Related papers: Efficient Wait-k Models for Simultaneous Machine Translation

Efficient Wait-k Models for Simultaneous Machine Translation

URL: http://arxiv.org/abs/2005.08595v2
Date: Tue, 4 Aug 2020 01:10:35 GMT
Title: Efficient Wait-k Models for Simultaneous Machine Translation
Authors: Maha Elbayad, Laurent Besacier, Jakob Verbeek
Abstract summary: Simultaneous machine translation consists in starting output generation before the entire input sequence is available. Wait-k decoders offer a simple but efficient approach for this problem. We investigate the behavior of wait-k decoding in low resource settings for spoken corpora using IWSLT datasets.
Score: 46.01342928010307
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Simultaneous machine translation consists in starting output generation before the entire input sequence is available. Wait-k decoders offer a simple but efficient approach for this problem. They first read k source tokens, after which they alternate between producing a target token and reading another source token. We investigate the behavior of wait-k decoding in low resource settings for spoken corpora using IWSLT datasets. We improve training of these models using unidirectional encoders, and training across multiple values of k. Experiments with Transformer and 2D-convolutional architectures show that our wait-k models generalize well across a wide range of latency levels. We also show that the 2D-convolution architecture is competitive with Transformers for simultaneous translation of spoken language.

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