Papers › Sequence-to-Sequence Learning as Beam-Search Optimization

Sequence-to-Sequence Learning as Beam-Search Optimization

9 Jun 2016EMNLP 2016 11arXiv:1606.02960archive 2025-07-28

Sam Wiseman, Alexander M. Rush

Sequence-to-Sequence (seq2seq) modeling has rapidly become an important general-purpose NLP tool that has proven effective for many text-generation and sequence-labeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, next-word distributions. In this work, we introduce a model and beam-search training scheme, based on the work of Daume III and Marcu (2005), that extends seq2seq to learn global sequence scores. This structured approach avoids classical biases associated with local training and unifies the training loss with the test-time usage, while preserving the proven model architecture of seq2seq and its efficient training approach. We show that our system outperforms a highly-optimized attention-based seq2seq system and other baselines on three different sequence to sequence tasks: word ordering, parsing, and machine translation.

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accuracy sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning/utils.py community (archive-listed) ran MIT (permissive) · 53daad1d7b99fbdf · report
boxes_iou AndreiMoraru123/ContextCollector/YOLO/utils.py community (archive-listed) unverified MIT (permissive) · 0b8377535f19b59a · report
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nms AndreiMoraru123/ContextCollector/YOLO/utils.py community (archive-listed) unverified MIT (permissive) · 87488d598797633f · report
prune_model_l1_structured AndreiMoraru123/ContextCollector/pipeline.py community (archive-listed) unverified MIT (permissive) · 8d85fd25730c83cd · report
prune_model_l1_unstructured AndreiMoraru123/ContextCollector/pipeline.py community (archive-listed) unverified MIT (permissive) · 05d051969149aebe · report
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Tasks

Language ModelingLanguage ModellingMachine TranslationText GenerationTranslation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Machine Translation IWSLT2015 German-English Word-level CNN w/attn, input feeding BLEU score 24.0 #13 of 15 Archive leaderboard report

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Methods

LSTMSeq2SeqSigmoid ActivationTanh Activation

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