Understanding open-domain text is one of the primary challenges in NLP. Machine comprehension evaluates the system\u2019s ability to understand text through a series of question-answering tasks on short pieces of text such that the correct answer can be found only in the given text. For this task, we posit that there is a hidden (latent) structure that explains the relation between the question, correct answer, and text. We call this the answer-entailing structure; given the structure, the correctness of the answer is evident. Since the structure is latent, it must be inferred. We present a unified max-margin framework that learns to find these hidden structures (given a corpus of question-answer pairs), and uses what it learns to answer machine comprehension questions on novel texts. We extend this framework to incorporate multi-task learning on the different subtasks that are required to perform machine comprehension. Evaluation on a publicly available dataset shows that our framework outperforms various IR and neuralnetwork baselines, achieving an overall accuracy of 67.8% (vs. 59.9%, the best previously-published result.)<\/p>\n","protected":false},"excerpt":{"rendered":"
Understanding open-domain text is one of the primary challenges in NLP. Machine comprehension evaluates the system\u2019s ability to understand text through a series of question-answering tasks on short pieces of text such that the correct answer can be found only in the given text. 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