We study a conversational reasoning model that strategically traverses through a largescale 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 15 K human-to-human roleplaying 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-ofthe-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 . 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 . Hence , we study a data-driven reasoning model topical jumps across open-ended multi-turn dialogs are annotated and grounded with a large-scale commonfact KG . To generate a KG entity response at each dialog turn , the model learns walkable paths within KG that lead to engaging and natural topics or entities given dialog context , while pruning non-ideal ( albeit factually correct ) KG paths among 1M+ candidate facts . that map dialog transitions with KG paths , aimed at identifying a subset of ideal entities to mention as a response to previous dialog contexts . Figure 1 illustrates a motivating dialog example between two conversation participants , which spans multiple related KG entities from a starting seed entity The Catcher in the Rye . Specifically , we observe that there exists a small subset of walkable patterns within a KG or a preferred sequence of graph traversal steps which often leads to more engaging entities or attributes than others ( e.g. Literacy Realism , Nathaniel Hawthorne , etc . vs. Catch Me If You Can , 277 , etc . all connected via one-or multi-hop factual connections ) . Note also that the walkable degree of each entity varies by dialog contexts and domains , thus making conventional rule-based or entity-toentity learning approaches intractable or not scalable for open-ended dialogs with 1M+ candidate facts . Therefore , 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 . To this end , 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 KG . Specifically , we propose a novel graph decoder that attends on viable KG paths to predict the most relevant entities from a KG , by associating these paths with the given input contexts : dialog , sentence , and a set of starting KG entities mentioned in the previous turn . We then build a parallel zeroshot learning model that predicts entities in the KG embeddings space , and ranks candidate entities based on decoded graph path output . To train the DialKG Walker model with groundtruth reference to KG entities , we collect a new human-to-human multi-turn dialogs dataset ( 91 K utterances across 15 K dialog sessions ) using Par-lAI ( Miller et al . , 2017 ) , where conversation participants play a role either as a user or as an assistant , while annotating their mention of an entity in a large-scale common fact KG . This new dataset provides a new way for researchers to study how conversational topics could jump across many different entities within multi-turn dialogs , grounded on KG paths that thread all of them . To the best of our knowledge , our OpenDialKG is the first parallel Dialog ↔ KG corpus where each mention of a KG entity and its factual connection in an openended dialog is fully annotated , allowing for indepth study of symbolic reasoning and natural language conversations . Note that our approaches are distinct from the previous work on dialog systems in that we completely ground dialogs in a large-scale commonfact KG , allowing for domain-agnostic conversational reasoning in open-ended conversations across various domains and tasks ( e.g. chit-chat , recommendations , etc . ) We therefore perform extensive cross-domain and transfer learning evaluations to demonstrate its flexibility . See Section 5 for the detailed literature review . Our contributions are as follows : we propose ( 1 ) a novel attention-based graph decoder that walks an optimal path within a large commonsense KG ( 100 K entities , 1.1 M facts ) to effectively prune unlikely candidate entities , and ( 2 ) a zeroshot learning model that leverages previous sentence , dialog , and KG contexts to re-rank candidates from pruned decoder graph output based on their relevance and path scores , which allows for generalizable and robust classification with a large number of candidate classes . We present ( 3 ) a new parallel open-ended dialog ↔ KG corpus called OpenDialKG where each mention of an entity in dialog is manually linked with its corresponding ground-truth KG path . We show that the proposed approaches outperform baselines in both indomain and cross-domain evaluation , demonstrating that the model learns domain-agnostic walking patterns that are generalizable for unseen domains . We study conversational reasoning grounded on knowledge graphs , and formulate an approach in which the model learns to navigate a largescale , open-ended KG given conversational contexts . For this study , we collect a newly annotated Dialog ↔ KG parallel corpus of 15 K humanto-human dialogs which includes ground-truth annotation of each dialog turn to its reasoning reference in a large-scale common fact KG . Our proposed DialKG Walker model improves upon the state-of-the-art knowledge-augmented conversation models by 1 ) a novel attention-based graph decoder that penalizes decoding of unnatural paths which effectively prunes candidate entities and paths from a large search space ( 1.1 M facts ) , 2 ) a zeroshot learning model that predicts its relevance score in the KG embeddings space , combined score of which is used for candidate ranking . The empirical results from in-domain , cross-domain , and transfer learning evaluation demonstrate the efficacy of the proposed model in domain-agnostic conversational reasoning .
Using a large knowledge base for dialogue systems is intractable or not scalable which calls for methods that prune search space for entities.
They provide an open-ended dialogue corpus where each utterance is annotated with entities and paths and propose a model that works on this data structure.
The proposed model can produce more natural responses than state-of-the-art models on automatic and human evaluation, and generated knowledge graph paths provide explainability.