Papers by Baikjin Jung

3 papers
Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine Translations (2023.acl-short)

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Challenge: Existing APE systems are not good at handling high-quality MTs even for a language pair with abundant data resources, English–German.
Approach: They propose a linguistically motivated method of regularization that encourages symmetric self-attention on the given MT.
Outcome: The proposed method improves the state-of-the-art architecture’s APE quality for high-quality MTs.
Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation (2021.eacl-main)

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Challenge: Automated Post-Editing (APE) aims to correct errors in the output of a given machine translation system.
Approach: They propose two new methods of synthesizing additional MT outputs by adapting back-translation to the APE task, obtaining robust enlargements of existing synthetic APE training dataset.
Outcome: The proposed methods improve translation quality on the English-German APE task by enlarging the existing training dataset.
Denoising Table-Text Retrieval for Open-Domain Question Answering (2024.lrec-main)

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Challenge: Existing studies in table-text open-domain question answering have problems with false-positive labels in training datasets.
Approach: They propose a denoised table-text retriever that discards false positives from training datasets . they integrate table-level ranking information into the retriever to assist in finding evidence .
Outcome: The proposed method outperforms baselines on retrieval recall and QA tasks.

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