Iterative Multi-document Neural Attention for Multiple Answer Prediction
This work addresses personalized information retrieval for users through conversational agents, but it appears incremental as it builds on existing datasets and tasks without claiming major breakthroughs.
The paper tackles the problem of answering questions with multiple answers by developing a neural network model that uses multiple facts from a knowledge base, achieving evaluation on the bAbI Movie Dialog dataset for factoid QA and recommendation tasks.
People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences. In a scenario in which the user profile can be considered as a question, intelligent agents able to answer questions can be used to find the most relevant answers for a given user. In this work we propose a novel model based on Artificial Neural Networks to answer questions with multiple answers by exploiting multiple facts retrieved from a knowledge base. The model is evaluated on the factoid Question Answering and top-n recommendation tasks of the bAbI Movie Dialog dataset. After assessing the performance of the model on both tasks, we try to define the long-term goal of a conversational recommender system able to interact using natural language and to support users in their information seeking processes in a personalized way.