Classifier Based Machine Comprehension
نویسندگان
چکیده
In this report we implement a machine comprehension system, and train and test it on the MCTest dataset (Richardson et al., 2013). We treat it as a classification problem. We use baseline features and syntactic features to compute the score for each candidate answers. We also used a set of NLP techniques, including word embedding, coreference resolution and lemmatization to improve the performance of our features. We train our system using a max-margin loss function with a latent variable. Our result shows significant improvement over the original baseline.
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