Machine Comprehension using Rich Semantic Representations
Mrinmaya Sachan, Eric P. Xing · 2016
Machine comprehension tests the system's ability to understand a piece of text through a reading comprehension task.For this task, we propose an approach using the Abstract Meaning Representation (AMR) formalism.We construct meaning representation graphs for the given text and for each question-answer pair by merging the AMRs of comprising sentences using cross-sentential phenomena such as coreference and rhetorical structures.Then, we reduce machine comprehension to a graph containment problem.We posit that there is a latent mapping of the question-answer meaning representation graph onto the text meaning representation graph that explains the answer.We present a unified max-margin framework that learns to find this mapping (given a corpus of texts and question-answer pairs), and uses what it learns to answer questions on novel texts.We show that this approach leads to state of the art results on the task.