| Challenge: | Recent work on Chinese word segmentation has been concerned about the following three perspectives. |
| Approach: | They propose to use a greedy decoding algorithm to improve Chinese word segmentation model. |
| Outcome: | The proposed model achieves state-of-the-art or comparable performance against strong baselines in strict closed test setting. |
Similar Papers
CWSeg: An Efficient and General Approach to Chinese Word Segmentation (2023.acl-industry)
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| Challenge: | Existing methods for Chinese word segmentation have achieved state-of-the-art performance, but they pose challenges in the deployment. |
| Approach: | They propose to augment PLM-based Chinese word segmentation schemes by developing cohort training and versatile decoding strategies. |
| Outcome: | The proposed model can be used to augment existing PLM-based models and improve their performance on Chinese LLaMA and Alpaca datasets. |
RethinkCWS: Is Chinese Word Segmentation a Solved Task? (2020.emnlp-main)
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| Challenge: | Recent years have seen remarkable success in the use of deep neural networks on Chinese word segmentation (CWS) however, the performance of CWS systems has gradually reached a plateau with the rapid development of deep networks. |
| Approach: | They propose a fine-grained evaluation for existing Chinese word segmentation systems that allows us to diagnose the strengths and weaknesses of existing models. |
| Outcome: | The proposed model can diagnose strengths and weaknesses of existing models and alleviate negative transfer problem when doing multi-criteria learning. |
Improving Multi-Criteria Chinese Word Segmentation through Learning Sentence Representation (2023.findings-emnlp)
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| Challenge: | Recent Chinese word segmentation models tend to learn the segmentation knowledge through in-vocabulary words rather than understanding the meaning of the entire context. |
| Approach: | They propose a context-aware approach that incorporates unsupervised sentence representation learning over different dropout masks into the multi-criteria training framework. |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on six of the nine CWS benchmark datasets and out-of vocabulary (OOV) recalls for eight of nine. |
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision (2021.emnlp-main)
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Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun
| Challenge: | Recent state-of-the-art (SOTA) effective neural network methods have been used in Chinese word segmentation (CWS) However, the robustness of the previous neural methods is limited by the large-scale annotated corpus. |
| Approach: | They propose a self-supervised Chinese word segmentation approach with a straightforward and effective architecture. |
| Outcome: | The proposed approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness. |
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation (2021.findings-emnlp)
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| Challenge: | Existing methods for Chinese word segmentation have high performance on benchmarks but are limited by the small-scale annotated corpus. |
| Approach: | They propose a framework that incorporates a lexicon-based graph convolutional network into the Transformer encoder to improve Chinese word segmentation (CWS) Chinese word is an essential and pre-processing step for many downstream NLP tasks. |
| Outcome: | The proposed framework captures the information of candidate words and improves performance on benchmarks and datasets. |
Is Word Segmentation Necessary for Deep Learning of Chinese Representations? (P19-1)
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| Challenge: | Using word-based models, we compare word-oriented models with char-based ones . word-driven models are more vulnerable to data sparsity and the presence of out-of-vocabulary words . |
| Approach: | They benchmark word-based models with char-based model which does not involve word segmentation in four NLP benchmark tasks. |
| Outcome: | The proposed model outperforms char-based models in four NLP benchmark tasks. |
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning (2020.coling-main)
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| Challenge: | Chinese word segmentation datasets have ambiguous annotation criteria resulting in multi-grained compounds. |
| Approach: | They propose a domain adaptive segmenter to exploit diverse annotation criteria of datasets . they use bidirectional encoder representations from transformers to introduce open-domain knowledge . |
| Outcome: | The proposed model outperforms the state-of-the-art models on 10 Chinese word datasets with superior efficiency. |
Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling (D18-1)
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| Challenge: | Existing approaches to unsupervised Chinese word segmentation (CWS) are discriminative and generative, but they are non-trivial. |
| Approach: | They propose a neural generative model for fully unsupervised Chinese word segmentation (CWS) their approach explicitly focuses on the segmental nature of Chinese, and preserves several properties of language models. |
| Outcome: | The proposed model achieves competitive performance to the state-of-the-art models on four datasets from SIGHAN 2005 bakeoff. |
Adaptive Multi-Task Transfer Learning for Chinese Word Segmentation in Medical Text (C18-1)
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| Challenge: | Chinese word segmentation (CWS) tools face a performance drop when dealing with domain text . domain-specific CWS requires extremely high annotation cost due to ambiguity caused by domain terms and writing style . |
| Approach: | They propose to exploit domain-invariant knowledge from high resource to low resource domains to build Chinese word segmentation models. |
| Outcome: | The proposed model achieves higher accuracy than single-task CWS and other transfer learning baselines . the model is based on domain-invariant knowledge from high resource to low resource domains based in the biomedical domain . |
More than Text: Multi-modal Chinese Word Segmentation (2021.acl-short)
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| Challenge: | Currently, word segmentation is performed in many languages without word delimiters. |
| Approach: | They propose to combine the multi-modality to perform Chinese word segmentation . they propose a time-dependent multi-module interactive model to integrate multi-modality information . |
| Outcome: | The proposed model integrates multi-modal information for word sequence labeling with Chinese language as target . the proposed model performs well on three training sets on Chinese and other languages without word delimiters. |