Facebook 2016年对于对话系统的综述：A long-term goal of machine learning research is to build an intelligent dialog
agent. Most research in natural language understanding has focused on learning
from fixed training sets of labeled data, with supervision either at the word level
(tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study
dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. We study this
setup in two domains: the bAbI dataset of [30] and large-scale question answering
from [4]. We evaluate a set of baseline learning strategies on these tasks, and show
that a novel model incorporating predictive lookahead is a promising approach for
learning from a teacher’s response. In particular, a surprising result is that it can
learn to answer questions correctly without any reward-based supervision at al
