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Article overview
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Trainable Greedy Decoding for Neural Machine Translation | Jiatao Gu
; Kyunghyun Cho
; Victor O.K. Li
; | Date: |
8 Feb 2017 | Abstract: | Recent research in neural machine translation has largely focused on two
aspects; neural network architectures and end-to-end learning algorithms. The
problem of decoding, however, has received relatively little attention from the
research community. In this paper, we solely focus on the problem of decoding
given a trained neural machine translation model. Instead of trying to build a
new decoding algorithm for any specific decoding objective, we propose the idea
of trainable decoding algorithm in which we train a decoding algorithm to find
a translation that maximizes an arbitrary decoding objective. More
specifically, we design an actor that observes and manipulates the hidden state
of the neural machine translation decoder and propose to train it using a
variant of deterministic policy gradient. We extensively evaluate the proposed
algorithm using four language pairs and two decoding objectives and show that
we can indeed train a trainable greedy decoder that generates a better
translation (in terms of a target decoding objective) with minimal
computational overhead. | Source: | arXiv, 1702.2429 | Services: | Forum | Review | PDF | Favorites |
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