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• W2997952181 abstract "Reinforcement learning (RL) has been widely used to aid training in language generation. This is achieved by enhancing standard maximum likelihood objectives with user-specified reward functions that encourage global semantic consistency. We propose a principled approach to address the difficulties associated with RL-based solutions, namely, high-variance gradients, uninformative rewards and brittle training. By leveraging the optimal transport distance, we introduce a regularizer that significantly alleviates the above issues. Our formulation emphasizes the preservation of semantic features, enabling end-to-end training instead of ad-hoc fine-tuning, and when combined with RL, it controls the exploration space for more efficient model updates. To validate the effectiveness of the proposed solution, we perform a comprehensive evaluation covering a wide variety of NLP tasks: machine translation, abstractive text summarization and image caption, with consistent improvements over competing solutions." @default.
• W2997952181 created "2020-01-10" @default.
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• W2997952181 date "2020-04-03" @default.
• W2997952181 modified "2023-09-26" @default.
• W2997952181 title "Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning" @default.
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• W2997952181 doi "https://doi.org/10.1609/aaai.v34i05.6249" @default.
• W2997952181 hasPublicationYear "2020" @default.
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