Papers › Classical Structured Prediction Losses for Sequence to Sequence Learning

Classical Structured Prediction Losses for Sequence to Sequence Learning

14 Nov 2017NAACL 2018 6arXiv:1711.04956archive 2025-07-28

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

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.

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Tasks

Abstractive Text Summarization Machine Translation Prediction Reinforcement Learning Reinforcement Learning (RL)Structured Prediction Translation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Machine Translation	IWSLT2014 German-English	Minimum Risk Training [Edunov2017]	BLEU score	32.84	#30 of 34	Archive leaderboard	report
Machine Translation	IWSLT2015 German-English	ConvS2S+Risk	BLEU score	32.93	#4 of 15	Archive leaderboard	report

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