2016/06/07 by Alessandro Sordoni, Philip Bachman, Sordoni, Alessandro +5
Computer Science · #Computation and Language (cs.CL) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Natural Language Processing Techniques #Neural and Evolutionary Computing (cs.NE) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1606.02245
openalex publication_date 2016/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a novel neural attention architecture to tackle machine comprehension tasks, such as answering Cloze-style queries with respect to a document. Unlike previous models, we do not collapse the query into a single vector, instead we deploy an iterative alternating attention mechanism that allows a fine-grained exploration of both the query and the document. Our model outperforms state-of-the-art baselines in standard machine comprehension benchmarks such as CNN news articles and the Children's Book Test (CBT) dataset.