22 citations · 54 across the 19 of their papers we have counts for
20 papers
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…
Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval
Jinrui Yang, Timothy Baldwin, Trevor Cohn
We present Multi-EuP, a new multilingual benchmark dataset, comprising 22K multi-lingual documents collected from the European Parliament, spanning 24 languages. This dataset is de…
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…
IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks
Xuanli He, Jun Wang, Benjamin Rubinstein +1
Backdoor attacks are an insidious security threat against machine learning models. Adversaries can manipulate the predictions of compromised models by inserting triggers into the t…
Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation
Xuanli He, Qiongkai Xu, Jun Wang +2
Modern NLP models are often trained over large untrusted datasets, raising the potential for a malicious adversary to compromise model behaviour. For instance, backdoors can be imp…
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP
Xudong Han, Timothy Baldwin, Trevor Cohn
Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. However current progress is hampered by a plurality of definitions…
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…
Optimising Equal Opportunity Fairness in Model Training
Aili Shen, Xudong Han, Trevor Cohn +2
Real-world datasets often encode stereotypes and societal biases. Such biases can be implicitly captured by trained models, leading to biased predictions and exacerbating existing…
fairlib: A Unified Framework for Assessing and Improving Classification Fairness
Xudong Han, Aili Shen, Yitong Li +3
This paper presents fairlib, an open-source framework for assessing and improving classification fairness. It provides a systematic framework for quickly reproducing existing basel…
Towards Equal Opportunity Fairness through Adversarial Learning
Xudong Han, Timothy Baldwin, Trevor Cohn
Adversarial training is a common approach for bias mitigation in natural language processing. Although most work on debiasing is motivated by equal opportunity, it is not explicitl…
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…
Balancing out Bias: Achieving Fairness Through Balanced Training
Xudong Han, Timothy Baldwin, Trevor Cohn
Group bias in natural language processing tasks manifests as disparities in system error rates across texts authorized by different demographic groups, typically disadvantaging min…
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation
Kemal Kurniawan, Lea Frermann, Philip Schulz +1
Cross-lingual transfer is a leading technique for parsing low-resource languages in the absence of explicit supervision. Simple `direct transfer' of a learned model based on a mult…
Diverse Adversaries for Mitigating Bias in Training
Xudong Han, Timothy Baldwin, Trevor Cohn
Adversarial learning can learn fairer and less biased models of language than standard methods. However, current adversarial techniques only partially mitigate model bias, added to…
A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning
Chang Xu, Jun Wang, Yuqing Tang +3
As modern neural machine translation (NMT) systems have been widely deployed, their security vulnerabilities require close scrutiny. Most recently, NMT systems have been found vuln…
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning
Philip Arthur, Trevor Cohn, Gholamreza Haffari
We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies. First, wepresent an algorithmic oracle to produce or…