Papers by Zhangyin Feng

5 papers
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.
Improved Visual Story Generation with Adaptive Context Modeling (2023.findings-acl)

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Challenge: a recent work shows that diffusion models generate images of high resolution and semantic consistency to text prompts.
Approach: They propose a method that uses adaptive context modeling to improve leading system . they evaluate their method on pororoSV and FlintstonesSV datasets .
Outcome: The proposed method achieves state-of-the-art FID scores on pororo and Flintstones datasets.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)

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Challenge: Large pre-trained models have improved performance on a variety of natural language processing tasks.
Approach: They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators.
Outcome: The proposed model performs better on two NL-PL applications by fine-tuning model parameters.
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network (2020.acl-main)

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Challenge: Existing methods for fact checking textual statements are not yet available.
Approach: They propose a neural network approach capable of leveraging logical operations for fact checking . they use a textual statement and semi-structured tables to generate a program from it .
Outcome: The proposed approach achieves state-of-the-art performance on TABFACT dataset . it derives a program (a.k.a. logical form) of the statement in semantic parsing manner .
“Is Whole Word Masking Always Better for Chinese BERT?”: Probing on Chinese Grammatical Error Correction (2022.findings-acl)

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Challenge: a Chinese model with whole word masking has no subword because each token is an atomic character.
Approach: They propose to use whole word masking to mask all subwords corresponding to a word at once . they ask models to revise or insert tokens in a masked language modeling manner .
Outcome: The proposed model performs better when one character is inserted or replaced . the model trained with standard character-level masking performs best when one token is masked .

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