Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation

• URL: http://arxiv.org/abs/2205.13928v1
• Date: Fri, 27 May 2022 12:11:40 GMT
• Title: Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation
• Authors: Deeksha Varshney, Akshara Prabhakar, Asif Ekbal
• Abstract summary: We present a novel open-domain dialogue generation model which effectively utilizes the large-scale commonsense and named entity based knowledge. 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. 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.
• Score: 20.283091595536835
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 \href{https://github.com/deekshaVarshney/CNTF}{https://github.com/deekshaVarshney/CNTF}; \href{https://www.iitp.ac.in/~ai-nlp-ml/resources/codes/CNTF.zip}{https://www.iitp.ac.in/-ai-nlp-ml/resources/ codes/CNTF.zip}.

Related papers

• Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model [25.459787361454353]
  We present a novel framework named R2S that leverages the CoD-Chain of Dialogue logic to guide large language models (LLMs) in generating knowledge-intensive multi-turn dialogues for instruction tuning. By integrating raw documents from both open-source datasets and domain-specific web-crawled documents into a benchmark K-BENCH, we cover diverse areas such as Wikipedia (English), Science (Chinese), and Artifacts (Chinese)
  arXiv  Detail & Related papers  (2024-07-03T12:04:10Z)
• Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
  We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems. KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities. KERL achieves state-of-the-art results in both recommendation and response generation tasks.
  arXiv  Detail & Related papers  (2023-12-18T06:41:23Z)
• Multi-grained Hypergraph Interest Modeling for Conversational Recommendation [75.65483522949857]
  We propose a novel multi-grained hypergraph interest modeling approach to capture user interest beneath intricate historical data. In our approach, we first employ the hypergraph structure to model users' historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations. We further conduct multi-grained hypergraph convolution on the two kinds of hypergraphs, and utilize the enhanced representations to develop interest-aware CRS.
  arXiv  Detail & Related papers  (2023-05-04T13:13:44Z)
• KPT: Keyword-guided Pre-training for Grounded Dialog Generation [82.68787152707455]
  We propose KPT (guided Pre-Training), a novel self-supervised pre-training method for grounded dialog generation. Specifically, we use a pre-trained language model to extract the most uncertain tokens in the dialog as keywords. We conduct extensive experiments on various few-shot knowledge-grounded generation tasks, including grounding on dialog acts, knowledge graphs, persona descriptions, and Wikipedia passages.
  arXiv  Detail & Related papers  (2022-12-04T04:05:01Z)
• CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation [75.60156479374416]
  CGoDial is a new challenging and comprehensive Chinese benchmark for Goal-oriented Dialog evaluation. It contains 96,763 dialog sessions and 574,949 dialog turns totally, covering three datasets with different knowledge sources. To bridge the gap between academic benchmarks and spoken dialog scenarios, we either collect data from real conversations or add spoken features to existing datasets via crowd-sourcing.
  arXiv  Detail & Related papers  (2022-11-21T16:21:41Z)
• kogito: A Commonsense Knowledge Inference Toolkit [19.859765103871943]
  kogito is an open-source tool for generating commonsense inferences about situations described in text. Kogito offers several features for targeted, multi-granularity knowledge generation.
  arXiv  Detail & Related papers  (2022-11-15T19:04:13Z)
• Open-domain Dialogue Generation Grounded with Dynamic Multi-form Knowledge Fusion [9.45662259790057]
  This paper presents a new dialogue generation model, Dynamic Multi-form Knowledge Fusion based Open-domain Chatt-ing Machine (DMKCM) DMKCM applies an indexed text (a virtual Knowledge Base) to locate relevant documents as 1st hop and then expands the content of the dialogue and its 1st hop using a commonsense knowledge graph to get apposite triples as 2nd hop. Experimental results indicate the effectiveness of our method in terms of dialogue coherence and informativeness.
  arXiv  Detail & Related papers  (2022-04-24T10:32:48Z)
• Multi-Sentence Knowledge Selection in Open-Domain Dialogue [11.936691632841388]
  We evaluate the existing state of open-domain conversation knowledge selection. We create an augmented dataset based on the Wizard of Wikipedia (WOW) corpus. WOW++ averages 8 relevant knowledge sentences per dialogue context.
  arXiv  Detail & Related papers  (2022-03-01T22:07:05Z)
• CO-NNECT: A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts [12.94206336329289]
  We leverage commonsense knowledge in form of knowledge paths to establish connections between sentences. These connections can be direct (singlehop paths) or require intermediate concepts (multihop paths)
  arXiv  Detail & Related papers  (2021-05-07T10:43:43Z)
• Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension [49.92173751203827]
  In multi-turn dialog, utterances do not always take the full form of sentences. We propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question.
  arXiv  Detail & Related papers  (2020-12-14T10:58:01Z)
• Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning [117.23425857240679]
  We study the problem of generating inferential texts of events for a variety of commonsense like textitif-else relations. Existing approaches typically use limited evidence from training examples and learn for each relation individually. In this work, we use multiple knowledge sources as fuels for the model.
  arXiv  Detail & Related papers  (2020-04-07T01:49:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.