Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation

Abstract

Grounding dialogue on external knowledge and interpreting linguistic patterns in dialogue history context, such as ellipsis, anaphora, and co-references is critical for dialogue comprehension and generation. In this paper, we present a novel open-domain dialogue generation model which effectively utilizes the large-scale commonsense and named entity based knowledge in addition to the unstructured topic-specific knowledge associated with each utterance. We enhance the commonsense knowledge with named entity-aware structures using co-references. Our proposed model utilizes a multi-hop attention layer to preserve the most accurate and critical parts of the dialogue history and the associated knowledge. In addition, we employ a Commonsense and Named Entity Enhanced Attention Module, which starts with the extracted triples from various sources and gradually finds the relevant supporting set of triples using multi-hop attention with the query vector obtained from the interactive dialogue-knowledge module.Empirical results on two benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art methods in terms of both automatic evaluation metrics and human judgment. Our code is publicly available at https://github.com/deekshaVarshney/CNTF; https://www.iitp.ac.in/-ai-nlp-ml/resources/ codes/CNTF.zip.

Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues

“…Wu et al [41] defined knowledge identification as finding relevant knowledge in an extensive document that aligns with a user's current query within the conversation context. In their work [15], introduce a KGD model for documentgrounded dialogue generation. The model leverages both structured and unstructured knowledge sources to enhance its performance.…”

“…Nonetheless, these conventional conversation models often grapple with generating informative and engaging responses due to their limited capacity to retain and leverage background knowledge [7,8]. To surmount this knowledge-absence issue prevalent in existing conversation models, Knowledge Grounded Dialogue (KGD) generation is recently proposed for generating responses by simultaneously referring to both the background knowledge and the dialogue context [7,[9][10][11][12][13][14][15][16][17]. The objective is to enhance dialogue response generation to facilitate engaging and in-depth conversations, while avoiding the inclusion of non-factual information.…”