# Emotional Neural Language Generation Grounded in Situational Contexts

arXiv: https://arxiv.org/abs/1911.11161 · HTML: https://mlsanity.com/paper/1911.11161/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

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

- [BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets](https://mlsanity.com/paper/2210.12687/) (2022)
- [Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles](https://mlsanity.com/paper/2206.11184/) (2022)
- [PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Health](https://mlsanity.com/paper/2412.16882/) (2024)
- [Stress-Testing Emotional Support Models: Moving from Homogeneous to Diverse Help Seekers](https://mlsanity.com/paper/2601.07698/) (2026)

## Abstract (arXiv)

Emotional language generation is one of the keys to human-like artificial intelligence. Humans use different type of emotions depending on the situation of the conversation. Emotions also play an important role in mediating the engagement level with conversational partners. However, current conversational agents do not effectively account for emotional content in the language generation process. To address this problem, we develop a language modeling approach that generates affective content when the dialogue is situated in a given context. We use the recently released Empathetic-Dialogues corpus to build our models. Through detailed experiments, we find that our approach outperforms the state-of-the-art method on the perplexity metric by about 5 points and achieves a higher BLEU metric score.