3,269 research outputs found
Revisiting Pre-Trained Models for Chinese Natural Language Processing
Bidirectional Encoder Representations from Transformers (BERT) has shown
marvelous improvements across various NLP tasks, and consecutive variants have
been proposed to further improve the performance of the pre-trained language
models. In this paper, we target on revisiting Chinese pre-trained language
models to examine their effectiveness in a non-English language and release the
Chinese pre-trained language model series to the community. We also propose a
simple but effective model called MacBERT, which improves upon RoBERTa in
several ways, especially the masking strategy that adopts MLM as correction
(Mac). We carried out extensive experiments on eight Chinese NLP tasks to
revisit the existing pre-trained language models as well as the proposed
MacBERT. Experimental results show that MacBERT could achieve state-of-the-art
performances on many NLP tasks, and we also ablate details with several
findings that may help future research. Resources available:
https://github.com/ymcui/MacBERTComment: 12 pages, to appear at Findings of EMNLP 202
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently
and has attracted a lot of attention. However, the existing reading
comprehension datasets are mostly in English. In this paper, we introduce a
Span-Extraction dataset for Chinese machine reading comprehension to add
language diversities in this area. The dataset is composed by near 20,000 real
questions annotated on Wikipedia paragraphs by human experts. We also annotated
a challenge set which contains the questions that need comprehensive
understanding and multi-sentence inference throughout the context. We present
several baseline systems as well as anonymous submissions for demonstrating the
difficulties in this dataset. With the release of the dataset, we hosted the
Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC
2018). We hope the release of the dataset could further accelerate the Chinese
machine reading comprehension research. Resources are available:
https://github.com/ymcui/cmrc2018Comment: 6 pages, accepted as a conference paper at EMNLP-IJCNLP 2019 (short
paper
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