SQuAD Reading Comprehension with Coattention
نویسنده
چکیده
Reading comprehension is an important task in NLP, which involves teaching a machine to understand text enough to answer questions. The Stanford Question Answering Dataset (SQuAD) is a dataset consisting of 100,000 question-context-answer datapoints. Here, deep learning methods are used to answer questions based on context data. A model based on the Attentive Reader [1,2] model is used as a baseline, with elements of a Dynamic Coattention Network [3] applied. Co-dependent attention representations that combine the individual representations of the question and context paragraph are implemented. The model is evaluated using F1 and exact match (EM) scores.
منابع مشابه
SQuAD reading comprehension deep learning model
We introduce a neural network model for reading comprehension using the SQuAD dataset. Our model is composed of a Dynamic Coattention Network encoder (Xiong et al. [2016]) and a novel decoder designed for runtime minimization. Our model obtained an F1 score of 52.283 when tested on the SQuAD dev set, and an exact-match score of 38.723.
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