ConjNLI: Natural Language Inference Over Conjunctive Sentences

Swarnadeep Saha, Yixin Nie, Mohit Bansal


Abstract
Reasoning about conjuncts in conjunctive sentences is important for a deeper understanding of conjunctions in English and also how their usages and semantics differ from conjunctive and disjunctive boolean logic. Existing NLI stress tests do not consider non-boolean usages of conjunctions and use templates for testing such model knowledge. Hence, we introduce ConjNLI, a challenge stress-test for natural language inference over conjunctive sentences, where the premise differs from the hypothesis by conjuncts removed, added, or replaced. These sentences contain single and multiple instances of coordinating conjunctions (“and”, “or”, “but”, “nor”) with quantifiers, negations, and requiring diverse boolean and non-boolean inferences over conjuncts. We find that large-scale pre-trained language models like RoBERTa do not understand conjunctive semantics well and resort to shallow heuristics to make inferences over such sentences. As some initial solutions, we first present an iterative adversarial fine-tuning method that uses synthetically created training data based on boolean and non-boolean heuristics. We also propose a direct model advancement by making RoBERTa aware of predicate semantic roles. While we observe some performance gains, ConjNLI is still challenging for current methods, thus encouraging interesting future work for better understanding of conjunctions.
Anthology ID:
2020.emnlp-main.661
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8240–8252
Language:
URL:
https://aclanthology.org/2020.emnlp-main.661
DOI:
10.18653/v1/2020.emnlp-main.661
Bibkey:
Cite (ACL):
Swarnadeep Saha, Yixin Nie, and Mohit Bansal. 2020. ConjNLI: Natural Language Inference Over Conjunctive Sentences. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8240–8252, Online. Association for Computational Linguistics.
Cite (Informal):
ConjNLI: Natural Language Inference Over Conjunctive Sentences (Saha et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.661.pdf
Video:
 https://slideslive.com/38939112
Code
 swarnaHub/ConjNLI
Data
SNLI