| Challenge: | Traditional Chinese character conversion is a common step in Chinese NLP but current methods do not take into account that a simplified Chinese character can correspond to multiple traditional characters. |
| Approach: | They propose a model that can disambiguate between mappings and convert between the two scripts by using subword segmentation and two language models. |
| Outcome: | The proposed model outperforms previous Chinese Character conversion approaches by 6 points in accuracy. |
Similar Papers
Sub-Character Tokenization for Chinese Pretrained Language Models (2023.tacl-1)
Copied to clipboard
Chenglei Si, Zhengyan Zhang, Yingfa Chen, Fanchao Qi, Xiaozhi Wang, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun
| Challenge: | Existing tokenization methods for Chinese PLMs treat each character as an indivisible token, but ignore the unique feature of the writing system where additional linguistic information exists below the character level. |
| Approach: | They propose to encode Chinese characters into short sequences and construct Chinese vocabulary based on the encoded text. |
| Outcome: | The proposed tokenizers can tokenize inputs into much shorter sequences, improving computational efficiency. |
Subword-Delimited Downsampling for Better Character-Level Translation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Subword-level models are expensive in terms of time and computation, but character-level model with downsampling component can be used for machine translation. |
| Approach: | They propose a character-level downsampling method which is informed by subwords to improve model performance. |
| Outcome: | The proposed method outperforms existing methods and shows that it can be done without sacrificing quality. |
Multiple Character Embeddings for Chinese Word Segmentation (P19-2)
Copied to clipboard
| 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. |
Neural Machine Translation without Embeddings (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing models operate over subword tokens, but byte-based models employ a different approach . a one-hot representation of each byte does not hurt performance, but it improves BLEU scores . |
| Approach: | They propose to represent every computerized text as a sequence of bytes via UTF-8 . this eliminates the need for an embedding layer and improves performance . |
| Outcome: | The proposed model improves BLEU scores on byte-to-byte translation models compared to character-level models . the proposed model does not require an embedding layer and does not drop out of the decoder . |
Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models (D18-1)
Copied to clipboard
| Challenge: | Recent advances in text normalization have limited applications in other languages . a novel approach to text normalizing uses character embeddings and word embedds . |
| Approach: | They propose a sequence-to-sequence model with character-based attention that uses pre-trained word embeddings to model subword information. |
| Outcome: | The proposed model achieves state-of-the-art F1 score on Arabic spelling correction task despite being small and unsuited for the task. |
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification (2021.eacl-main)
Copied to clipboard
| Challenge: | Semi-supervised learning and multilingual pretraining have been shown to be effective for task-specific labelled data shortages. |
| Approach: | They propose to combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. |
| Outcome: | The proposed method outperforms state-of-the-art models in low-resource settings across several languages and outperformed existing models in English. |
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information (2021.acl-long)
Copied to clipboard
| Challenge: | ChineseBERT model incorporates glyph and pinyin information of Chinese characters into pretraining . proposed model achieves new performance boost over baseline models with fewer training steps . |
| Approach: | They propose a ChineseBERT model that incorporates glyph and pinyin information into pretraining . the glyph embedding is obtained based on different fonts of a character, and the pinyink embeddment characterizes the pronunciation of Chinese characters. |
| Outcome: | The proposed model achieves new performance boosts over baseline models with fewer training steps. |
Subword Encoding in Lattice LSTM for Chinese Word Segmentation (N19-1)
Copied to clipboard
| 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. |
Enhancing Pre-trained Chinese Character Representation with Word-aligned Attention (2020.acl-main)
Copied to clipboard
| 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. |
CWSeg: An Efficient and General Approach to Chinese Word Segmentation (2023.acl-industry)
Copied to clipboard
| 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. |