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Calibration of Encoder Decoder Models for Neural Machine Translation

2019-03-03 01:08:47
Aviral Kumar, Sunita Sarawagi
arXiv_CL
         
Abstract

We study the calibration of several state of the art neural machine translation(NMT) systems built on attention-based encoder-decoder models. For structured outputs like in NMT, calibration is important not just for reliable confidence with predictions, but also for proper functioning of beam-search inference. We show that most modern NMT models are surprisingly miscalibrated even when conditioned on the true previous tokens. Our investigation leads to two main reasons -- severe miscalibration of EOS (end of sequence marker) and suppression of attention uncertainty. We design recalibration methods based on these signals and demonstrate improved accuracy, better sequence-level calibration, and more intuitive results from beam-search.

Abstract (translated)

URL

https://arxiv.org/abs/1903.00802

PDF

https://arxiv.org/pdf/1903.00802.pdf

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