# Training Neural Response Selection for Task-Oriented Dialogue Systems

arXiv: https://arxiv.org/abs/1906.01543 · HTML: https://mlsanity.com/paper/1906.01543/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 30 of 49)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders](https://mlsanity.com/paper/2104.08027/) (2021)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.00783/) (2021)
- [Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech](https://mlsanity.com/paper/2106.01625/) (2021)
- [Multilingual and Cross-Lingual Intent Detection from Spoken Data](https://mlsanity.com/paper/2104.08524/) (2021)
- [TWEETSUMM -- A Dialog Summarization Dataset for Customer Service](https://mlsanity.com/paper/2111.11894/) (2021)
- [Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog](https://mlsanity.com/paper/2205.10400/) (2022)
- [Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval](https://mlsanity.com/paper/2103.11920/) (2021)
- [Extreme Multi-label Learning for Semantic Matching in Product Search](https://mlsanity.com/paper/2106.12657/) (2021)
- [ConvFiT: Conversational Fine-Tuning of Pretrained Language Models](https://mlsanity.com/paper/2109.10126/) (2021)
- [Pretraining the Noisy Channel Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2103.10518/) (2021)
- [DS-TOD: Efficient Domain Specialization for Task Oriented Dialog](https://mlsanity.com/paper/2110.08395/) (2021)
- [Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization](https://mlsanity.com/paper/2205.07208/) (2022)
- [Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems](https://mlsanity.com/paper/2108.12589/) (2021)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [Learning Dialogue Representations from Consecutive Utterances](https://mlsanity.com/paper/2205.13568/) (2022)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)
- [A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles](https://mlsanity.com/paper/2102.08322/) (2021)
- [NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue](https://mlsanity.com/paper/2204.13021/) (2022)
- [AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog](https://mlsanity.com/paper/2209.03632/) (2022)
- [Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders](https://mlsanity.com/paper/2205.00267/) (2022)
- [TADA: Efficient Task-Agnostic Domain Adaptation for Transformers](https://mlsanity.com/paper/2305.12717/) (2023)
- [Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation](https://mlsanity.com/paper/2310.18119/) (2023)
- [Domain Adaptation in Intent Classification Systems: A Review](https://mlsanity.com/paper/2404.14415/) (2024)
- [Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning](https://mlsanity.com/paper/2110.00159/) (2021)
- [DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling](https://mlsanity.com/paper/2204.07679/) (2022)
- [DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog](https://mlsanity.com/paper/2212.07112/) (2022)
- [Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridging](https://mlsanity.com/paper/2105.10267/) (2021)
- [Language Scaling for Universal Suggested Replies Model](https://mlsanity.com/paper/2106.02232/) (2021)

## Abstract (arXiv)

Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks. Inspired by the recent success of pretraining in language modelling, we propose an effective method for deploying response selection in task-oriented dialogue. To train response selection models for task-oriented dialogue tasks, we propose a novel method which: 1) pretrains the response selection model on large general-domain conversational corpora; and then 2) fine-tunes the pretrained model for the target dialogue domain, relying only on the small in-domain dataset to capture the nuances of the given dialogue domain. Our evaluation on six diverse application domains, ranging from e-commerce to banking, demonstrates the effectiveness of the proposed training method.