# A Context-aware Natural Language Generator for Dialogue Systems

arXiv: https://arxiv.org/abs/1608.07076 · HTML: https://mlsanity.com/paper/1608.07076/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2016.

## Built on by (most cited first, 13 of 13)

- [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://mlsanity.com/paper/2102.01672/) (2021)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Toxicity Detection can be Sensitive to the Conversational Context](https://mlsanity.com/paper/2111.10223/) (2021)
- [Learning from Failures in Multi-Attempt Reinforcement Learning](https://mlsanity.com/paper/2503.04808/) (2025)
- [Beyond Discrete Personas: Personality Modeling Through Journal Intensive Conversations](https://mlsanity.com/paper/2412.11250/) (2024)
- [A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts](https://mlsanity.com/paper/1907.05346/) (2019)
- [Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning](https://mlsanity.com/paper/2209.07873/) (2022)
- [Context Matters in Semantically Controlled Language Generation for Task-oriented Dialogue Systems](https://mlsanity.com/paper/2111.14119/) (2021)
- [LEEETs-Dial: Linguistic Entrainment in End-to-End Task-oriented Dialogue systems](https://mlsanity.com/paper/2311.09390/) (2023)
- [Estimating Subjective Crowd-Evaluations as an Additional Objective to Improve Natural Language Generation](https://mlsanity.com/paper/2104.05224/) (2021)
- [Recent Progress in Conversational AI](https://mlsanity.com/paper/2204.09719/) (2022)
- [SUMBot: Summarizing Context in Open-Domain Dialogue Systems](https://mlsanity.com/paper/2210.06496/) (2022)
- [Lexical Entrainment for Conversational Systems](https://mlsanity.com/paper/2310.09651/) (2023)

## Abstract (arXiv)

We present a novel natural language generation system for spoken dialogue systems capable of entraining (adapting) to users' way of speaking, providing contextually appropriate responses. The generator is based on recurrent neural networks and the sequence-to-sequence approach. It is fully trainable from data which include preceding context along with responses to be generated. We show that the context-aware generator yields significant improvements over the baseline in both automatic metrics and a human pairwise preference test.