Search Results for author:
Found 6 papers, 3 papers with code
Can Domain Pre-training Help Interdisciplinary Researchers from Data Annotation Poverty? A Case Study of Legal Argument Mining with BERT-based Transformers
Our case study focuses on: the comparison of general pre-training and domain pre-training; the generalisability of different domain pre-trained transformers; and the potential of merging general pre-training with domain pre-training.
Enhancing Legal Argument Mining with Domain Pre-training and Neural Networks
In this paper, we provide a broad study of both classic and contextual embedding models and their performance on practical case law from the European Court of Human Rights (ECHR).
Crisis Domain Adaptation Using Sequence-to-sequence Transformers
In this paper, we investigate how this prior knowledge can be best leveraged for new crises by examining the extent to which crisis events of a similar type are more suitable for adaptation to new events (cross-domain adaptation).
Transformer-based Multi-task Learning for Disaster Tweet Categorisation
Social media has enabled people to circulate information in a timely fashion, thus motivating people to post messages seeking help during crisis situations.
The UCD-Net System at SemEval-2020 Task 1: Temporal Referencing with Semantic Network Distances
This paper describes the UCD system entered for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection.
