Topic-based Evaluation for Conversational Bots

TL;DR

This paper introduces topic-based metrics for evaluating conversational bots, focusing on coherence, engagement, and topic diversity, using a novel deep learning approach to classify conversation topics.

Contribution

It presents a new topic classification method with a topic-word attention mechanism and demonstrates that topic-based metrics align with human judgments in bot evaluation.

Findings

Metrics correlate with human ratings

Topic diversity improves user engagement

Proposed method outperforms baseline classifiers

Abstract

Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined. We propose to evaluate dialog quality using topic-based metrics that describe the ability of a conversational bot to sustain coherent and engaging conversations on a topic, and the diversity of topics that a bot can handle. To detect conversation topics per utterance, we adopt Deep Average Networks (DAN) and train a topic classifier on a variety of question and query data categorized into multiple topics. We propose a novel extension to DAN by adding a topic-word attention table that allows the system to jointly capture topic keywords in an utterance and perform topic classification. We compare our proposed topic based metrics with the ratings provided by users and show that our metrics both correlate with and complement human judgment. Our analysis is performed on tens of thousands of real human-bot dialogs from the Alexa Prize competition and highlights user expectations for conversational bots.