Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

Paloma Jeretic, Alex Warstadt, Suvrat Bhooshan, Adina Williams


Abstract
Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of 32K semi-automatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by “some” as entailments. For some presupposition triggers like “only”, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.
Anthology ID:
2020.acl-main.768
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8690–8705
Language:
URL:
https://aclanthology.org/2020.acl-main.768
DOI:
10.18653/v1/2020.acl-main.768
Bibkey:
Cite (ACL):
Paloma Jeretic, Alex Warstadt, Suvrat Bhooshan, and Adina Williams. 2020. Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8690–8705, Online. Association for Computational Linguistics.
Cite (Informal):
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition (Jeretic et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.768.pdf
Dataset:
 2020.acl-main.768.Dataset.zip
Video:
 http://slideslive.com/38929367
Code
 alexwarstadt/data_generation
Data
IMPPRESGLUEMultiNLISNLI