Abstract

We study a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes. For this study, we collect a new Open-ended Dialog KG parallel corpus called OpenDialKG, where each utterance from 15K human-to-human role-playing dialogs is manually annotated with ground-truth reference to corresponding entities and paths from a large-scale KG with 1M+ facts. We then propose the DialKG Walker model that learns the symbolic transitions of dialog contexts as structured traversals over KG, and predicts natural entities to introduce given previous dialog contexts via a novel domain-agnostic, attention-based graph path decoder. Automatic and human evaluations show that our model can retrieve more natural and human-like responses than the state-of-the-art baselines or rule-based models, in both in-domain and cross-domain tasks. The proposed model also generates a KG walk path for each entity retrieved, providing a natural way to explain conversational reasoning.

Highlights

  • The key element of an open-ended dialog system is its ability to understand conversational contexts and to respond naturally by introducing relevant entities and attributes, which often leads to increased engagement and coherent interactions (Chen et al, 2018)

  • We propose a new model called DialKG Walker that can learn natural knowledge paths among entities mentioned over dialog contexts, and reason grounded on a large commonsense knowledge graph (KG)

  • Parameters: We tune the parameters of each model with the following search space: KG embeddings size: {64, 128, 256, 512}, LSTM hidden states: {64, 128, 256, 512}, word embeddings size: {100, 200, 300}, max dialog window size: {2, 3, 4, 5}

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Summary

Introduction

The key element of an open-ended dialog system is its ability to understand conversational contexts and to respond naturally by introducing relevant entities and attributes, which often leads to increased engagement and coherent interactions (Chen et al, 2018). While a large-scale knowledge graph (KG) includes vast knowledge of all the related entities connected via one or more factual connections from conversational contexts, the core challenge is in the domain-agnostic and scalable prediction of a small subset from those reachable entities that follows natural conceptual threads that can keep conversations engaging and meaningful. Pruning the search space for entities based on dialog contexts and their relationbased walk paths is a crucial step in operating knowledge-augmented dialog systems at scale

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