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• W2972025845 abstract "Task-specific scores are often used to optimize for and evaluate the performance of conditional text generation systems. However, such scores are non-differentiable and cannot be used in the standard supervised learning paradigm. Hence, policy gradient methods are used since the gradient can be computed without requiring a differentiable objective. However, we argue that current n-gram overlap based measures that are used as rewards can be improved by using model-based rewards transferred from tasks that directly compare the similarity of sentence pairs. These reward models either output a score of sentence-level syntactic and semantic similarity between entire predicted and target sentences as the expected return, or for intermediate phrases as segmented accumulative rewards. We demonstrate that using a textit{Transferable Reward Learner} leads to improved results on semantical evaluation measures in policy-gradient models for image captioning tasks. Our InferSent actor-critic model improves over a BLEU trained actor-critic model on MSCOCO when evaluated on a Word Mover's Distance similarity measure by 6.97 points, also improving on a Sliding Window Cosine Similarity measure by 10.48 points. Similar performance improvements are also obtained on the smaller Flickr-30k dataset, demonstrating the general applicability of the proposed transfer learning method." @default.
• W2972025845 created "2019-09-12" @default.
• W2972025845 creator A5073503574 @default.
• W2972025845 creator A5083742715 @default.
• W2972025845 date "2019-09-09" @default.
• W2972025845 modified "2023-10-17" @default.
• W2972025845 title "Transfer Reward Learning for Policy Gradient-Based Text Generation." @default.
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• W2972025845 hasPublicationYear "2019" @default.
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