Adapting RNN Sequence Prediction Model to Multi-label Set Prediction

Kechen Qin, Cheng Li, Virgil Pavlu, Javed Aslam

Abstract
We present an adaptation of RNN sequence models to the problem of multi-label classification for text, where the target is a set of labels, not a sequence. Previous such RNN models define probabilities for sequences but not for sets; attempts to obtain a set probability are after-thoughts of the network design, including pre-specifying the label order, or relating the sequence probability to the set probability in ad hoc ways. Our formulation is derived from a principled notion of set probability, as the sum of probabilities of corresponding permutation sequences for the set. We provide a new training objective that maximizes this set probability, and a new prediction objective that finds the most probable set on a test document. These new objectives are theoretically appealing because they give the RNN model freedom to discover the best label order, which often is the natural one (but different among documents). We develop efficient procedures to tackle the computation difficulties involved in training and prediction. Experiments on benchmark datasets demonstrate that we outperform state-of-the-art methods for this task.
Anthology ID:
N19-1321
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3181–3190
Language:
URL:
https://aclanthology.org/N19-1321
DOI:
10.18653/v1/N19-1321
Bibkey:
Cite (ACL):
Kechen Qin, Cheng Li, Virgil Pavlu, and Javed Aslam. 2019. Adapting RNN Sequence Prediction Model to Multi-label Set Prediction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3181–3190, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Adapting RNN Sequence Prediction Model to Multi-label Set Prediction (Qin et al., NAACL 2019)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/N19-1321.pdf
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