Shenbin Qian, Archchana Sindhujan, Minnie Kabra, Diptesh Kanojia, Constantin Orasan, Tharindu Ranasinghe, Fred Blain. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

The goal of information extraction is to extract structural knowledge (such as entities, relations and events) from plain and unstructured texts. Information extraction in legal documents has recently gained a lot of attention in the natural language processing (NLP) community due to the high demand for efficient information extraction for legal practitioners and companies. Given that the legal documents are unique and their processing is challenging, there is a pressing need for applications of NLP techniques to tackle these challenges. In this research, we present a survey on the recent advancements in legal information extraction focusing on three tasks: named entity recognition, relationship extraction and event detection. We report language resources and systems in multiple jurisdictions and languages for each task. Based on the thorough review conducted, we identify insights into the techniques employed and promising research directions that merit further exploration in future studies. We maintain a public repository and consistently update related resources at https://github.com/DamithDR/legalinformationextraction .

Machine translation (MT) is widely used to translate content on social media platforms aiming to improve accessibility. A great part of the content circulated on social media is user-generated and often contains non-standard spelling, hashtags, and emojis that pose challenges to MT systems. This leads to many mistranslated instances that are presented to users of these platforms, hindering their understanding of content written in other languages. In this paper, we investigate the impact of MT on offensive language identification. We pose that MT and potential mistranslations have an important and mostly under-explored impact on social media tasks such as sentiment analysis and offensive language identification. We create MT-Offense, a novel dataset containing English originals and translations in Arabic, Hindi, Marathi, Sinhala, and Spanish produced by multiple open-access Neural Machine Translation systems. We evaluate the performance of various offensive language models on both original and MT content in different training and test set combinations. We report the F1 scores of the models. Our results show that (1) offensive language identification models perform better on original data than on MT data, and (2) the use of MT data in training helps models better identify offensive language in MT content compared to models trained exclusively on original data.

Deepak Pandita, Tharindu Cyril Weerasooriya, Sujan Dutta, Sarah K. K. Luger, Tharindu Ranasinghe, Ashiqur R. KhudaBukhsh, Marcos Zampieri, Christopher M Homan. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024.

Tharindu Ranasinghe, Hansi Hettiarachchi, Constantin Orasan, Ruslan Mitkov. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2025.

Tharindu Ranasinghe, Hansi Hettiarachchi, Nadeesha Chathurangi Naradde Vidana Pathirana, Damith Premasiri, Lasitha Uyangodage, Isuri Nanomi Arachchige, Alistair Plum, Paul Rayson, Ruslan Mitkov. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.