# Personalized Topic Selection Model for Topic-Grounded Dialogue

arXiv: https://arxiv.org/abs/2406.01988 · HTML: https://mlsanity.com/paper/2406.01988/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Towards Topic-Guided Conversational Recommender System](https://mlsanity.com/paper/2010.04125/) (2020)
- [Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/1905.11553/) (2019)
- [Are LLMs All You Need for Task-Oriented Dialogue?](https://mlsanity.com/paper/2304.06556/) (2023)
- [Keyword-Guided Neural Conversational Model](https://mlsanity.com/paper/2012.08383/) (2020)
- [Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction](https://mlsanity.com/paper/2106.03044/) (2021)
- [Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems](https://mlsanity.com/paper/2109.04084/) (2021)
- [Improving Personality Consistency in Conversation by Persona Extending](https://mlsanity.com/paper/2208.10816/) (2022)
- [Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/2002.01196/) (2020)
- [TREA: Tree-Structure Reasoning Schema for Conversational Recommendation](https://mlsanity.com/paper/2307.10543/) (2023)
- [MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control](https://mlsanity.com/paper/2310.18342/) (2023)
- [Target-Guided Open-Domain Conversation Planning](https://mlsanity.com/paper/2209.09746/) (2022)
- [Incorporating Causal Analysis into Diversified and Logical Response Generation](https://mlsanity.com/paper/2209.09482/) (2022)
- [Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue](https://mlsanity.com/paper/2210.08801/) (2022)

## Built on by (most cited first, 1 of 1)

- [Enhanced Sample Selection with Confidence Tracking: Identifying Correctly Labeled yet Hard-to-Learn Samples in Noisy Data](https://mlsanity.com/paper/2504.17474/) (2025)

## Abstract (arXiv)

Recently, the topic-grounded dialogue (TGD) system has become increasingly popular as its powerful capability to actively guide users to accomplish specific tasks through topic-guided conversations. Most existing works utilize side information (eg topics or personas) in isolation to enhance the topic selection ability. However, due to disregarding the noise within these auxiliary information sources and their mutual influence, current models tend to predict user-uninteresting and contextually irrelevant topics. To build user-engaging and coherent dialogue agent, we propose a Personalized topic sElection model for Topic-grounded Dialogue, named PETD, which takes account of the interaction of side information to selectively aggregate such information for more accurately predicting subsequent topics. Specifically, we evaluate the correlation between global topics and personas and selectively incorporate the global topics aligned with user personas. Furthermore, we propose a contrastive learning based persona selector to filter out irrelevant personas under the constraint of lacking pertinent persona annotations. Throughout the selection and generation, diverse relevant side information is considered. Extensive experiments demonstrate that our proposed method can generate engaging and diverse responses, outperforming state-of-the-art baselines across various evaluation metrics.