Papers with Chinese natural language processing
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing (2020.tacl-1)
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| Challenge: | Chinese word segmentation and dependency parsing suffer from error propagation . a graph-based model can integrate both tasks, but it suffers from performance limitations . |
| Approach: | They propose a graph-based model to integrate Chinese word segmentation and dependency parsing . their model achieves better performance than previous joint models . |
| Outcome: | The proposed model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. |
fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP (2021.acl-demo)
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| Challenge: | Recently, the need for Chinese natural language processing (NLP) has a dramatic increase for many downstream applications. |
| Approach: | They propose to use Chinese word segmentation (CWS), Part-of-Speech (POS) tagging, named entity recognition (NER), and dependency parsing to train a multi-task model based on a pruned BERT. |
| Outcome: | The proposed model performs better than popular segmentation tools on a non-training corpus. |
MVP-BERT: Multi-Vocab Pre-training for Chinese BERT (2021.acl-srw)
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| Challenge: | Pre-trained language models (PLMs) improve performance of Chinese NLP tasks, but the vocabulary (vocab) for these Chinese PLMs remains to be the one provided by Google Chinese BERT (CITATION), which is based on Chinese characters (chars). |
| Approach: | They propose two versions of multi-vocab pre-training (MVP) to improve Chinese PLMs' expressiveness. |
| Outcome: | The proposed model outperforms the recent AMBERT (CITATION) model on span-level tasks and is more robust against adversarial attacks. |
That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory (2022.acl-long)
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| Challenge: | Grammar, vocabulary, and lexical semantic shifts take place over time, creating diachronic linguistic gap. |
| Approach: | a Chinese cross-era learning framework incorporates era-specific linguistic knowledge into a neural network. |
| Outcome: | a new learning framework for Chinese word segmentation improves on four corpora from different eras. |
ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition (2022.lrec-1)
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| Challenge: | Chinese word segmentation and named entity recognition are important tasks in natural language processing. |
| Approach: | They develop a Chinese medical corpus annotated with Chinese word boundary and medical term information to address this problem. |
| Outcome: | The proposed corpus will be a valuable resource for Chinese word segmentation and named entity recognition research on the medical domain. |