arXiv:1806.04357
공식 코드 · Papers with Code
Multi-Task Neural Models for Translating Between Styles Within and Across Languages
원문 초록
Generating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer and formality-sensitive machine translation. We propose to solve these tasks jointly using multi-task learning, and show that our models achieve state-of-the-art performance for formality transfer and are able to perform formality-sensitive translation without being explicitly trained on style-annotated translation examples.
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TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS
CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation
Gender Bias in English-to-Greek Machine Translation
TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration
Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations
Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation
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