26 citations · 74 across the 49 of their papers we have counts for
11 papers · 1 filter
Improving Unsupervised Constituency Parsing via Maximizing Semantic Information
Junjie Chen, Xiangheng He, Yusuke Miyao +1
Unsupervised constituency parsers organize phrases within a sentence into a tree-shaped syntactic constituent structure that reflects the organization of sentence semantics. Howeve…
Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic Changes
Taichi Aida, Danushka Bollegala
The sense-aware contextualised word embeddings (SCWEs) encode semantic changes of words within the contextualised word embedding (CWE) spaces. Despite the superior performance of S…
Evaluating Short-Term Temporal Fluctuations of Social Biases in Social Media Data and Masked Language Models
Yi Zhou, Danushka Bollegala, Jose Camacho-Collados
Social biases such as gender or racial biases have been reported in language models (LMs), including Masked Language Models (MLMs). Given that MLMs are continuously trained with in…
Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning
Tianhui Zhang, Bei Peng, Danushka Bollegala
Generative Commonsense Reasoning (GCR) requires a model to reason about a situation using commonsense knowledge, while generating coherent sentences. Although the quality of the ge…
Unsupervised Parsing by Searching for Frequent Word Sequences among Sentences with Equivalent Predicate-Argument Structures
Junjie Chen, Xiangheng He, Danushka Bollegala +1
Unsupervised constituency parsing focuses on identifying word sequences that form a syntactic unit (i.e., constituents) in target sentences. Linguists identify the constituent by e…
Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER
Micheal Abaho, Danushka Bollegala, Gary Leeming +2
Adapting language models (LMs) to novel domains is often achieved through fine-tuning a pre-trained LM (PLM) on domain-specific data. Fine-tuning introduces new knowledge into an L…
Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings
Gaifan Zhang, Yi Zhou, Danushka Bollegala
Sentence embeddings produced by Pretrained Language Models (PLMs) have received wide attention from the NLP community due to their superior performance when representing texts in n…
A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection
Taichi Aida, Danushka Bollegala
Detecting temporal semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. Lexical Semantic Change Detection (SCD) ta…
Eagle: Ethical Dataset Given from Real Interactions
Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin
Recent studies have demonstrated that large language models (LLMs) have ethical-related problems such as social biases, lack of moral reasoning, and generation of offensive content…
Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting
Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki +1
There exist both scalable tasks, like reading comprehension and fact-checking, where model performance improves with model size, and unscalable tasks, like arithmetic reasoning and…
The Gaps between Pre-train and Downstream Settings in Bias Evaluation and Debiasing
Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin
The output tendencies of Pre-trained Language Models (PLM) vary markedly before and after Fine-Tuning (FT) due to the updates to the model parameters. These divergences in output t…