32 citations · 300 across the 54 of their papers we have counts for
65 papers
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
Neha Sengupta, Sunil Kumar Sahu, Bokang Jia +29
We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-…
Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings
Chen Cecilia Liu, Fajri Koto, Timothy Baldwin +1
Large language models (LLMs) are highly adept at question answering and reasoning tasks, but when reasoning in a situational context, human expectations vary depending on the relev…
Collective Human Opinions in Semantic Textual Similarity
Yuxia Wang, Shimin Tao, Ning Xie +3
Despite the subjective nature of semantic textual similarity (STS) and pervasive disagreements in STS annotation, existing benchmarks have used averaged human ratings as the gold s…
Unsupervised Paraphrasing of Multiword Expressions
Takashi Wada, Yuji Matsumoto, Timothy Baldwin +1
We propose an unsupervised approach to paraphrasing multiword expressions (MWEs) in context. Our model employs only monolingual corpus data and pre-trained language models (without…
Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation
Haonan Li, Fajri Koto, Minghao Wu +2
Instruction tuning has shown great promise in improving the performance of large language models. However, research on multilingual instruction tuning has been limited due to the s…
NusaCrowd: Open Source Initiative for Indonesian NLP Resources
Samuel Cahyawijaya, Holy Lovenia, Alham Fikri Aji +44
We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Thro…
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…
Unsupervised Lexical Substitution with Decontextualised Embeddings
Takashi Wada, Timothy Baldwin, Yuji Matsumoto +1
We propose a new unsupervised method for lexical substitution using pre-trained language models. Compared to previous approaches that use the generative capability of language mode…
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…
NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages
Genta Indra Winata, Alham Fikri Aji, Samuel Cahyawijaya +11
Natural language processing (NLP) has a significant impact on society via technologies such as machine translation and search engines. Despite its success, NLP technology is only w…
One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia
Alham Fikri Aji, Genta Indra Winata, Fajri Koto +9
NLP research is impeded by a lack of resources and awareness of the challenges presented by underrepresented languages and dialects. Focusing on the languages spoken in Indonesia,…
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…
Contrastive Learning for Fair Representations
Aili Shen, Xudong Han, Trevor Cohn +2
Trained classification models can unintentionally lead to biased representations and predictions, which can reinforce societal preconceptions and stereotypes. Existing debiasing me…
Fairness-aware Class Imbalanced Learning
Shivashankar Subramanian, Afshin Rahimi, Timothy Baldwin +2
Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the…
Evaluating Debiasing Techniques for Intersectional Biases
Shivashankar Subramanian, Xudong Han, Timothy Baldwin +2
Bias is pervasive in NLP models, motivating the development of automatic debiasing techniques. Evaluation of NLP debiasing methods has largely been limited to binary attributes in…
KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning
Haonan Li, Yeyun Gong, Jian Jiao +3
Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural languag…
Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP
Anna Rogers, Tim Baldwin, Kobi Leins
A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core…
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization
Fajri Koto, Jey Han Lau, Timothy Baldwin
We present IndoBERTweet, the first large-scale pretrained model for Indonesian Twitter that is trained by extending a monolingually-trained Indonesian BERT model with additive doma…
Automatic Claim Review for Climate Science via Explanation Generation
Shraey Bhatia, Jey Han Lau, Timothy Baldwin
There is unison is the scientific community about human induced climate change. Despite this, we see the web awash with claims around climate change scepticism, thus driving the ne…
Evaluating the Efficacy of Summarization Evaluation across Languages
Fajri Koto, Jey Han Lau, Timothy Baldwin
While automatic summarization evaluation methods developed for English are routinely applied to other languages, this is the first attempt to systematically quantify their panlingu…
Evaluating Document Coherence Modelling
Aili Shen, Meladel Mistica, Bahar Salehi +3
While pretrained language models ("LM") have driven impressive gains over morpho-syntactic and semantic tasks, their ability to model discourse and pragmatic phenomena is less clea…
Top-down Discourse Parsing via Sequence Labelling
Fajri Koto, Jey Han Lau, Timothy Baldwin
We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020). By framing the task as a sequ…
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…
Liputan6: A Large-scale Indonesian Dataset for Text Summarization
Fajri Koto, Jey Han Lau, Timothy Baldwin
In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from Liputan6.com, an online news portal, and obtain 215,827 document-summary pairs.…