Creating and sharing knowledge for telecommunications

Smoothing and Shrinking the Sparse Seq2Seq Search Space

Peters, B. ; Martins, A.

Smoothing and Shrinking the Sparse Seq2Seq Search Space, Proc Association for Computational Linguistics Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies NAACL-HLT, Conference Online, Vol. , pp. - , June, 2021.

Digital Object Identifier: 10.18653/v1/2021.naacl-main.210

Download Full text PDF ( 499 KBs)

Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences. While this setup has led to strong results in a variety of tasks, one unsatisfying aspect is its length bias: models give high scores to short, inadequate hypotheses and often make the empty string the argmax—the so-called cat got your tongue problem. Recently proposed entmax-based sparse sequence-to-sequence models present a possible solution, since they can shrink the search space by assigning zero probability to bad hypotheses, but their ability to handle word-level tasks with transformers has never been tested. In this work, we show that entmax-based models effectively solve the cat got your tongue problem, removing a major source of model error for neural machine translation. In addition, we generalize label smoothing, a critical regularization technique, to the broader family of Fenchel-Young losses, which includes both cross-entropy and the entmax losses. Our resulting label-smoothed entmax loss models set a new state of the art on multilingual grapheme-to-phoneme conversion and deliver improvements and better calibration properties on cross-lingual morphological inflection and machine translation for 7 language pairs.