IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension
Prakhar Sharma, Sumegh Roychowdhury · 2019
In this paper, we describe our system for COIN 2019 Shared Task 1: Commonsense Inference in Everyday Narrations Ostermann et al. (2019).We show the power of leveraging state-of-the-art pre-trained language models such as BERT (Bidirectional Encoder Representations from Transformers) Devlin et al. (2018) and XLNet Yang et al. (2019) over other Commonsense Knowledge Base Resources such as ConceptNet Speer et al. (2018) and NELL Mitchell et al. (2015) for modeling machine comprehension.We used an ensemble of BERT Large and XLNet Large .Experimental results show that our model gives substantial improvements over the baseline and other systems incorporating knowledge bases and got the 2nd position on the final test set leaderboard with an accuracy of 90.5%.