LSTM-Based Mixture-of-Experts for Knowledge-Aware Dialogues
Phong Le, Marc Dymetman, Jean-Michel Renders · 2016
We introduce an LSTM-based method for dynamically integrating several wordprediction experts to obtain a conditional language model which can be good simultaneously at several subtasks.We illustrate this general approach with an application to dialogue where we integrate a neural chat model, good at conversational aspects, with a neural question-answering model, good at retrieving precise information from a knowledge-base, and show how the integration combines the strengths of the independent components.We hope that this focused contribution will attract attention on the benefits of using such mixtures of experts in NLP and dialogue systems specifically.* Work performed during Phong Le's internship at XRCE in 2015.