# Utilizing coarse-grained data in low-data settings for event extraction

arXiv: https://arxiv.org/abs/2205.05468 · HTML: https://mlsanity.com/paper/2205.05468/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens](https://mlsanity.com/paper/1805.02214/) (2018)
- [Financial Event Extraction Using Wikipedia-Based Weak Supervision](https://mlsanity.com/paper/1911.10783/) (2019)
- [ScopeIt: Scoping Task Relevant Sentences in Documents](https://mlsanity.com/paper/2003.04988/) (2020)

## Built on by (most cited first, 1 of 1)

- [Extended Multilingual Protest News Detection -- Shared Task 1, CASE 2021 and 2022](https://mlsanity.com/paper/2211.11360/) (2022)

## Abstract (arXiv)

Annotating text data for event information extraction systems is hard, expensive, and error-prone. We investigate the feasibility of integrating coarse-grained data (document or sentence labels), which is far more feasible to obtain, instead of annotating more documents. We utilize a multi-task model with two auxiliary tasks, document and sentence binary classification, in addition to the main task of token classification. We perform a series of experiments with varying data regimes for the aforementioned integration. Results show that while introducing extra coarse-grained data offers greater improvement and robustness, a gain is still possible with only the addition of negative documents that have no information on any event.