Year: 2017
Type: article
Abstract: Question Answering is a task which requires building models capable of providing answers to questions expressed in human language.Full question answering involves some form of reasoning ability.We introduce a neural network architecture for this task, which is a form of Memory Network, that recognizes entities and their relations to answers through a focus attention mechanism.Our model is named Question Dependent Recurrent Entity Network and extends the Recurrent Entity Network by exploiting aspects of the question during the memorization process.We validate the model on both synthetic and real datasets: the bAbI question answering dataset and the CNN & Daily News reading comprehension dataset.In our experiments, our models improved the existing Recurrent Entity Network and achieved competitive results in both dataset.
DOI: 10.4000/ijcol.547
Authors: Andrea Madotto, Giuseppe Attardi
Institution: Hong Kong University of Science and Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Università di Pisa
OpenAlex ID: W2736631019