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Beyond Parallel Data: Joint Word Alignment and Decipherment Improves Machine Translation.

, , and . EMNLP, page 557-565. ACL, (2014)

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Music Transformer, , , , , , , , , and . (2018)cite arxiv:1809.04281Comment: Improved skewing section and accompanying figures. Previous titles are Än Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation" and "Music Transformer".Models and Training for Unsupervised Preposition Sense Disambiguation., , , , and . ACL (2), page 323-328. The Association for Computer Linguistics, (2011)Efficient Content-Based Sparse Attention with Routing Transformers., , , and . CoRR, (2020)Aligning context-based statistical models of language with brain activity during reading., , , and . EMNLP, page 233-243. ACL, (2014)Radiobot-CFF: a spoken dialogue system for military training., , , , , , and . INTERSPEECH, ISCA, (2006)Decoding with Large-Scale Neural Language Models Improves Translation., , , and . EMNLP, page 1387-1392. ACL, (2013)Scaling Local Self-Attention for Parameter Efficient Visual Backbones., , , , , and . CVPR, page 12894-12904. Computer Vision Foundation / IEEE, (2021)Scale Efficiently: Insights from Pretraining and Finetuning Transformers., , , , , , , , , and . ICLR, OpenReview.net, (2022)Smaller Alignment Models for Better Translations: Unsupervised Word Alignment with the l0-norm., , and . ACL (1), page 311-319. The Association for Computer Linguistics, (2012)Rule Markov Models for Fast Tree-to-String Translation., , , and . ACL, page 856-864. The Association for Computer Linguistics, (2011)