Danushka Bollegala

research

∙ 09/19/2023

A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings

We propose a Neighbourhood-Aware Differential Privacy (NADP) mechanism c...

0 Danushka Bollegala, et al. ∙

research

∙ 09/16/2023

The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated

Pre-trained language models trained on large-scale data have learned ser...

0 Masahiro Kaneko, et al. ∙

research

∙ 09/13/2023

In-Contextual Bias Suppression for Large Language Models

Despite their impressive performance in a wide range of NLP tasks, Large...

0 Daisuke Oba, et al. ∙

research

∙ 09/12/2023

Learning to Predict Concept Ordering for Common Sense Generation

Prior work has shown that the ordering in which concepts are shown to a ...

0 Tianhui Zhang, et al. ∙

research

∙ 07/14/2023

Learn from Incomplete Tactile Data: Tactile Representation Learning with Masked Autoencoders

The missing signal caused by the objects being occluded or an unstable s...

0 Guanqun Cao, et al. ∙

research

∙ 06/22/2023

Multimodal Zero-Shot Learning for Tactile Texture Recognition

Tactile sensing plays an irreplaceable role in robotic material recognit...

0 Guanqun Cao, et al. ∙

research

∙ 05/25/2023

Metrics for quantifying isotropy in high dimensional unsupervised clustering tasks in a materials context

Clustering is a common task in machine learning, but clusters of unlabel...

0 Samantha Durdy, et al. ∙

research

∙ 05/15/2023

Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings

Languages are dynamic entities, where the meanings associated with words...

0 Taichi Aida, et al. ∙

research

∙ 02/11/2023

Evaluating the Robustness of Discrete Prompts

Discrete prompts have been used for fine-tuning Pre-trained Language Mod...

0 Yoichi Ishibashi, et al. ∙

research

∙ 01/28/2023

Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples

Numerous types of social biases have been identified in pre-trained lang...

0 Masahiro Kaneko, et al. ∙

research

∙ 01/17/2023

Vis2Hap: Vision-based Haptic Rendering by Cross-modal Generation

To assist robots in teleoperation tasks, haptic rendering which allows h...

0 Guanqun Cao, et al. ∙

research

∙ 10/06/2022

Debiasing isn't enough! – On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Tasks

We study the relationship between task-agnostic intrinsic and task-speci...

4 Masahiro Kaneko, et al. ∙

research

∙ 06/17/2022

Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties

With machine learning being a popular topic in current computational mat...

0 Samantha Durdy, et al. ∙

research

∙ 05/19/2022

Gender Bias in Meta-Embeddings

Combining multiple source embeddings to create meta-embeddings is consid...

6 Masahiro Kaneko, et al. ∙

research

∙ 04/27/2022

Learning to Borrow – Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion

Prior work on integrating text corpora with knowledge graphs (KGs) to im...

0 Huda Hakami, et al. ∙

research

∙ 04/26/2022

Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings

Given multiple source word embeddings learnt using diverse algorithms an...

0 Danushka Bollegala, et al. ∙

research

∙ 04/25/2022

A Survey on Word Meta-Embedding Learning

Meta-embedding (ME) learning is an emerging approach that attempts to le...

0 Danushka Bollegala, et al. ∙

research

∙ 04/16/2022

Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models

A variety of contextualised language models have been proposed in the NL...

0 Keigo Takahashi, et al. ∙

research

∙ 03/14/2022

Sense Embeddings are also Biased–Evaluating Social Biases in Static and Contextualised Sense Embeddings

Sense embedding learning methods learn different embeddings for the diff...

9 Yi Zhou, et al. ∙

research

∙ 10/05/2021

Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy

Contextualised word embeddings generated from Neural Language Models (NL...

0 Yi Zhou, et al. ∙

research

∙ 06/15/2021

Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance

This paper presents a novel unsupervised abstractive summarization metho...

0 Masaru Isonuma, et al. ∙

research

∙ 05/11/2021

Backretrieval: An Image-Pivoted Evaluation Metric for Cross-Lingual Text Representations Without Parallel Corpora

Cross-lingual text representations have gained popularity lately and act...

3 Mikhail Fain, et al. ∙

research

∙ 04/15/2021

Detect and Classify – Joint Span Detection and Classification for Health Outcomes

A health outcome is a measurement or an observation used to capture and ...

0 Michael Abaho, et al. ∙

research

∙ 04/15/2021

Unmasking the Mask – Evaluating Social Biases in Masked Language Models

Masked Language Models (MLMs) have shown superior performances in numero...

0 Masahiro Kaneko, et al. ∙

research

∙ 04/14/2021

I Wish I Would Have Loved This One, But I Didn't – A Multilingual Dataset for Counterfactual Detection in Product Reviews

Counterfactual statements describe events that did not or cannot take pl...

