22 citations · 48 across the 12 of their papers we have counts for
14 papers
Language models are not naysayers: An analysis of language models on negation benchmarks
Thinh Hung Truong, Timothy Baldwin, Karin Verspoor +1
Negation has been shown to be a major bottleneck for masked language models, such as BERT. However, whether this finding still holds for larger-sized auto-regressive language model…
Systematic Evaluation of Predictive Fairness
Xudong Han, Aili Shen, Trevor Cohn +2
Mitigating bias in training on biased datasets is an important open problem. Several techniques have been proposed, however the typical evaluation regime is very limited, consideri…
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…
Detecting Backdoors in Deep Text Classifiers
You Guo, Jun Wang, Trevor Cohn
Deep neural networks are vulnerable to adversarial attacks, such as backdoor attacks in which a malicious adversary compromises a model during training such that specific behaviour…
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…
Improving negation detection with negation-focused pre-training
Thinh Hung Truong, Timothy Baldwin, Trevor Cohn +1
Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of…