# Multi-Task Learning for Situated Multi-Domain End-to-End Dialogue Systems

arXiv: https://arxiv.org/abs/2110.05221 · HTML: https://mlsanity.com/paper/2110.05221/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1901.11504/) (2019)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1907.05774/) (2019)
- [SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching](https://mlsanity.com/paper/2005.05298/) (2020)
- [BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1907.03040/) (2019)
- [Situated and Interactive Multimodal Conversations](https://mlsanity.com/paper/2006.01460/) (2020)
- [Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning](https://mlsanity.com/paper/1606.02560/) (2016)
- [Multi-task learning for Joint Language Understanding and Dialogue State Tracking](https://mlsanity.com/paper/1811.05408/) (2018)
- [Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker](https://mlsanity.com/paper/2002.02450/) (2020)
- [SIMMC: Situated Interactive Multi-Modal Conversational Data Collection And Evaluation Platform](https://mlsanity.com/paper/1911.02690/) (2019)
- [Jointly Optimizing State Operation Prediction and Value Generation for Dialogue State Tracking](https://mlsanity.com/paper/2010.14061/) (2020)

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

- [Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark](https://mlsanity.com/paper/2305.18212/) (2023)

## Abstract (arXiv)

Task-oriented dialogue systems have been a promising area in the NLP field. Previous work showed the effectiveness of using a single GPT-2 based model to predict belief states and responses via causal language modeling. In this paper, we leverage multi-task learning techniques to train a GPT-2 based model on a more challenging dataset with multiple domains, multiple modalities, and more diversity in output formats. Using only a single model, our method achieves better performance on all sub-tasks, across domains, compared to task and domain-specific models. Furthermore, we evaluated several proposed strategies for GPT-2 based dialogue systems with comprehensive ablation studies, showing that all techniques can further improve the performance.