0 James O'Neill, et al. ∙

research

∙ 02/12/2021

Semantically-Conditioned Negative Samples for Efficient Contrastive Learning

Negative sampling is a limiting factor w.r.t. the generalization of metr...

26 James O'Neill, et al. ∙

research

∙ 01/25/2021

RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding

Embedding entities and relations of a knowledge graph in a low-dimension...

13 Danushka Bollegala, et al. ∙

research

∙ 01/23/2021

Dictionary-based Debiasing of Pre-trained Word Embeddings

Word embeddings trained on large corpora have shown to encode high level...

0 Masahiro Kaneko, et al. ∙

research

∙ 01/23/2021

Debiasing Pre-trained Contextualised Embeddings

In comparison to the numerous debiasing methods proposed for the static ...

0 Masahiro Kaneko, et al. ∙

research

∙ 01/22/2021

k-Neighbor Based Curriculum Sampling for Sequence Prediction

Multi-step ahead prediction in language models is challenging due to the...

0 James O'Neill, et al. ∙

research

∙ 10/25/2020

Autoencoding Improves Pre-trained Word Embeddings

Prior work investigating the geometry of pre-trained word embeddings hav...

0 Masahiro Kaneko, et al. ∙

research

∙ 08/10/2020

Spatio-temporal Attention Model for Tactile Texture Recognition

Recently, tactile sensing has attracted great interest in robotics, espe...

7 Guanqun Cao, et al. ∙

research

∙ 05/12/2020

Do not let the history haunt you – Mitigating Compounding Errors in Conversational Question Answering

The Conversational Question Answering (CoQA) task involves answering a s...

0 Angrosh Mandya, et al. ∙

research

∙ 05/06/2020

Weakly-Supervised Neural Response Selection from an Ensemble of Task-Specialised Dialogue Agents

Dialogue engines that incorporate different types of agents to converse ...

5 Asir Saeed, et al. ∙

research

∙ 04/22/2020

Contextualised Graph Attention for Improved Relation Extraction

This paper presents a contextualized graph attention network that combin...

0 Angrosh Mandya, et al. ∙

research

∙ 04/14/2020

Multi-source Attention for Unsupervised Domain Adaptation

Domain adaptation considers the problem of generalising a model learnt u...

0 Xia Cui, et al. ∙

research

∙ 02/25/2020

Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction

Language-independent tokenisation (LIT) methods that do not require labe...

3 Danushka Bollegala, et al. ∙

research

∙ 11/28/2019

Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA

We propose a novel non-parametric method for cross-modal retrieval which...

0 Mikhail Fain, et al. ∙

research

∙ 09/12/2019

Anonymising Queries by Semantic Decomposition

Protecting the privacy of search engine users is an important requiremen...

0 Danushka Bollegala, et al. ∙

research

∙ 09/09/2019

Transfer Reward Learning for Policy Gradient-Based Text Generation

Task-specific scores are often used to optimize for and evaluate the per...

6 James O'Neill, et al. ∙

research

∙ 06/03/2019

Gender-preserving Debiasing for Pre-trained Word Embeddings

Word embeddings learnt from massive text collections have demonstrated s...

0 Masahiro Kaneko, et al. ∙

research

∙ 02/17/2019

"Touching to See" and "Seeing to Feel": Robotic Cross-modal SensoryData Generation for Visual-Tactile Perception

The integration of visual-tactile stimulus is common while humans perfor...

0 Jet-Tsyn Lee, et al. ∙

research

∙ 01/21/2019

Error-Correcting Neural Sequence Prediction

In this paper we propose a novel neural language modelling (NLM) method ...

2 James O'Neill, et al. ∙

research

∙ 11/02/2018

Analysing Dropout and Compounding Errors in Neural Language Models

This paper carries out an empirical analysis of various dropout techniqu...

4 James O'Neill, et al. ∙

research

∙ 11/02/2018

Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction

We propose in this paper a combined model of Long Short Term Memory and ...

0 Angrosh Mandya, et al. ∙

research

∙ 09/16/2018

Curriculum-Based Neighborhood Sampling For Sequence Prediction

The task of multi-step ahead prediction in language models is challengin...

0 James O'Neill, et al. ∙

research

∙ 09/16/2018

Semi-Supervised Multi-Task Word Embeddings

Word embeddings have been shown to benefit from ensembling several word ...

0 James O'Neill, et al. ∙

research

∙ 08/13/2018

Angular-Based Word Meta-Embedding Learning

Ensembling word embeddings to improve distributed word representations h...

2 James O'Neill, et al. ∙

research

∙ 04/14/2018

Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings

Creating accurate meta-embeddings from pre-trained source embeddings has...

0 Joshua Coates, et al. ∙

research

∙ 04/14/2018

ClassiNet -- Predicting Missing Features for Short-Text Classification

The fundamental problem in short-text classification is feature sparsene...

0 Danushka Bollegala, et al. ∙

Danushka Bollegala

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