Papers by Jessy Lin

2 papers
Automatic Correction of Human Translations (2022.naacl-main)

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Challenge: Despite recent advances in machine translation, a tremendous amount of translated content in the world is still written by humans.
Approach: They propose a task of translation error correction (TEC) that corrects human-generated translations by correcting all errors in a source sentence and a human-created translation.
Outcome: The proposed system improves translation accuracy by 5.1 points compared to MT systems with human errors .
Inferring Rewards from Language in Context (2022.acl-long)

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Challenge: a new study grounding language to reward functions extends the standard instruction following setup in this way.
Approach: They propose a model that infers rewards from language pragmatically by reasoning about how speakers choose utterances to elicit desired actions and reveal information about their preferences.
Outcome: The proposed model infers rewards from language pragmatically on a flight–booking task with natural language.

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