# Improving Conditioning in Context-Aware Sequence to Sequence Models

arXiv: https://arxiv.org/abs/1911.09728 · HTML: https://mlsanity.com/paper/1911.09728/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity](https://mlsanity.com/paper/2112.05843/) (2021)
- [Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey](https://mlsanity.com/paper/2411.09166/) (2024)
- [Policy-driven Knowledge Selection and Response Generation for Document-grounded Dialogue](https://mlsanity.com/paper/2410.15970/) (2024)
- [Structures Facilitate Retrieve, Rerank, and Generate](https://mlsanity.com/paper/2606.03247/) (2026)

## Abstract (arXiv)

Neural sequence to sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus on cases where generation is conditioned on both a short query and a long context, such as abstractive question answering or document-level translation. We modify the standard sequence-to-sequence approach to make better use of both the query and the context by expanding the conditioning mechanism to intertwine query and context attention. We also introduce a simple and efficient data augmentation method for the proposed model. Experiments on three different tasks show that both changes lead to consistent improvements.