ICML 2017poster65 citations
Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling
Hairong Liu, Zhenyao Zhu, Xiangang Li, Sanjeev Satheesh
Abstract
Most existing sequence labelling models rely on a fixed decomposition of a target sequence into a sequence of basic units. These methods suffer from two major drawbacks: $1$) the set of basic units is fixed, such as the set of words, characters or phonemes in speech recognition, and $2$) the decomposition of target sequences is fixed. These drawbacks usually result in sub-optimal performance of modeling sequences. In this paper, we extend the popular CTC loss criterion to alleviate these limitations, and propose a new loss function called
BibTeX
@InProceedings{pmlr-v70-liu17f,
title = {{G}ram-{CTC}: Automatic Unit Selection and Target Decomposition for Sequence Labelling},
author = {Hairong Liu and Zhenyao Zhu and Xiangang Li and Sanjeev Satheesh},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {2188--2197},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/liu17f/liu17f.pdf},
url = {https://proceedings.mlr.press/v70/liu17f.html},
abstract = {Most existing sequence labelling models rely on a fixed decomposition of a target sequence into a sequence of basic units. These methods suffer from two major drawbacks: $1$) the set of basic units is fixed, such as the set of words, characters or phonemes in speech recognition, and $2$) the decomposition of target sequences is fixed. These drawbacks usually result in sub-optimal performance of modeling sequences. In this paper, we extend the popular CTC loss criterion to alleviate these limitations, and propose a new loss function called