SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on large-scale corpora-based language models is limited and hard to generalize to all types of pre-trained language models. |
| Approach: | They propose a two-stage SimOAP strategy that over-samples and post-evaluates large-scale responses from existing models and selects a good response based on multiple evaluation metrics. |
| Outcome: | The proposed strategy outperforms baseline and automatic evaluation strategies in both automatic and human evaluations. |
Similar Papers
Post Persona Alignment for Multi-Session Dialogue Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for multi-session persona-based dialogue generation typically retrieve persona information before response generation, which can constrain diversity and result in generic outputs. |
| Approach: | They propose a two-stage framework that reverses the process of retrieving persona information before response generation. |
| Outcome: | Experiments on multi-session persona-based dialogue data show that the proposed framework outperforms existing methods in consistency, diversity, and persona relevance. |
Dialogue Language Model with Large-Scale Persona Data Engineering (2025.naacl-industry)
Copied to clipboard
| Challenge: | Existing persona-consistent dialogue models lack robustness due to limited scale and diversity of datasets. |
| Approach: | They propose an open-domain persona dialogue system that employs extensive generative pre-training on a persona dialog dataset to enhance persona consistency. |
| Outcome: | The proposed model generates vast persona dialogue datasets and addresses invalid persona bias. |
Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing persona-based dialogue models generate human-like responses but can hardly avoid the generation of inconsistent persona words. |
| Approach: | They propose a framework that deletes inconsistent words from a generated response prototype and further rewrites it to a personality-consistent one. |
| Outcome: | The proposed framework achieves good performance on the persona-chat dataset. |
Combining Discourse Coherence with Large Language Models for More Inclusive, Equitable, and Robust Task-Oriented Dialogue (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are capable of generating well-formed responses, but they struggle in goal-oriented settings. |
| Approach: | They propose a discourse-aware multimodal task-oriented dialogue system that combines discourse theories with offline LLM generation. |
| Outcome: | The proposed system reduces misunderstandings in the dialect of African-American Vernacular English from 93% to 57%. |
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity (D18-1)
Copied to clipboard
| Challenge: | Existing encoder-decoder models for open domain dialogue generate generic, uninformative, and non-coherent responses. |
| Approach: | They propose to introduce a measure of coherence as the GloVe embedding similarity between dialogue context and generated response to improve output diversity. |
| Outcome: | The proposed model improves on the OpenSubtitles corpus in terms of BLEU score and diversity metrics. |
A Synthetic Data Generation Framework for Grounded Dialogues (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to train grounded dialogues require large amounts of data. |
| Approach: | They propose a synthetic data generation framework for grounded dialogues that takes knowledge data and heuristics to determine a dialogue flow and incrementally turn it into a dialog. |
| Outcome: | The proposed framework significantly boosts model performance in training data and low-resource scenarios. |
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data (2021.acl-long)
Copied to clipboard
| Challenge: | Existing persona-based dialogue models use crowd-sourced data, such as the PersonaChat . however, the cost of such datasets is limited, and the model is not robust. |
| Approach: | They propose to disentangle persona-based dialogue generation into two sub-tasks by using a BERT-over-BERT model. |
| Outcome: | The proposed model outperforms baselines in response quality and persona consistency under different limited data settings. |
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence (2021.acl-long)
Copied to clipboard
| Challenge: | Existing generation models struggle to maintain a coherent event sequence throughout the generated text. |
| Approach: | They propose a long text generation model which can represent prefix sentences at sentence level and discourse level in the decoding process. |
| Outcome: | The proposed model can generate more coherent texts than state-of-the-art models. |
Improving Multi-party Dialogue Generation via Topic and Rhetorical Coherence (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on multi-party dialogue generation focus on the reply-to structure of dialogue histories, but they neglect the coherence between generated responses and target utterances. |
| Approach: | They propose a Reinforcement Learning approach emphasizing Topic and Rhetorical Coherence to enhance the model's perception of coherence with the target utterance. |
| Outcome: | The proposed approach significantly outperforms the state-of-the-art baselines on two popular datasets. |
Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Empathetic dialogue is an essential part of building harmonious social relationships and contributes to the development of a helpful AI. |
| Approach: | They propose three methods to improve the performance of large language models (LLMs) they propose semantically similar in-context learning, two-stage interactive generation and combination with the knowledge base. |
| Outcome: | The proposed methods achieve state-of-the-art in automatic and human evaluations and the possibility of GPT-4 simulating human evaluators. |