Non-Autoregressive Sentence Ordering

Yi Bin, Wenhao Shi, Bin Ji, Jipeng Zhang, Yujuan Ding, Yang Yang

Abstract
Existing sentence ordering approaches generally employ encoder-decoder frameworks with the pointer net to recover the coherence by recurrently predicting each sentence step-by-step. Such an autoregressive manner only leverages unilateral dependencies during decoding and cannot fully explore the semantic dependency between sentences for ordering. To overcome these limitations, in this paper, we propose a novel Non-Autoregressive Ordering Network, dubbed NAON, which explores bilateral dependencies between sentences and predicts the sentence for each position in parallel. We claim that the non-autoregressive manner is not just applicable but also particularly suitable to the sentence ordering task because of two peculiar characteristics of the task: 1) each generation target is in deterministic length, and 2) the sentences and positions should match exclusively. Furthermore, to address the repetition issue of the naive non-autoregressive Transformer, we introduce an exclusive loss to constrain the exclusiveness between positions and sentences. To verify the effectiveness of the proposed model, we conduct extensive experiments on several common-used datasets and the experimental results show that our method outperforms all the autoregressive approaches and yields competitive performance compared with the state-of-the-arts. The codes are available at: https://github.com/steven640pixel/nonautoregressive-sentence-ordering.
Anthology ID:
2023.findings-emnlp.277
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4198–4214
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.277
DOI:
10.18653/v1/2023.findings-emnlp.277
Bibkey:
Cite (ACL):
Yi Bin, Wenhao Shi, Bin Ji, Jipeng Zhang, Yujuan Ding, and Yang Yang. 2023. Non-Autoregressive Sentence Ordering. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4198–4214, Singapore. Association for Computational Linguistics.
Cite (Informal):
Non-Autoregressive Sentence Ordering (Bin et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/nschneid-patch-3/2023.findings-emnlp.277.pdf
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