# Attention with Intention for a Neural Network Conversation Model

arXiv: https://arxiv.org/abs/1510.08565 · HTML: https://mlsanity.com/paper/1510.08565/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2015.

## Built on by (most cited first, 1 of 1)

- [Ericson: An Interactive Open-Domain Conversational Search Agent](https://mlsanity.com/paper/2304.02233/) (2023)

## Abstract (arXiv)

In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the intention process. The decoder network is a recurrent network produces responses to the input from the source side. It is a language model that is dependent on the intention and has an attention mechanism to attend to particular source side words, when predicting a symbol in the response. The model is trained end-to-end without labeling data. Experiments show that this model generates natural responses to user inputs.