Papers with multi-task method

2 papers
MTRec: Multi-Task Learning over BERT for News Recommendation (2022.findings-acl)

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Challenge: Existing news recommendation methods learn news representations solely based on news titles. Existing methods only utilize title information and neglect other valuable news information such as categories and entities.
Approach: They propose a multi-task method to incorporate multi-field information into BERT, which improves its news encoding capability.
Outcome: Extensive experiments on the MIND news recommendation benchmark show the proposed method is effective.
Jointly Learning to Repair Code and Generate Commit Message (2021.emnlp-main)

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Challenge: Existing work performs code repair and commit message generation independently.
Approach: They propose a cascaded method to repair program codes and generate commit messages in a unified framework.
Outcome: The proposed model significantly outperforms baselines on a buggy-fixed-commit dataset.

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