@inproceedings{liang-etal-2019-learning,
title = "Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization",
author = "Liang, Paul Pu and
Liu, Zhun and
Tsai, Yao-Hung Hubert and
Zhao, Qibin and
Salakhutdinov, Ruslan and
Morency, Louis-Philippe",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1152",
doi = "10.18653/v1/P19-1152",
pages = "1569--1576",
abstract = "There has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition. However, naturally occurring multimodal data is often imperfect as a result of imperfect modalities, missing entries or noise corruption. To address these concerns, we present a regularization method based on tensor rank minimization. Our method is based on the observation that high-dimensional multimodal time series data often exhibit correlations across time and modalities which leads to low-rank tensor representations. However, the presence of noise or incomplete values breaks these correlations and results in tensor representations of higher rank. We design a model to learn such tensor representations and effectively regularize their rank. Experiments on multimodal language data show that our model achieves good results across various levels of imperfection.",
}
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%0 Conference Proceedings
%T Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization
%A Liang, Paul Pu
%A Liu, Zhun
%A Tsai, Yao-Hung Hubert
%A Zhao, Qibin
%A Salakhutdinov, Ruslan
%A Morency, Louis-Philippe
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F liang-etal-2019-learning
%X There has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition. However, naturally occurring multimodal data is often imperfect as a result of imperfect modalities, missing entries or noise corruption. To address these concerns, we present a regularization method based on tensor rank minimization. Our method is based on the observation that high-dimensional multimodal time series data often exhibit correlations across time and modalities which leads to low-rank tensor representations. However, the presence of noise or incomplete values breaks these correlations and results in tensor representations of higher rank. We design a model to learn such tensor representations and effectively regularize their rank. Experiments on multimodal language data show that our model achieves good results across various levels of imperfection.
%R 10.18653/v1/P19-1152
%U https://aclanthology.org/P19-1152
%U https://doi.org/10.18653/v1/P19-1152
%P 1569-1576
Markdown (Informal)
[Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization](https://aclanthology.org/P19-1152) (Liang et al., ACL 2019)
ACL