# Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models

arXiv: https://arxiv.org/abs/1704.08966 · HTML: https://mlsanity.com/paper/1704.08966/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (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)
- [Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement](https://mlsanity.com/paper/2504.05614/) (2025)
- [Stop Filtering: Multi-View Attribute-Enhanced Dialogue Learning](https://mlsanity.com/paper/2205.11206/) (2022)
- [Mixture of Decoders for Diverse Dialog Response Generation](https://mlsanity.com/paper/2610.00621/) (2026)

## Abstract (arXiv)

Neural conversational models require substantial amounts of dialogue data for their parameter estimation and are therefore usually learned on large corpora such as chat forums or movie subtitles. These corpora are, however, often challenging to work with, notably due to their frequent lack of turn segmentation and the presence of multiple references external to the dialogue itself. This paper shows that these challenges can be mitigated by adding a weighting model into the architecture. The weighting model, which is itself estimated from dialogue data, associates each training example to a numerical weight that reflects its intrinsic quality for dialogue modelling. At training time, these sample weights are included into the empirical loss to be minimised. Evaluation results on retrieval-based models trained on movie and TV subtitles demonstrate that the inclusion of such a weighting model improves the model performance on unsupervised metrics.