Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation

Deeksha Varshney, Akshara Prabhakar, Asif Ekbal


Abstract
Grounding dialogue on external knowledge and interpreting linguistic patterns in dialogue history context, such as ellipsis, anaphora, and co-reference 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 datasets 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.
Anthology ID:
2022.naacl-main.95
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1322–1335
Language:
URL:
https://aclanthology.org/2022.naacl-main.95
DOI:
10.18653/v1/2022.naacl-main.95
Bibkey:
Cite (ACL):
Deeksha Varshney, Akshara Prabhakar, and Asif Ekbal. 2022. Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1322–1335, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation (Varshney et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.95.pdf
Software:
 2022.naacl-main.95.software.zip
Code
 deekshavarshney/cntf
Data
Wizard of Wikipedia