Papers by Edward Lin
GEM: A General Evaluation Benchmark for Multimodal Tasks (2021.findings-acl)
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Lin Su, Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti
| Challenge: | Existing datasets that focus on natural language tasks are not considered as a general evaluation benchmark for multimodal tasks. |
| Approach: | They present a general evaluation benchmark for multimodal tasks, GEM 1 . they compare it with existing multimodal vision-language datasets . |
| Outcome: | The proposed model is compared with existing vision-language datasets focusing on natural language tasks . it is the largest vision-linguistic dataset covering image-language tasks and video-language task at the same time . |
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)
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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu
| Challenge: | Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence. |
| Approach: | They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy. |
| Outcome: | The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%. |
LayerNorm Induces Recency Bias in Transformer Decoders (2026.findings-acl)
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| Challenge: | Existing studies show that stacking causal self-attention layers alone induces a positional bias in attention scores toward earlier tokens, but this differs from the bias toward later tokens observed in Transformer decoders, known as recency bias. |
| Approach: | They propose to stack causal self-attention layers and layer norm to induce recency bias in Transformer decoders by analyzing the interaction between causal self and other architectural components. |
| Outcome: | The proposed method provides new theoretical insights into how positional information interacts with architectural components and suggests improvements in positional encoding strategies. |
Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction (2022.emnlp-main)
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| Challenge: | Text error correction methods usually use the source (incorrect) sentence as encoder input and generate the target (correct) sentences through the decoder. |
| Approach: | They propose a method to correct errors in text sequences by randomly masking out the correct tokens in the source sentence. |
| Outcome: | The proposed method improves accuracy on Mandarin and English datasets with autoregressive and non-autoregressive generation models. |