# Listening between the Lines: Learning Personal Attributes from Conversations

arXiv: https://arxiv.org/abs/1904.10887 · HTML: https://mlsanity.com/paper/1904.10887/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 16 of 16)

- [Conversational Entity Linking: Problem Definition and Datasets](https://mlsanity.com/paper/2105.04903/) (2021)
- [You Don't Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers' Private Personas](https://mlsanity.com/paper/2205.10228/) (2022)
- [An Ecosystem for Personal Knowledge Graphs: A Survey and Research Roadmap](https://mlsanity.com/paper/2304.09572/) (2023)
- [A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles](https://mlsanity.com/paper/2102.08322/) (2021)
- [Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis](https://mlsanity.com/paper/2104.11612/) (2021)
- [Extracting and Inferring Personal Attributes from Dialogue](https://mlsanity.com/paper/2109.12702/) (2021)
- [NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human](https://mlsanity.com/paper/2406.03749/) (2024)
- [A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots](https://mlsanity.com/paper/2401.00609/) (2023)
- [Personal Entity, Concept, and Named Entity Linking in Conversations](https://mlsanity.com/paper/2206.07836/) (2022)
- [Personal Attribute Prediction from Conversations](https://mlsanity.com/paper/2209.09619/) (2022)
- [Automatic Prompt Optimization for Dataset-Level Feature Discovery](https://mlsanity.com/paper/2601.13922/) (2026)
- [Low-resource Personal Attribute Prediction from Conversation](https://mlsanity.com/paper/2211.15324/) (2022)
- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)
- [Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey](https://mlsanity.com/paper/2411.09166/) (2024)
- [Speaker Profiling in Multiparty Conversations](https://mlsanity.com/paper/2304.08801/) (2023)
- [SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems](https://mlsanity.com/paper/2610.18308/) (2026)

## Abstract (arXiv)

Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition of such knowledge, for personalization in downstream Web applications, by extracting personal attributes from conversations. This problem is more challenging than the established task of information extraction from scientific publications or Wikipedia articles, because dialogues often give merely implicit cues about the speaker. We propose methods for inferring personal attributes, such as profession, age or family status, from conversations using deep learning. Specifically, we propose several Hidden Attribute Models, which are neural networks leveraging attention mechanisms and embeddings. Our methods are trained on a per-predicate basis to output rankings of object values for a given subject-predicate combination (e.g., ranking the doctor and nurse professions high when speakers talk about patients, emergency rooms, etc). Experiments with various conversational texts including Reddit discussions, movie scripts and a collection of crowdsourced personal dialogues demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.