# Fine-Grained Sentence Functions for Short-Text Conversation

arXiv: https://arxiv.org/abs/1907.10302 · HTML: https://mlsanity.com/paper/1907.10302/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Progressive Open-Domain Response Generation with Multiple Controllable Attributes](https://mlsanity.com/paper/2106.14614/) (2021)
- [REAMsharp: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation](https://mlsanity.com/paper/2105.14488/) (2021)
- [Towards End-to-End Open Conversational Machine Reading](https://mlsanity.com/paper/2210.07113/) (2022)
- [A Multi-view Discourse Framework for Integrating Semantic and Syntactic Features in Dialog Agents](https://mlsanity.com/paper/2504.09073/) (2025)

## Abstract (arXiv)

Sentence function is an important linguistic feature referring to a user's purpose in uttering a specific sentence. The use of sentence function has shown promising results to improve the performance of conversation models. However, there is no large conversation dataset annotated with sentence functions. In this work, we collect a new Short-Text Conversation dataset with manually annotated SEntence FUNctions (STC-Sefun). Classification models are trained on this dataset to (i) recognize the sentence function of new data in a large corpus of short-text conversations; (ii) estimate a proper sentence function of the response given a test query. We later train conversation models conditioned on the sentence functions, including information retrieval-based and neural generative models. Experimental results demonstrate that the use of sentence functions can help improve the quality of the returned responses.