# Quantized-Dialog Language Model for Goal-Oriented Conversational Systems

arXiv: https://arxiv.org/abs/1812.10356 · HTML: https://mlsanity.com/paper/1812.10356/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [Learning Interpretable Latent Dialogue Actions With Less Supervision](https://mlsanity.com/paper/2209.11128/) (2022)
- [Recent Progress in Conversational AI](https://mlsanity.com/paper/2204.09719/) (2022)

## Abstract (arXiv)

We propose a novel methodology to address dialog learning in the context of goal-oriented conversational systems. The key idea is to quantize the dialog space into clusters and create a language model across the clusters, thus allowing for an accurate choice of the next utterance in the conversation. The language model relies on n-grams associated with clusters of utterances. This quantized-dialog language model methodology has been applied to the end-to-end goal-oriented track of the latest Dialog System Technology Challenges (DSTC6). The objective is to find the correct system utterance from a pool of candidates in order to complete a dialog between a user and an automated restaurant-reservation system. Our results show that the technique proposed in this paper achieves high accuracy regarding selection of the correct candidate utterance, and outperforms other state-of-the-art approaches based on neural networks.