We present InferBert, a method to enhance transformer-based inference models with relevant relational knowledge. Our approach facilitates learning generic inference patterns requiring relational knowledge (e.g. inferences related to hypernymy) during training, while injecting on-demand the relevant relational facts (e.g. pangolin is an animal) at test time. We apply InferBERT to the NLI task over a diverse set of inference types (hypernymy, location, color, and country of origin), for which we collected challenge datasets. In this setting, InferBert succeeds to learn general inference patterns, from a relatively small number of training instances, while not hurting performance on the original NLI data and substantially outperforming prior knowledge enhancement models on the challenge data. It further applies its inferences successfully at test time to previously unobserved entities. InferBert is computationally more efficient than most prior methods, in terms of number of parameters, memory consumption and training time.
|Title of host publication||*SEM 2021 - 10th Conference on Lexical and Computational Semantics, Proceedings of the Conference|
|Editors||Lun-Wei Ku, Vivi Nastase, Ivan Vulic|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||10|
|State||Published - 2021|
|Event||10th Conference on Lexical and Computational Semantics, *SEM 2021 - Virtual, Bangkok, Thailand|
Duration: 5 Aug 2021 → 6 Aug 2021
|Name||*SEM 2021 - 10th Conference on Lexical and Computational Semantics, Proceedings of the Conference|
|Conference||10th Conference on Lexical and Computational Semantics, *SEM 2021|
|Period||5/08/21 → 6/08/21|
Bibliographical noteFunding Information:
The work described herein was supported in part by grants from Intel Labs, Facebook, the Israel Science Foundation grant 1951/17, the Israeli Ministry of Science and Technology and the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1).
© 2021 Lexical and Computational Semantics