Abstract
We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seqs typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply to causal generation, the task of predicting a proposition’s plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.- Anthology ID:
- 2020.emnlp-main.421
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5199–5211
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.421
- DOI:
- 10.18653/v1/2020.emnlp-main.421
- Cite (ACL):
- Nathaniel Weir, João Sedoc, and Benjamin Van Durme. 2020. COD3S: Diverse Generation with Discrete Semantic Signatures. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5199–5211, Online. Association for Computational Linguistics.
- Cite (Informal):
- COD3S: Diverse Generation with Discrete Semantic Signatures (Weir et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.emnlp-main.421.pdf
- Code
- nweir127/COD3S