( Image credit: SQuAD )
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After identifying KGs for each question, we examine the skew in the distribution of the number of questions for each KG.
While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering itself.
Open Domain Question Answering requires systems to retrieve external knowledge and perform multi-hop reasoning by composing knowledge spread over multiple sentences.
Neural approaches to natural language processing (NLP) often fail at the logical reasoning needed for deeper language understanding.
We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i. e., the question-answer pairs.
Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference.