# Conditional Generation and Snapshot Learning in Neural Dialogue Systems

arXiv: https://arxiv.org/abs/1606.03352 · HTML: https://mlsanity.com/paper/1606.03352/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2016.

## Built on by (most cited first, 17 of 17)

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- [KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning](https://mlsanity.com/paper/2211.16773/) (2022)
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- [OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue](https://mlsanity.com/paper/2209.04595/) (2022)
- [A Hybrid Task-Oriented Dialog System with Domain and Task Adaptive Pretraining](https://mlsanity.com/paper/2102.04506/) (2021)
- [Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation](https://mlsanity.com/paper/2212.01956/) (2022)
- [Integrating Pre-trained Model into Rule-based Dialogue Management](https://mlsanity.com/paper/2102.08553/) (2021)
- [Autoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone Programming](https://mlsanity.com/paper/2302.05950/) (2023)

## Abstract (arXiv)

Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework. A method called snapshot learning is also proposed to facilitate learning from supervised sequential signals by applying a companion cross-entropy objective function to the conditioning vector. The experimental and analytical results demonstrate firstly that competition occurs between the conditioning vector and the LM, and the differing architectures provide different trade-offs between the two. Secondly, the discriminative power and transparency of the conditioning vector is key to providing both model interpretability and better performance. Thirdly, snapshot learning leads to consistent performance improvements independent of which architecture is used.