MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on generating implicit user profiles from the user’s dialogue history, thus it suffers from data sparsity and performance degradation. |
| Approach: | They propose a self-supervised learning framework MCP for capturing better representations from users’ dialogue history for personalized chatbots. |
| Outcome: | The proposed model improves on two real-world datasets. |
Similar Papers
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents (2021.naacl-main)
Copied to clipboard
| Challenge: | End-to-end deep learning methods that focus on user satisfaction are challenging due to the required annotation costs and turnaround times. |
| Approach: | They propose a self-supervised contrastive learning approach that leverages the pool of unlabeled data to learn user-agent interactions. |
| Outcome: | The proposed approach reduces the required number of annotations while improving generalization on unseen skills. |
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. |
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation (2023.emnlp-main)
Copied to clipboard
Chenxu Yang, Zheng Lin, Lanrui Wang, Chong Tian, Liang Pang, Jiangnan Li, Qirong Ho, Yanan Cao, Weiping Wang
| Challenge: | Existing knowledge-grounded dialogue generation models struggle with dull and repetitive outputs, a problem commonly termed as text degeneration. |
| Approach: | They propose a framework that allows the model to "cheat" the objective by duplicating knowledge segments in a superficial pattern matching based on overlap. |
| Outcome: | The proposed framework can be applied to a WoW dataset and shows that it works across models and decoding strategies. |
“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. |
Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing personalized dialogue systems extract user profiles from dialogue history to guide personalized response generation. |
| Approach: | They propose to refine the user dialogue history on a large scale to obtain more persona information from the dialogue history and leverage other similar users' data to enhance personalization. |
| Outcome: | The proposed model can handle more dialogue history and obtain more abundant and accurate persona information. |
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. |
Contextualized and Generalized Sentence Representations by Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to learn contextualized and generalized sentence representations are limited by the size of manually annotated data. |
| Approach: | They propose a method to learn contextualized and generalized sentence representations using contrastive self-supervised learning. |
| Outcome: | The proposed method outperforms baseline methods based on BERT, XLNet, and RoBERTa in English and Japanese and outperformed strong baseline methods. |
Sequence-level Large Language Model Training with Contrastive Preference Optimization (2025.findings-naacl)
Copied to clipboard
| Challenge: | a new method to improve the performance of large language models requires a small computational cost. |
| Approach: | They propose a CPO procedure that can inject sequence-level information into the model at any training stage without expensive human labeled data. |
| Outcome: | The proposed objective surpasses the next token prediction in terms of win rate in instruction-following and text generation tasks. |
RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Training (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing intent detection approaches have relied on adaptively pre-training language models with large-scale datasets, yet the predominant cost of data collection may hinder their superiority. |
| Approach: | They propose a self-supervised framework dedicated to task-oriented dialogues which incorporates agent responses for pre-training in a two-stage manner. |
| Outcome: | The proposed framework outperforms the state-of-the-art frameworks for task-oriented dialogues on two real-world customer service datasets. |
Group-wise Contrastive Learning for Neural Dialogue Generation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to training dialogue models have low diversity in open-domain contexts . prior art suggests that naive MLE objective is not effective enough . |
| Approach: | They propose to incorporate contrastive learning into dialogue generation by using a pretrained baseline model as a reference. |
| Outcome: | The proposed framework is suited for training a wide range of dialogue generation models with favorable performance over baseline training approaches. |