LCQMC:A Large-scale Chinese Question Matching Corpus

By Xin Liu, Qingcai Chen, Chong Deng, Hua-Jun Zeng, Dongfang Li, Buzhou Tang. 2018

226 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Maosong Sun
2. Zhiyuan Liu
3. Ting-Wei Liu
4. Yang Liu
5. Hua Wu
6. Bing Qin
7. Qun Liu
8. Yiming Cui

In Evaluation & Benchmarks

1. Zhiyuan Liu
2. Maosong Sun
3. Xu Han
4. Shuohuan Wang
5. Haifeng Wang
6. Shi Feng
7. Yuan Yao
8. Xiaoyan Zhu

Citations in the corpus

• ERNIE: Enhanced Representation through Knowledge Integration (2019, background): “The Large-scale Chinese Question Matching Corpus (LCQMC) (Liu et al., 2018) aims at identifying whether two sentences have the same intention.”
• ERNIE 2.0: A Continual Pre-training Framework for Language Understanding (2019, methodology): “For the SS task, we use the LCQMC [32] and BQ Corpus [33] datasets.”
• FastBERT: a Self-distilling BERT with Adaptive Inference Time (2020, methodology): “The six Chinese datasets include the sentence classification tasks (ChnSentiCorp, Book review(Qiu et al., 2018), Shopping review, Weibo and THUCNews) and a sentences-matching task (LCQMC(Liu et al., 2018)).”
• K-BERT: Enabling Language Representation with Knowledge Graph (2019, methodology): “XNLI (Conneau et al. 2018), LCQMC (Liu et al. 2018) are two-sentence classification tasks, NLPCC-DBQA 12 is a Q&A matching task, and MSRA-NER (Levow 2006) is a Named Entity Recognition (NER) task: • XNLI XNLI is a cross-language language understanding dataset in which each entry contains two…”
• ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation (2021, background): “0 Titan on five typical types of downstream tasks, including natural language inference (XNLI [103]), semantic analysis (ChnSentiCorp [104]), document question answering (NLPCC-DBQA22), semantic similarity (LCQMC [73]), and machine reading comprehension (CMRC2018 [105]).”
• TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020, methodology): “For Chinese datasets, we use XNLI (Conneau et al., 2018), LCQMC (Liu et al., 2018), CMRC 2018 (Cui et al., 2019b) and DRCD (Shao et al., 2018).”
• CPM-2: Large-scale Cost-effective Pre-trained Language Models (2021, methodology): “Given two questions, LCQMC requires the model to answer whether the two questions express similar intent.”
• NEZHA: Neural Contextualized Representation for Chinese Language Understanding (2019, background): “• LCQMC (Large-scale Chinese Question Matching Corpus) [18]: A sentence pair matching task.”
• Revisiting Pre-Trained Models for Chinese Natural Language Processing (2020, unknown): “CMRC 2018, DRCD, CJRC. • Sentence Pair Matching (SPM) : XNLI, LCQMC, BQ Corpus.”
• Pre-Training with Whole Word Masking for Chinese BERT (2019, methodology): “…Inference (NLI) : XNLI (Conneau et al., 2018) • Sentiment Classification (SC) : ChnSentiCorp 12 , Sina Weibo 13 • Sentence Pair Matching (SPM) : LCQMC (Liu et al., 2018), BQ Corpus (Chen et al., 2018) • Document Classification (DC) : THUCNews (Li and Sun, 2007) In order to make a fair…”
• ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding (2020, methodology): “We execute extensive experiments on seven Chinese language understanding tasks, including natural language inference (XNLI; Conneau et al., 2018), machine reading comprehension (CMRC2018, DRCD, DuReader; Cui et al., 2018; Shao et al., 2018; He et al., 2017), named entity recognition (MSRA-NER; Levow”