paper-with-me

홈 › Papers

Attention Is All You Need for Chinese Word Segmentation

2019-10-31 · EMNLP 2020 11 · Sufeng Duan, Hai Zhao

Taking greedy decoding algorithm as it should be, this work focuses on further strengthening the model itself for Chinese word segmentation (CWS), which results in an even more fast and more accurate CWS model. Our model consists of an attention only stacked encoder and a light enough decoder for the greedy segmentation plus two highway connections for smoother training, in which the encoder is composed of a newly proposed Transformer variant, Gaussian-masked Directional (GD) Transformer, and a biaffine attention scorer. With the effective encoder design, our model only needs to take unigram features for scoring. Our model is evaluated on SIGHAN Bakeoff benchmark datasets. The experimental results show that with the highest segmentation speed, the proposed model achieves new state-of-the-art or comparable performance against strong baselines in terms of strict closed test setting.

📄 PDF Abstract BibTeX arXiv:1910.14537

Code (1)

akibcmi/SAMS 공식 구현 pytorch

Tasks

AllChinese Word SegmentationDecoderSegmentation

Methods 이 논문이 사용한 방법론

Test 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…
Position-Wise Feed-Forward Layer 설명 없음
Residual Connection 설명 없음
BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…
Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…
Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…

Similar Papers 제목 키워드 기반

A New Clustering neural network for Chinese word segmentation

2020-02-18 · Yuze Zhao

In this article I proposed a new model to achieve Chinese word segmentation(CWS),which may have the potentiality to apply in other domains in the future.It is a new thinking in CWS compared to previous works,to consider …

Chinese Word SegmentationClusteringSegmentationSentence

Investigating Self-Attention Network for Chinese Word Segmentation

2019-07-26 · Leilei Gan, Yue Zhang

Neural network has become the dominant method for Chinese word segmentation. Most existing models cast the task as sequence labeling, using BiLSTM-CRF for representing the input and making output predictions. Recently, a…

Chinese Word SegmentationSegmentation

Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge

2020-07-01 · ACL 2020 6 · Yuanhe Tian, Yan Song, Xiang Ao, Fei Xia 외

Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks. Previous st…

Chinese Word SegmentationPart-Of-Speech TaggingPOSPOS Tagging

Incorporating Uncertain Segmentation Information into Chinese NER for Social Media Text

2020-04-14 · WS 2020 7 · Shengbin Jia, Ling Ding, Xiaojun Chen, Shijia E 외

Chinese word segmentation is necessary to provide word-level information for Chinese named entity recognition (NER) systems. However, segmentation error propagation is a challenge for Chinese NER while processing colloqu…

Chinese Named Entity RecognitionChinese Word Segmentationnamed-entity-recognitionNamed Entity Recognition+5

Combine CRF and MMSEG to Boost Chinese Word Segmentation in Social Media

2015-10-24 · Yao Yushi, Huang Zheng

In this paper, we propose a joint algorithm for the word segmentation on Chinese social media. Previous work mainly focus on word segmentation for plain Chinese text, in order to develop a Chinese social media processing…

Chinese Word SegmentationSegmentation