Learning Retrieval Augmentation for Personalized Dialogue Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Personalized dialogue generation is a popular approach for conversational AI applications . however, persona profiles may not provide comprehensive descriptions of the persona . |
| Approach: | They propose a method that leverages persona profiles and dialogue context to generate personalized dialogues by leveraging personas and persona profile. |
| Outcome: | The proposed method outperforms baselines on the CONVAI2 dataset . it is expected to generate personalized dialogues based on persona profiles and dialogue context . |
Similar Papers
A Personalized Dialogue Generator with Implicit User Persona Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models for personalized dialogue generation tend to be self-centered, with little care for the user in the dialogue. |
| Approach: | They propose a personalized dialogue generator by detecting an implicit user persona and using conditional variational inference to model the user's potential persona with no external knowledge. |
| Outcome: | The proposed model improves both automatic metrics and human evaluations by focusing on the user's persona and posterior-discriminated regularization. |
“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create. |
| Approach: | They propose a framework that leverages dialogue history to characterize personas without pre-defined profiles. |
| Outcome: | The proposed framework improves BLEU and ROUGE scores on three datasets and human evaluations further validate the proposed method. |
Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona (2023.acl-long)
Copied to clipboard
| Challenge: | Existing personalized dialogue agents model persona profiles from sparse or dense persona descriptions and dialogue histories. |
| Approach: | They propose a model that clusters dense persona descriptions into sparse categories and generates personalized responses from dialogue histories. |
| Outcome: | The proposed model improves on Chinese and English datasets. |
Learning to Abstract for Memory-augmented Conversational Response Generation (P19-1)
Copied to clipboard
| Challenge: | Existing generative models for open-domain chit-chat conversations lack informativeness and diversity. |
| Approach: | They propose a retrieval-augmented generative model that learns to abstract from the training corpus and saves useful information to the memory to assist the response generation. |
| Outcome: | The proposed model outperforms other baselines in query-response clustering and learning to utilize these characteristics for response generation. |
RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences. |
| Approach: | They propose a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. |
| Outcome: | The proposed model generates more fluent and personalized responses under a suite of human and automatic metrics and is superior to state-of-the-art baselines on English Reddit conversations. |
Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations (2024.lrec-main)
Copied to clipboard
| Challenge: | Personalization is a multifaceted process that requires multiple definitions and varies between individuals. |
| Approach: | They propose to systemically survey the recent landscape of personalized dialogue generation including the datasets employed, methodologies developed, and evaluation metrics applied. |
| Outcome: | The proposed model can generate fluent and coherent responses to human queries in a language-based conversational agent. |
Training Millions of Personalized Dialogue Agents (D18-1)
Copied to clipboard
| Challenge: | Current dialogue systems fail at being engaging for users when trained end-to-end without relying on proactive reengaging scripted strategies. |
| Approach: | They propose a dataset that provides 5 million personas and 700 million person-based dialogues. |
| Outcome: | The proposed dataset provides 5 million personas and 700 million person-based dialogues. |
PRODIGy: a PROfile-based DIalogue Generation dataset (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing profiles-based dialogue datasets lack explicit profile representations or are difficult to collect. |
| Approach: | They propose a dataset that brings together multiple profiles for each speaker, and then integrates them together to provide a more comprehensive profile dimension set for generative language models. |
| Outcome: | The PRODIGy dataset provides a more comprehensive profile dimension set for each speaker. |
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling (2022.coling-1)
Copied to clipboard
| Challenge: | a conversational system can learn to rank response candidates for a given dialogue context by computing similarity between their vector representations. |
| Approach: | They propose a framework that incorporates augmented dialogue contexts into the learning objective. |
| Outcome: | The proposed framework outperforms existing methods and is more robust to perturbations seen during inference. |
We Are What We Repeatedly Do: Inducing and Deploying Habitual Schemas in Persona-Based Responses (2023.emnlp-main)
Copied to clipboard
| Challenge: | a variety of personas can be elicited from large language models, but they are opaque and unpredictable. |
| Approach: | They propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses. |
| Outcome: | The proposed method captures habitual knowledge and generates persona-based responses from a large language model. |