40 citations · 152 across the 36 of their papers we have counts for
7 papers · 1 filter
Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA
Mikhail Fain, Niall Twomey, Andrey Ponikar +2
We propose a novel non-parametric method for cross-modal recipe retrieval which is applied on top of precomputed image and text embeddings. By combining our method with standard ap…
Transfer Reward Learning for Policy Gradient-Based Text Generation
James O' Neill, Danushka Bollegala
Task-specific scores are often used to optimize for and evaluate the performance of conditional text generation systems. However, such scores are non-differentiable and cannot be u…
Query Obfuscation Semantic Decomposition
Danushka Bollegala, Tomoya Machide, Ken-ichi Kawarabayashi
We propose a method to protect the privacy of search engine users by decomposing the queries using semantically \emph{related} and unrelated \emph{distractor} terms. Instead of a s…
Gender-preserving Debiasing for Pre-trained Word Embeddings
Masahiro Kaneko, Danushka Bollegala
Word embeddings learnt from massive text collections have demonstrated significant levels of discriminative biases such as gender, racial or ethnic biases, which in turn bias the d…
Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition
Thai-Hoang Pham, Khai Mai, Nguyen Minh Trung +4
Fine-Grained Named Entity Recognition (FG-NER) is critical for many NLP applications. While classical named entity recognition (NER) has attracted a substantial amount of research,…
"Touching to See" and "Seeing to Feel": Robotic Cross-modal SensoryData Generation for Visual-Tactile Perception
Jet-Tsyn Lee, Danushka Bollegala, Shan Luo
The integration of visual-tactile stimulus is common while humans performing daily tasks. In contrast, using unimodal visual or tactile perception limits the perceivable dimensiona…
Error-Correcting Neural Sequence Prediction
James O' Neill, Danushka Bollegala
We propose a novel neural sequence prediction method based on \textit{error-correcting output codes} that avoids exact softmax normalization and allows for a tradeoff between speed…