Papers with CWS
Lingxi: A Diversity-aware Chinese Modern Poetry Generation System (2023.acl-demo)
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| Challenge: | Chinese modern poetry generation is a challenging task because of the word segmentation problem and decoding methods . the decoding method may induce repetition and boredom and lower the diversity of generated poetry. |
| Approach: | They propose a Chinese word segmentation-based decoding system that incorporates Chinese word segments into tokenization. |
| Outcome: | The proposed system can achieve high vocabulary coverage rate with a reasonable vocabulary size. |
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. |
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism (D18-1)
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| Challenge: | Existing methods for named entity recognition (NER) do not exploit word boundary information from CWS or cannot filter the specific information of CWS. |
| Approach: | They propose to exploit task-shared boundary information to make full use of Chinese NER task and Chinese word segmentation (CWS) . |
| Outcome: | The proposed model significantly outperforms other state-of-the-art methods on two widely used datasets. |
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. |
Multiple Character Embeddings for Chinese Word Segmentation (P19-2)
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| Challenge: | Chinese word segmentation is regarded as character-based sequence labeling task in most current work but it neglects important fact: Chinese characters contain both semantic and phonetic meanings. |
| Approach: | They propose a shared bi-LSTM-CRF model which fuses linguistic features efficiently by sharing the LSTM network during the training procedure. |
| Outcome: | The proposed model achieves state-of-the-art in AS and CityU corpora without external lexical resources. |
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. |
Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation (2025.coling-main)
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| Challenge: | Recent studies on Chinese Word Segmentation (CWS) have focused on the cross-domain scenarios, but there is a high cost of manually annotating high-quality data. |
| Approach: | They propose to explicitly mine word boundaries from parallel speech-text data by using the Montreal Forced Aligner toolkit to perform character-level alignment on speech- text data. |
| Outcome: | The proposed approach is based on character-level alignment on speech-text data and a robust complete-then-train (CTT) strategy. |
Weighted self Distillation for Chinese word segmentation (2022.findings-acl)
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| Challenge: | Recent researches show that multi-criteria resources and n-gram features are beneficial to Chinese word segmentation (CWS). |
| Approach: | They propose a framework that uses weighted self distillation to learn Chinese word segmentation using unigram features. |
| Outcome: | The proposed framework achieves state-of-the-art or competitive performance on SIGHAN Bakeoff datasets. |
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. |
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. |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams (2020.coling-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging can be performed in a sequential order . existing studies have shown that jointly performing them can be effective . |
| Approach: | They propose a character-based neural model enhanced by multi-channel attention of n-grams. |
| Outcome: | The proposed model outperforms baseline models on five benchmark datasets. |
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. |
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings (N19-1)
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| Challenge: | Existing approaches to Chinese word segmentation (CWS) are character-based and word-based . character-driven approaches use conditional random field models to label sequences, with complex hand-crafted discrete features. |
| Approach: | They propose a semi-supervised word-based approach to improve cross-domain Chinese word segmentation given a baseline segmenter. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on five datasets covering domains in novels, medicine, and patent. |
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 . |
Word Segmentation by Separation Inference for East Asian Languages (2022.findings-acl)
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| Challenge: | Chinese Word Segmentation (CWS) is a sequence labeling task that divides sentences into words . despite diverse tagging schemas, they all carry implicit position information. |
| Approach: | They propose to model the separation state of every two consecutive characters by tagging them as two tags. |
| Outcome: | The proposed framework outperforms state-of-the-art on Japanese and Korean Word Segmentation datasets. |
Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation (2022.findings-acl)
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| Challenge: | Existing methods for unsupervised Chinese word segmentation exploit shallow semantic information, which can miss important context. |
| Approach: | They propose to take advantage of deep contextual semantic information with a self-training manner to transform it into explicit word segmentation ability. |
| Outcome: | The proposed approach achieves state-of-the-art F1 score on two CWS benchmark 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. |
