# Neural Multi-Task Learning for Citation Function and Provenance

arXiv: https://arxiv.org/abs/1811.07351 · HTML: https://mlsanity.com/paper/1811.07351/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2018.

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

- [Why Did You Not Compare With That? Identifying Papers for Use as Baselines](https://mlsanity.com/paper/2201.08089/) (2022)
- [Inline Citation Classification using Peripheral Context and Time-evolving Augmentation](https://mlsanity.com/paper/2303.00344/) (2023)
- [CiteFusion: An Ensemble Framework for Citation Intent Classification Harnessing Dual-Model Binary Couples and SHAP Analyses](https://mlsanity.com/paper/2407.13329/) (2024)
- [Zero-shot data citation function classification using transformer-based large language models (LLMs)](https://mlsanity.com/paper/2511.02936/) (2025)
- [Exploring Motivations for Algorithm Mention in the Domain of Natural Language Processing: A Deep Learning Approach](https://mlsanity.com/paper/2606.29859/) (2026)

## Abstract (arXiv)

Citation function and provenance are two cornerstone tasks in citation analysis. Given a citation, the former task determines its rhetorical role, while the latter locates the text in the cited paper that contains the relevant cited information. We hypothesize that these two tasks are synergistically related, and build a model that validates this claim. For both tasks, we show that a single-layer convolutional neural network (CNN) outperforms existing state-of-the-art baselines. More importantly, we show that the two tasks are indeed synergistic: by jointly training both of the tasks in a multi-task learning setup, we demonstrate additional performance gains. Altogether, our models improve the current state-of-the-arts up to 2%, with statistical significance for both citation function and provenance prediction tasks.