Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension
This addresses the challenge of improving machine comprehension for AI systems by integrating commonsense knowledge, though it is incremental as it builds on existing methods and datasets.
The paper tackled the problem of machine comprehension using commonsense knowledge by developing a system with Three-way Attentive Networks and relational embeddings from ConceptNet, achieving state-of-the-art performance with 83.95% accuracy on the test data.
This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment the input with relation embedding from the graph of general knowledge ConceptNet (Speer et al., 2017). As a result, our system achieves state-of-the-art performance with 83.95% accuracy on the official test data. Code is publicly available at https://github.com/intfloat/commonsense-rc