12 citations · 30 across the 7 of their papers we have counts for
5 papers · 1 filter
Enhancing Legal Argument Mining with Domain Pre-training and Neural Networks
Gechuan Zhang, Paul Nulty, David Lillis
The contextual word embedding model, BERT, has proved its ability on downstream tasks with limited quantities of annotated data. BERT and its variants help to reduce the burden of…
Crisis Domain Adaptation Using Sequence-to-sequence Transformers
Congcong Wang, Paul Nulty, David Lillis
User-generated content (UGC) on social media can act as a key source of information for emergency responders in crisis situations. However, due to the volume concerned, computation…
Transformer-based Multi-task Learning for Disaster Tweet Categorisation
Congcong Wang, Paul Nulty, David Lillis
Social media has enabled people to circulate information in a timely fashion, thus motivating people to post messages seeking help during crisis situations. These messages can cont…
Multi-task transfer learning for finding actionable information from crisis-related messages on social media
Congcong Wang, David Lillis
The Incident streams (IS) track is a research challenge aimed at finding important information from social media during crises for emergency response purposes. More specifically, g…
UCD-CS at W-NUT 2020 Shared Task-3: A Text to Text Approach for COVID-19 Event Extraction on Social Media
Congcong Wang, David Lillis
In this paper, we describe our approach in the shared task: COVID-19 event extraction from Twitter. The objective of this task is to extract answers from COVID-related tweets to a…