Fan, Erhong Yang
To accomplish this task, we construct the Chinese Definition Modeling Corpus (CDM), which contains triples of word, sememes and the corresponding definition.
no code implementations • IJCNLP 2017 • Changliang Li, Cunliang Kong
Multi-choice question answering in exams is a typical QA task.