# Multi-domain Neural Network Language Generation for Spoken Dialogue Systems

arXiv: https://arxiv.org/abs/1603.01232 · HTML: https://mlsanity.com/paper/1603.01232/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2016.

## Built on by (most cited first, 18 of 18)

- [NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://mlsanity.com/paper/2112.08726/) (2021)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Plan-then-Generate: Controlled Data-to-Text Generation via Planning](https://mlsanity.com/paper/2108.13740/) (2021)
- [Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation](https://mlsanity.com/paper/2201.13405/) (2022)
- [AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation](https://mlsanity.com/paper/2106.05589/) (2021)
- [GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2204.06674/) (2022)
- [Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation](https://mlsanity.com/paper/2401.10186/) (2024)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Towards Standard Criteria for human evaluation of Chatbots: A Survey](https://mlsanity.com/paper/2105.11197/) (2021)
- [XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages](https://mlsanity.com/paper/2202.00291/) (2022)
- [Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation](https://mlsanity.com/paper/2205.02570/) (2022)
- [DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning](https://mlsanity.com/paper/2205.12662/) (2022)
- [Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning](https://mlsanity.com/paper/2209.07873/) (2022)
- [Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation](https://mlsanity.com/paper/2205.09661/) (2022)
- [XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages](https://mlsanity.com/paper/2209.11252/) (2022)
- [Compositional Generalization for Data-to-Text Generation](https://mlsanity.com/paper/2312.02748/) (2023)
- [Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks](https://mlsanity.com/paper/2110.15724/) (2021)
- [Requirements Elicitation in Cognitive Service for Recommendation](https://mlsanity.com/paper/2203.14958/) (2022)

## Abstract (arXiv)

Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains. Therefore, it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation. In this paper, we propose a procedure to train multi-domain, Recurrent Neural Network-based (RNN) language generators via multiple adaptation steps. In this procedure, a model is first trained on counterfeited data synthesised from an out-of-domain dataset, and then fine tuned on a small set of in-domain utterances with a discriminative objective function. Corpus-based evaluation results show that the proposed procedure can achieve competitive performance in terms of BLEU score and slot error rate while significantly reducing the data needed to train generators in new, unseen domains. In subjective testing, human judges confirm that the procedure greatly improves generator performance when only a small amount of data is available in the domain.