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Papers/Classical Structured Prediction Losses for Sequence to Seq...

Classical Structured Prediction Losses for Sequence to Sequence Learning

Sergey Edunov, Myle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato

2017-11-14NAACL 2018 6Machine TranslationStructured PredictionReinforcement LearningAbstractive Text SummarizationTranslationPrediction
PaperPDFCode(official)

Abstract

There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models. Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup. We also report new state of the art results on both IWSLT'14 German-English translation as well as Gigaword abstractive summarization. On the larger WMT'14 English-French translation task, sequence-level training achieves 41.5 BLEU which is on par with the state of the art.

Results

TaskDatasetMetricValueModel
Machine TranslationIWSLT2015 German-EnglishBLEU score32.93ConvS2S+Risk
Machine TranslationIWSLT2014 German-EnglishBLEU score32.84Minimum Risk Training [Edunov2017]

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