12 citations · 29 across the 9 of their papers we have counts for
11 papers
Automatic Evaluation of Mental Health Stigma in Online Communication
Naomi Baes, Jemima Kang, Nick Haslam +4
Mental health stigma has profoundly harmful impacts but its complexity makes it difficult to evaluate. Stigma may involve explicit derogation, but also subtler forms of blame, fear…
Article and Comment Frames Shape the Quality of Online Comments
Matteo Guida, Yulia Otmakhova, Eduard Hovy +1
Framing theory posits that how information is presented shapes audience responses, but computational work has largely ignored audience reactions. While recent work showed that arti…
FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation
Yulia Otmakhova, Hung Thinh Truong, Rahmad Mahendra +4
We present FLUKE (Framework for LingUistically-driven and tasK-agnostic robustness Evaluation), a framework for assessing model robustness through systematic minimal variations of…
Revisiting subword tokenization: A case study on affixal negation in large language models
Thinh Hung Truong, Yulia Otmakhova, Karin Verspoor +2
In this work, we measure the impact of affixal negation on modern English large language models (LLMs). In affixal negation, the negated meaning is expressed through a negative mor…
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
Lucy Lu Wang, Yulia Otmakhova, Jay DeYoung +4
Evaluating multi-document summarization (MDS) quality is difficult. This is especially true in the case of MDS for biomedical literature reviews, where models must synthesize contr…
Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation
Thinh Hung Truong, Yulia Otmakhova, Timothy Baldwin +3
Negation is poorly captured by current language models, although the extent of this problem is not widely understood. We introduce a natural language inference (NLI) test suite to…
LED down the rabbit hole: exploring the potential of global attention for biomedical multi-document summarisation
Yulia Otmakhova, Hung Thinh Truong, Timothy Baldwin +3
In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task. Specifically, we adapt PRIMERA (Xiao et al., 2022) to the bio…
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
Qingyu Chen, Alexis Allot, Robert Leaman +35
The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design…
ITTC @ TREC 2021 Clinical Trials Track
Thinh Hung Truong, Yulia Otmakhova, Rahmad Mahendra +6
This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cogniti…
Impact of detecting clinical trial elements in exploration of COVID-19 literature
Simon Šuster, Karin Verspoor, Timothy Baldwin +4
The COVID-19 pandemic has driven ever-greater demand for tools which enable efficient exploration of biomedical literature. Although semi-structured information resulting from conc…
COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research
Karin Verspoor, Simon Šuster, Yulia Otmakhova +7
We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language…