# Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps?

arXiv: https://arxiv.org/abs/2305.15908 · HTML: https://mlsanity.com/paper/2305.15908/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Beyond Goldfish Memory: Long-Term Open-Domain Conversation](https://mlsanity.com/paper/2107.07567/) (2021)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020)
- [Long Time No See! Open-Domain Conversation with Long-Term Persona Memory](https://mlsanity.com/paper/2203.05797/) (2022)
- [Knowledge-Grounded Dialogue Generation with Pre-trained Language Models](https://mlsanity.com/paper/2010.08824/) (2020)
- [Personalizing Dialogue Agents via Meta-Learning](https://mlsanity.com/paper/1905.10033/) (2019)
- [Inseq: An Interpretability Toolkit for Sequence Generation Models](https://mlsanity.com/paper/2302.13942/) (2023)
- [Keep Me Updated! Memory Management in Long-term Conversations](https://mlsanity.com/paper/2210.08750/) (2022)
- [Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering](https://mlsanity.com/paper/2211.03433/) (2022)
- [Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study](https://mlsanity.com/paper/2204.01440/) (2022)
- [Personalization in Goal-Oriented Dialog](https://mlsanity.com/paper/1706.07503/) (2017)
- [Personalized Dialogue Generation with Persona-Adaptive Attention](https://mlsanity.com/paper/2210.15088/) (2022)
- [IT5: Text-to-text Pretraining for Italian Language Understanding and Generation](https://mlsanity.com/paper/2203.03759/) (2022)
- [GePpeTto Carves Italian into a Language Model](https://mlsanity.com/paper/2004.14253/) (2020)
- [Building a Personalized Dialogue System with Prompt-Tuning](https://mlsanity.com/paper/2206.05399/) (2022)
- [Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function](https://mlsanity.com/paper/2303.13797/) (2023)
- [Adapting Document-Grounded Dialog Systems to Spoken Conversations using Data Augmentation and a Noisy Channel Model](https://mlsanity.com/paper/2112.08844/) (2021)
- [Look Who's Talking: Inferring Speaker Attributes from Personal Longitudinal Dialog](https://mlsanity.com/paper/1904.11610/) (2019)

## Built on by (most cited first, 6 of 6)

- [Towards Lifelong Dialogue Agents via Timeline-based Memory Management](https://mlsanity.com/paper/2406.10996/) (2024)
- [Are LLMs Robust for Spoken Dialogues?](https://mlsanity.com/paper/2401.02297/) (2024)
- [Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue](https://mlsanity.com/paper/2406.06399/) (2024)
- [AI-generated Text Detection: A Multifaceted Approach to Binary and Multiclass Classification](https://mlsanity.com/paper/2505.11550/) (2025)
- [Post Persona Alignment for Multi-Session Dialogue Generation](https://mlsanity.com/paper/2506.11857/) (2025)
- [Personalized LLM for Generating Customized Responses to the Same Query from Different Users](https://mlsanity.com/paper/2412.11736/) (2024)

## Abstract (arXiv)

Longitudinal Dialogues (LD) are the most challenging type of conversation for human-machine dialogue systems. LDs include the recollections of events, personal thoughts, and emotions specific to each individual in a sparse sequence of dialogue sessions. Dialogue systems designed for LDs should uniquely interact with the users over multiple sessions and long periods of time (e.g. weeks), and engage them in personal dialogues to elaborate on their feelings, thoughts, and real-life events. In this paper, we study the task of response generation in LDs. We evaluate whether general-purpose Pre-trained Language Models (PLM) are appropriate for this purpose. We fine-tune two PLMs, GePpeTto (GPT-2) and iT5, using a dataset of LDs. We experiment with different representations of the personal knowledge extracted from LDs for grounded response generation, including the graph representation of the mentioned events and participants. We evaluate the performance of the models via automatic metrics and the contribution of the knowledge via the Integrated Gradients technique. We categorize the natural language generation errors via human evaluations of contextualization, appropriateness and engagement of the user.