HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension
This work addresses machine comprehension with commonsense knowledge for NLP researchers, but it is incremental as it builds on existing attention-based methods for a specific competition task.
The paper tackled commonsense reading comprehension by proposing a Hybrid Multi-Aspects (HMA) neural network model, which achieved state-of-the-art results with an accuracy of 84.13% on the SemEval-2018 Task 11 test set.
This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. In this paper, we present a neural network called Hybrid Multi-Aspects (HMA) model, which mimic the human's intuitions on dealing with the multiple-choice reading comprehension. In this model, we aim to produce the predictions in multiple aspects by calculating attention among the text, question and choices, and combine these results for final predictions. Experimental results show that our HMA model could give substantial improvements over the baseline system and got the first place on the final test set leaderboard with the accuracy of 84.13%.