Machine Question and Answering

Machine comprehension, an unsolved problem in machine learning, enables a machine to process a passage and answer associated questions with a high level of accuracy that matches human performance. We use the SQuAD dataset, which simplifies the question-answer process, where the answer is defined by the starting and ending indices of its location in the context paragraph. In this paper, we explore several different models with a focus on the Multi-Perspective Context Matching (MPCM) model proposed by Wang et al [7]. The MPCM model features a relevancy matrix, used to filter out words in the context that are less relevant to the question, and uses a bi-directional LSTM encoding of the question and context. The model then applies attention mechanisms defined in the paper, including full-matching, maxpooling-matching, and meanpooling-matching to derive matching vectors, which are then decoded into starting and ending indices of the answer. In addition implementing and performing ablation on the features described in the MPCM paper, we also added an additional enforcement layer when determining the final indices of the answer, which conditions the ending index on the starting index. Our implementation of the machine comprehension model was able to achieve moderate results on the leader board, with an F1 of 57.45 and EM of 45.19.