CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation

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 datasets demonstrate that our model significantly outperforms the state-of-the-art methods in terms of both automatic evaluation metrics and human judgment.

Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation

**Topics:** Dialogue and Interactive Systems 
Efficient Methods for NLP 
Multilinguality 
Sentiment Analysis, Stylistic Analysis, and Argument Mining 
Unsupervised and Weakly-Supervised Methods in NLP 
Ethics 
Information Retrieval and Text Mining 
Interpretability, Interactivity and Analysis of Models for NLP 
Language Modeling and Analysis of Language Models 
Linguistic Theories, Cognitive Modeling and Psycholinguistics 
Machine Learning for NLP

Poster Session 15

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4E: Dialogue and Interactive Systems 1

technical paper

## Welcome to NAACL 2022!
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Deeksha Varshney

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SHORT BIO

Presentations

CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation

Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation

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