Papers by Hongjia Li
Structural Supervision for Word Alignment and Machine Translation (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge on syntactic structure neglects the rich structural information from target tokens and the structural similarity between the source and target sentences. |
| Approach: | They propose to incorporate syntactic structure of both source and target tokens into the encoder-decoder framework, tightly correlating the internal logic of word alignment and machine translation for multi-task learning. |
| Outcome: | The proposed method outperforms baselines on four publicly available language pairs and consistently outperformed baselines in alignment accuracy and translation quality. |
LET: Leveraging Error Type Information for Grammatical Error Correction (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for grammatical error correction (GEC) are mainly divided into detection-based and end-to-end generative models. |
| Approach: | They propose an end-to-end framework which Leverages Error Type (LET) information in the generation process to introduce more convincing error type information. |
| Outcome: | The proposed framework outperforms existing methods on various datasets by a clear margin. |