| Challenge: | End-to-end neural dialogue generation does not employ knowledge to guide the generation. |
| Approach: | They propose a neural knowledge diffusion model to introduce knowledge into dialogue generation. |
| Outcome: | The proposed model outperforms baseline models on a real-world dataset. |
Similar Papers
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue (2022.findings-emnlp)
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| Challenge: | Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. |
| Approach: | They propose a modular model for incorporating knowledge into conversational agents that generates a knowledge sequence and then attends to its own generated knowledge sequence. |
| Outcome: | The proposed model hallucinates less in knowledge-grounded dialogue tasks and has advantages in terms of interpretability and modularity. |
Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness (2020.acl-main)
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| Challenge: | Generative dialogue systems tend to produce generic and boring responses, causing boring conversations . a novel commonsense knowledge-aware dialogue generation model is proposed to solve this problem . |
| Approach: | They propose to retrieve and introduce knowledge facts from knowledge graphs to reduce boring conversations . they use a Felicitous Fact mechanism to help the model focus on context-relevant knowledge facts . |
| Outcome: | The proposed model outperforms the state-of-the-art approach in most experiments. |
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)
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| Challenge: | Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data. |
| Approach: | They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks. |
| Outcome: | The proposed language model generalizes well across knowledge-grounded dialogue tasks. |
Extending Neural Generative Conversational Model using External Knowledge Sources (D18-1)
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| Challenge: | Existing generative dialogue models lack coherence and are content poor . however, current models lack the capacity to handle large unstructured knowledge sources. |
| Approach: | They propose an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. |
| Outcome: | The proposed architecture improves the next utterance prediction in chit-chat type of generative dialogue models by incorporating external knowledge from Wikipedia summaries and the NELL knowledge base. |
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation (2021.emnlp-main)
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| Challenge: | Existing knowledge-grounded dialogues perform poorly when transfer into new domains with limited training samples. |
| Approach: | They propose a weakly supervised three-stage learning framework based on weakly-supervised learning based upon large scale ungrounded dialogues and unstructured knowledge base. |
| Outcome: | The proposed framework outperforms state-of-the-art methods even in zero-resource setting. |
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space (2024.lrec-main)
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| Challenge: | Existing studies have tried to introduce discrete or Gaussian-based latent variables to address the one-to-many problem, but the diversity is limited. |
| Approach: | They propose a diffusion model to enhance the diversity of dialogue generation by using continuous latent variables instead of discrete ones. |
| Outcome: | The proposed model greatly enhances diversity of dialog response while keeping the coherence. |
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable (2020.acl-main)
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| Challenge: | Existing pre-training models for dialogue generation have been proven effective for a wide range of tasks. |
| Approach: | They propose a dialogue generation pre-training framework that leverages bi-directional context and uni-directional characteristic of language generation. |
| Outcome: | The proposed framework is superior to existing models on three publicly available datasets. |
Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue (2024.lrec-main)
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| Challenge: | Existing approaches to knowledge retrieval are limited by the knowledge base encoder, but our work focuses on the knowledge-base encoder. |
| Approach: | They propose an approach that utilizes topic modeling on the knowledge base to improve retrieval accuracy and as a result, improve response generation. |
| Outcome: | The proposed approach can improve retrieval and generation performance on two datasets. |
Grounding in social media: An approach to building a chit-chat dialogue model (2022.naacl-srw)
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| Challenge: | Existing open-domain dialogue models fail to capture and utilize external knowledge, leading to repetitive or generic responses to unseen utterances. |
| Approach: | They propose to use social media comments to improve the raw conversation ability of open-domain dialogue systems. |
| Outcome: | The proposed model improves the raw conversation ability of open-domain dialogue systems by mimicking human responses through casual interactions found on social media. |