COD3S: Diverse Generation with Discrete Semantic Signatures

Nathaniel Weir, João Sedoc, Benjamin Van Durme


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.421.pdf
Video:
 https://slideslive.com/38939341
Code
 nweir127/COD3S