# Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

arXiv: https://arxiv.org/abs/1710.07388 · HTML: https://mlsanity.com/paper/1710.07388/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2017.

## Built on by (most cited first, 12 of 12)

- [Revealing Persona Biases in Dialogue Systems](https://mlsanity.com/paper/2104.08728/) (2021)
- [A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles](https://mlsanity.com/paper/2102.08322/) (2021)
- [Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function](https://mlsanity.com/paper/2303.13797/) (2023)
- [Meta-evaluation of Conversational Search Evaluation Metrics](https://mlsanity.com/paper/2104.13453/) (2021)
- [A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots](https://mlsanity.com/paper/2401.00609/) (2023)
- [Text Style Transfer: An Introductory Overview](https://mlsanity.com/paper/2407.14822/) (2024)
- [Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation](https://mlsanity.com/paper/2205.02570/) (2022)
- [Stylistic Retrieval-based Dialogue System with Unparallel Training Data](https://mlsanity.com/paper/2109.05477/) (2021)
- [Know Deeper: Knowledge-Conversation Cyclic Utilization Mechanism for Open-domain Dialogue Generation](https://mlsanity.com/paper/2107.07771/) (2021)
- [An Empirical Study of Multitask Learning to Improve Open Domain Dialogue Systems](https://mlsanity.com/paper/2304.08115/) (2023)
- [Recent Progress in Conversational AI](https://mlsanity.com/paper/2204.09719/) (2022)
- [A Survey of Text Style Transfer: Applications and Ethical Implications](https://mlsanity.com/paper/2407.16737/) (2024)

## Abstract (arXiv)

Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers' traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.