# LSTM based Conversation Models

arXiv: https://arxiv.org/abs/1603.09457 · HTML: https://mlsanity.com/paper/1603.09457/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2016.

## Built on by (most cited first, 3 of 3)

- [Multi-turn Dialogue Comprehension from a Topic-aware Perspective](https://mlsanity.com/paper/2309.09666/) (2023)
- [FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems](https://mlsanity.com/paper/2304.00180/) (2023)
- [Adversarial Conversational Shaping for Intelligent Agents](https://mlsanity.com/paper/2307.11785/) (2023)

## Abstract (arXiv)

In this paper, we present a conversational model that incorporates both context and participant role for two-party conversations. Different architectures are explored for integrating participant role and context information into a Long Short-term Memory (LSTM) language model. The conversational model can function as a language model or a language generation model. Experiments on the Ubuntu Dialog Corpus show that our model can capture multiple turn interaction between participants. The proposed method outperforms a traditional LSTM model as measured by language model perplexity and response ranking. Generated responses show characteristic differences between the two participant roles.