Attention Is All You Need for Chinese Word Segmentation (2020.emnlp-main)
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| 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. |
A Joint Multiple Criteria Model in Transfer Learning for Cross-domain Chinese Word Segmentation (2020.emnlp-main)
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| Challenge: | Existing methods for word-level segmentation (CWS) for the Chinese language have been successful in large-scale annotated corpora. |
| Approach: | They propose a method that integrates different segmentation criteria into one model . they use a transfer learning method to improve the performance of OOV words . |
| Outcome: | The proposed method achieves state-of-the-art performance on multiple benchmark datasets . it shows a competitive practicability and generalization ability for the CWS task . |
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition (N19-1)
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| Challenge: | Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. |
| Approach: | They propose to use a Chinese Named Entity Recognition (NER) model that uses a character-based convolutional neural network and a gated recurrent unit to capture the information from adjacent characters and sentence contexts. |
| Outcome: | The proposed model outperforms existing models on Weibo, MSRA and Chinese Resume datasets. |
Federated Chinese Word Segmentation with Global Character Associations (2021.findings-acl)
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| Challenge: | Chinese word segmentation (CWS) is a fundamental task for natural language processing. |
| Approach: | They propose a neural model for Chinese word segmentation with federated learning to help CWS deal with data isolation. |
| Outcome: | The proposed model outperforms baselines on a simulated environment with five nodes. |
Enhancing Chinese Word Segmentation via Pseudo Labels for Practicability (2021.findings-acl)
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| Challenge: | Pre-trained language models alleviate segmentation ambiguity and out-of-vocabulary (OOV) words. |
| Approach: | They propose a semisupervised neural method which distills knowledge from unlabeled data to a student model to improve both in-domain and out-of-domain CWS. |
| Outcome: | The proposed method can keep practicability of the lightweight student model and improve segmentation effectively on downstream Chinese NLP tasks. |
Pre-training with Meta Learning for Chinese Word Segmentation (2021.naacl-main)
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| Challenge: | Recent studies show that pre-trained models are beneficial to Chinese Word Segmentation (CWS). However, these models lack task-specific prior segmentation knowledge. |
| Approach: | They propose a pre-trained Chinese word segmentation model MetaSeg which incorporates meta learning into a multi-criteria pre-training task. |
| Outcome: | Empirical results show that MetaSeg can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings. |
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. |
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. |
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. |
Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation (2020.acl-main)
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| Challenge: | Fully supervised neural approaches have achieved significant progress in the task of Chinese word segmentation (CWS) however, they suffer from the cross-domain issue when they come to processing of out-of-domain data. |
| Approach: | They propose to use Chinese word as a target domain for distant annotation and adversarial training to reduce noise and maximize utilization of the source domain information. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods on real-world datasets and significantly outperformed previous state- of-the art methods. |
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. |
Improving Chinese Word Segmentation with Wordhood Memory Networks (2020.acl-main)
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| Challenge: | Contextual features are important in Chinese word segmentation (CWS) but it is difficult to integrate wordhood information into existing neural models. |
| Approach: | They propose a neural framework that integrates contextual wordhood information with several popular encoder-decoder combinations for Chinese word segmentation. |
| Outcome: | The proposed framework achieves state-of-the-art performance on five benchmark datasets. |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge (2020.acl-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging are important fundamental tasks in natural language processing. |
| Approach: | They propose a neural model for Chinese word segmentation and part-of-speech tagging . they incorporate context features and syntactic knowledge for each input character . |
| Outcome: | The proposed model can learn and benefit from existing tools, but its quality may be poor. |
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN (2023.findings-acl)
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| Challenge: | Existing sequence labeling algorithms can be decomposed into two parts . |
| Approach: | They propose a graph neural networks sequence labeling (GNN-SL) that augments the vanilla SL model output with similar tagging examples retrieved from the whole training set. |
| Outcome: | The proposed model performs well on three sequence labeling tasks. |
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. |