# Learning Dynamic Author Representations with Temporal Language Models

arXiv: https://arxiv.org/abs/1909.04985 · HTML: https://mlsanity.com/paper/1909.04985/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

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

- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [Large Human Language Models: A Need and the Challenges](https://mlsanity.com/paper/2312.07751/) (2023)
- [Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input](https://mlsanity.com/paper/2102.09914/) (2021)
- [Human Language Modeling](https://mlsanity.com/paper/2205.05128/) (2022)
- [Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?](https://mlsanity.com/paper/2401.12492/) (2024)

## Abstract (arXiv)

Language models are at the heart of numerous works, notably in the text mining and information retrieval communities. These statistical models aim at extracting word distributions, from simple unigram models to recurrent approaches with latent variables that capture subtle dependencies in texts. However, those models are learned from word sequences only, and authors' identities, as well as publication dates, are seldom considered. We propose a neural model, based on recurrent language modeling, which aims at capturing language diffusion tendencies in author communities through time. By conditioning language models with author and temporal vector states, we are able to leverage the latent dependencies between the text contexts. This allows us to beat several temporal and non-temporal language baselines on two real-world corpora, and to learn meaningful author representations that vary through time.