Étude de modèles neuronaux de questions-réponses
In the field of natural language processing, the question-answer (QA) task involving the development of systems generating a plausible answer to a question asked in natural language by a user from an information source, remains a hot topic. It has many practical applications including fine-grained information retrieval on the web. Today, following a query, current search engines return lists of classified documents but they do not provide an answer to the user’s question.  Since more than fifty years, a number of approaches and technologies have been developed which have led to significant advances in QA. Among these, word embeddings, numerical vectors representing the meaning of words in contexts, the development of model or benchmark datasets and the use of neural networks (NN) have enabled the development of efficient QA systems.  In this context, this thesis focused on the study of the BIDAF (Bidirectional Attention Flow) model, one of the most efficient NN-based QA systems at the start of this work, tested using SQuAD (Stanford Question Answering Dataset), one of the most popular benchmarks in QA. BIDAF’s initial study showed a number of notable structural differences between its literary description and its implementation. Different variants of BIDAF were therefore developed and tested to which were added word embedding training during model training as well as cyclic modulation of the learning rate.  The models of structures similar to the literary description of BIDAF with training of word embeddings and a simplified version showed better performances, i.e. 59.56% in exact match (EM) and 67.09% in F1, and EM = 60.20% and F1 = 67.69 %, respectively. This performance was improved by modulating the learning rate to EM = 61.42% and                F1 = 68.46%.
embeddings BIDAF Question-answer task Neural networks