| Challenge: | Existing word-based model can not be trained in batches due to its DAG structure. |
| Approach: | They propose a lattice model that integrates word information into the start or end characters of a word and integrates it into a fixed-sized representation for efficient batch training. |
| Outcome: | The proposed model outperforms other state-of-the-art models on benchmark datasets and shows that it can be trained in batches without a shortcut path. |
Similar Papers
Chinese NER Using Lattice LSTM (P18-1)
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| Challenge: | Chinese named entity recognition (NER) is a fundamental task in information extraction. |
| Approach: | They propose a lattice-structured LSTM model for Chinese named entity recognition (NER) model leverages word and word sequence information to encode a sequence of input characters and all potential words that match a lexicon. |
| Outcome: | The proposed model outperforms word-based and character-based models on Chinese NER datasets. |
Subword Encoding in Lattice LSTM for Chinese Word Segmentation (N19-1)
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| Challenge: | Using subwords, Chinese word segmentation models use character combination information to disambiguate characters. |
| Approach: | They propose a subword-based neural word segmentor that integrates character embeddings into a Lattice LSTM network over a character sequence. |
| Outcome: | The proposed model can utilize abundant character combination information, which is effective to disambiguate characters. |
Simplify the Usage of Lexicon in Chinese NER (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is concerned with the identification of named entities in unstructured text. |
| Approach: | They propose a method for incorporating word lexicon into character representations . experimental results show method can be easily incorporated with pre-trained models . |
| Outcome: | The proposed method achieves 6.15 times faster inference speed and better performance on four benchmark Chinese NER datasets. |
State-of-the-art Chinese Word Segmentation with Bi-LSTMs (D18-1)
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| Challenge: | A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. |
| Approach: | They propose a bidirectional LSTM model with standard deep learning techniques and best practices for the task of Chinese word segmentation. |
| Outcome: | The proposed model outperforms models based on standard deep learning techniques and best practices on Chinese word segmentation datasets. |
FLAT: Chinese NER Using Flat-Lattice Transformer (2020.acl-main)
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| Challenge: | Named entity recognition (NER) models are difficult to use because of the complex nature of the lattice structure and the low inference speed. |
| Approach: | They propose a character-word lattice structure that converts lattics into flat structures consisting of spans. |
| Outcome: | The proposed model outperforms other lexicon-based models on four datasets and is highly parallel. |
Porous Lattice Transformer Encoder for Chinese NER (2020.coling-main)
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| Challenge: | Existing methods to integrate word boundary information into character-level Chinese NER are inefficient and lack semantic interaction. |
| Approach: | They propose an extension of transformer encoder that is tailored for ChineseNER to incorporate lexicons into character-level Chinese NER by lattices. |
| Outcome: | The proposed extension performs 11.4 times faster than state-of-the-art methods while retaining the rich long-term dependencies. |
Enhancing Pre-trained Chinese Character Representation with Word-aligned Attention (2020.acl-main)
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| Challenge: | Pre-trained Chinese language models take character as the basic unit and learn representation according to character’s external contexts, ignoring the semantics expressed in the word, which is the smallest meaningful utterance in Chinese. |
| Approach: | They propose to pool character-level attention to the word level and propose to alleviate the potential issue of segmentation error propagation by multi-source information fusion. |
| Outcome: | The proposed approach improves on five Chinese NLP benchmark tasks against BERT, ERNIE and BERT-wwm. |
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations (2020.findings-emnlp)
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| Challenge: | Experimental results show that pre-trained text encoders can perform many NLP tasks with less resource. |
| Approach: | They propose a BERT-based Chinese text encoder enhanced by n-gram representations . they show reasonable performance when ZEN is trained on a small corpus . |
| Outcome: | The proposed encoder incorporates the comprehensive information of both the character sequence and words or phrases it contains. |
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. |
Entity Enhanced BERT Pre-training for Chinese NER (2020.emnlp-main)
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| Challenge: | Character-level BERT pre-trained in Chinese suffers from lacking lexicon information, which shows effectiveness for Chinese NER. |
| Approach: | They propose a semi-supervised method to integrate lexicon into pre-trained LMs in Chinese . they extract an entity lexiconal from raw text and integrate it into BERT . |
| Outcome: | The proposed method is highly effective and achieves the best results on a news dataset and two datasets annotated by the authors. |