Computer Science > Computation and Language
[Submitted on 23 Jan 2017 (v1), last revised 3 Feb 2017 (this version, v2)]
Title:Learning to Decode for Future Success
View PDFAbstract:We introduce a simple, general strategy to manipulate the behavior of a neural decoder that enables it to generate outputs that have specific properties of interest (e.g., sequences of a pre-specified length). The model can be thought of as a simple version of the actor-critic model that uses an interpolation of the actor (the MLE-based token generation policy) and the critic (a value function that estimates the future values of the desired property) for decision making. We demonstrate that the approach is able to incorporate a variety of properties that cannot be handled by standard neural sequence decoders, such as sequence length and backward probability (probability of sources given targets), in addition to yielding consistent improvements in abstractive summarization and machine translation when the property to be optimized is BLEU or ROUGE scores.
Submission history
From: Jiwei Li [view email][v1] Mon, 23 Jan 2017 18:36:37 UTC (378 KB)
[v2] Fri, 3 Feb 2017 21:11:31 UTC (378 KB)
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