Papers with Chinese characters
The Impact of Visual Information in Chinese Characters: Evaluating Large Models’ Ability to Recognize and Utilize Radicals (2025.naacl-long)
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| Challenge: | Existing studies have examined whether large language models and vision-language models can harness these sub-character features in Chinese through prompting. |
| Approach: | They establish a benchmark to evaluate large language models' understanding of visual elements in Chinese characters, including radicals, composition structures, strokes, and stroke counts. |
| Outcome: | The proposed model exhibits some, but still limited, knowledge of the visual elements in Chinese characters regardless of whether images of characters are provided. |
Chinese Idiom Paraphrasing (2023.tacl-1)
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| Challenge: | Chinese idioms are hard to understand by children and non-native speakers due to their non-compositionality and metaphorical meaning. |
| Approach: | They propose a task to rephrase idiom-containing sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning. |
| Outcome: | The proposed method has better performance than baselines based on the established dataset. |
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking (2021.findings-acl)
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| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for usergenerated text in Chinese. |
| Approach: | They propose a Chinese spell checker that leverages multimodal Chinese characters' information to predict the correct output. |
| Outcome: | The proposed model outperforms strong baselines on the SIGHAN benchmarks by a large margin. |
MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition (2021.acl-long)
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| Challenge: | Named Entity Recognition (NER) is a sequence tagging task that extracts named entities from unstructured text. |
| Approach: | They propose to integrate Chinese character features with radical-level embedding to improve Chinese NER by integrating Chinese character information. |
| Outcome: | The proposed method can improve Chinese Named Entity Recognition (NER) on well-known datasets. |
MYCanCor: A Video Corpus of spoken Malaysian Cantonese (L18-1)
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| Challenge: | The corpus consists of 20 hours of video recordings of spontaneous talk-in-interaction typically involving 2-4 speakers. |
| Approach: | the corpus consists of 20 hours of video recordings of spontaneous talk-in-interaction typically involving 2-4 speakers. |
| Outcome: | the corpus consists of 20 hours of video recordings of spontaneous talk-in-interaction typically involving 2-4 speakers. |
Exploring and Adapting Chinese GPT to Pinyin Input Method (2022.acl-long)
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| Challenge: | a frozen GPT can generate state-of-the-art performance on perfect pinyin, but performance drops when input includes abbreviated pinyan, which links to even larger number of Chinese characters. |
| Approach: | They propose to use Chinese GPT to generate fluent sentences using abbreviated pinyin. |
| Outcome: | The proposed approach improves on abbreviated pinyin across all domains. |
HUE: Pretrained Model and Dataset for Understanding Hanja Documents of Ancient Korea (2022.findings-naacl)
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| Challenge: | Historical records in Korea before the 20th century were primarily written in Hanja, an extinct language based on Chinese characters. |
| Approach: | They present a dataset that includes tasks for attribution, topic classification, named entity recognition and summary retrieval for classical Hanja documents. |
| Outcome: | The proposed models improve on the Annals of the Joseon Dynasty and Diaries of the Royal Secretariats datasets. |
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information (2021.acl-long)
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| 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. |
ZiNet: Linking Chinese Characters Spanning Three Thousand Years (2022.findings-acl)
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| Challenge: | tens of thousands of ancient characters must be deciphered by experts to interpret unearthed documents. |
| Approach: | They propose a diachronic Chinese knowledge base to help researchers discover glyph similar characters by measuring glyph similarities between ancient Chinese characters. |
| Outcome: | The proposed method shows strong correlations between the scores obtained from the method and from human experts. |
PTCSpell: Pre-trained Corrector Based on Character Shape and Pinyin for Chinese Spelling Correction (2023.findings-acl)
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| Challenge: | Chinese spelling correction (CSC) is a task which detects incorrect characters in Chinese text and corrects them. |
| Approach: | They propose to pre-train a Chinese spelling correction corrector under the detector-corrector architecture and propose to capture pronunciation and shape information in Chinese characters. |
| Outcome: | The proposed corrector achieves an average of 5.8% F1 improvements over state-of-the-art methods, verifying its effectiveness. |
Learning to Pronounce Chinese Without a Pronunciation Dictionary (2020.emnlp-main)
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| Challenge: | EM method achieves a test-set accuracy of 71%, vector-based method achieve 81%. |
| Approach: | They propose a program that learns to pronounce Chinese text in Mandarin without a pronunciation dictionary. |
| Outcome: | The proposed program deciphers Chinese text in Mandarin without a pronunciation dictionary. |
PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check (2021.acl-long)
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| Challenge: | False gram and phonological errors make Chinese spelling check difficult . a novel end-to-end trainable model outperforms existing methods . |
| Approach: | They propose a trainable Chinese spelling check model that integrates phonological and visual information into a pre-trained language model. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on three benchmarks. |
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation (D19-1)
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| Challenge: | Currently, Chinese characters share glyph and phonetic variations to escape detection algorithms due to their complexity and complexity. |
| Approach: | They propose a Chinese variation-enhanced Graph Embedding algorithm that can learn Chinese character embeddings and latent variation families. |
| Outcome: | The proposed model outperforms state-of-the-art models on Chinese spam detection datasets and review datasets. |
Quantifying Character Similarity with Vision Transformers (2023.emnlp-main)
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| Challenge: | Off-the-shelf string matching methods are widely used to link entities across datasets, but they are not available for many settings. |
| Approach: | They propose to use augmented digital fonts to measure character substitution costs for OCR’ed documents by using vision transformers. |
| Outcome: | The proposed method significantly improves record linkage compared to other string matching methods. |
Chinese Inertial GAN for Handwriting Signal Generation and Recognition (2025.acl-long)
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| Challenge: | Inertial sensors can measure the acceleration and angular velocity of moving objects and are widely used in electronic devices such as smartphones, smartwatches, and fitness bands. |
| Approach: | They propose to use Chinese glyph encoding, forced optimal transport, and semantic relevance alignment to acquire unlimited training samples for Chinese inertial writing recognition. |
| Outcome: | The proposed system improves the performance of six widely used classifiers from 6.7% to 98.4%. |