Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

TL;DR

CONVEX is an unsupervised approach for conversational question answering over knowledge graphs that effectively handles incomplete and context-dependent queries by expanding the graph exploration frontier.

Contribution

The paper introduces CONVEX, a novel unsupervised method for answering incomplete conversational questions over knowledge graphs, with a new benchmark dataset ConvQuestions.

Findings

CONVEX outperforms state-of-the-art baselines.

It effectively handles incomplete and ambiguous questions.

Provides conversational support to existing QA systems.

Abstract

Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop CONVEX: an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate CONVEX, we release ConvQuestions, a crowdsourced benchmark with 11,200 distinct conversations from five different domains. We show that CONVEX: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies.