# A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts

arXiv: https://arxiv.org/abs/2204.02712 · HTML: https://mlsanity.com/paper/2204.02712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 17 of 17)

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- [A Survey on Text Classification: From Shallow to Deep Learning](https://mlsanity.com/paper/2008.00364/) (2020)
- [Transfer Fine-Tuning: A BERT Case Study](https://mlsanity.com/paper/1909.00931/) (2019)
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- [Information search in a professional context - exploring a collection of professional search tasks](https://mlsanity.com/paper/1905.04577/) (2019)

## Abstract (arXiv)

Recent progress in natural language processing has been impressive in many different areas with transformer-based approaches setting new benchmarks for a wide range of applications. This development has also lowered the barriers for people outside the NLP community to tap into the tools and resources applied to a variety of domain-specific applications. The bottleneck however still remains the lack of annotated gold-standard collections as soon as one's research or professional interest falls outside the scope of what is readily available. One such area is genocide-related research (also including the work of experts who have a professional interest in accessing, exploring and searching large-scale document collections on the topic, such as lawyers). We present GTC (Genocide Transcript Corpus), the first annotated corpus of genocide-related court transcripts which serves three purposes: (1) to provide a first reference corpus for the community, (2) to establish benchmark performances (using state-of-the-art transformer-based approaches) for the new classification task of paragraph identification of violence-related witness statements, (3) to explore first steps towards transfer learning within the domain. We consider our contribution to be addressing in particular this year's hot topic on Language Technology for All.