Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems

TL;DR

This paper introduces a large-scale, movie-based dataset with diverse tasks to evaluate and improve end-to-end dialog systems, bridging the gap between synthetic reasoning tests and real conversational data.

Contribution

It presents a new extensive dataset and tasks for assessing dialog models' reasoning, personalization, and natural conversation abilities in a realistic domain.

Findings

Models show varying performance across tasks

Dataset enables evaluation of reasoning and personalization

Results highlight challenges in natural dialog understanding

Abstract

A long-term goal of machine learning is to build intelligent conversational agents. One recent popular approach is to train end-to-end models on a large amount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals & Le, 2015; Shang et al., 2015). However, this approach leaves many questions unanswered as an understanding of the precise successes and shortcomings of each model is hard to assess. A contrasting recent proposal are the bAbI tasks (Weston et al., 2015b) which are synthetic data that measure the ability of learning machines at various reasoning tasks over toy language. Unfortunately, those tests are very small and hence may encourage methods that do not scale. In this work, we propose a suite of new tasks of a much larger scale that attempt to bridge the gap between the two regimes. Choosing the domain of movies, we provide tasks that test the ability of models to answer factual questions (utilizing OMDB), provide personalization (utilizing MovieLens), carry short conversations about the two, and finally to perform on natural dialogs from Reddit. We provide a dataset covering 75k movie entities and with 3.5M training examples. We present results of various models on these tasks, and evaluate their performance.