Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization

Paul Pu Liang, Zhun Liu, Yao-Hung Hubert Tsai, Qibin Zhao, Ruslan Salakhutdinov, Louis-Philippe Morency


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.
Anthology ID:
P19-1152
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1569–1576
Language:
URL:
https://aclanthology.org/P19-1152
DOI:
10.18653/v1/P19-1152
Bibkey:
Cite (ACL):
Paul Pu Liang, Zhun Liu, Yao-Hung Hubert Tsai, Qibin Zhao, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2019. Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1569–1576, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization (Liang et al., ACL 2019)
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PDF:
https://aclanthology.org/P19-1152.pdf