no code implementations • NAACL (CMCL) 2021 • Steven Derby, Paul Miller, Barry Devereux
Furthermore, in order to make more meaningful comparisons with theories of human language comprehension in psycholinguistics, we focus on two key stages where the meaning of a particular target word may arise: immediately before the word’s presentation to the model (comparable to forward inferencing), and immediately after the word token has been input into the network.
no code implementations • 21 Sep 2023 • Elena Shushkevich, Long Mai, Manuel V. Loureiro, Steven Derby, Tri Kurniawan Wijaya
Nowadays, the use of intelligent systems to detect redundant information in news articles has become especially prevalent with the proliferation of news media outlets in order to enhance user experience.
1 code implementation • 24 Feb 2023 • Congcong Wang, Gonzalo Fiz Pontiveros, Steven Derby, Tri Kurniawan Wijaya
Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving natural language problems difficult.
no code implementations • 6 Jan 2023 • Manuel V. Loureiro, Steven Derby, Tri Kurniawan Wijaya
We demonstrate that our approach consistently outperforms other state-of-the-art topic models across coherency metrics and find that the explicit knowledge encoded in the graph-based embeddings provides more coherent topics than the implicit knowledge encoded with the contextualized embeddings of language models.
1 code implementation • 22 Dec 2022 • Víctor Suárez-Paniagua, Steven Derby, Tri Kurniawan Wijaya
To evaluate the effectiveness of our approach, and due to the lack of datasets in this area, we also contribute to the research community with a gold-standard multilingual news-location dataset, NewsLOC.
no code implementations • COLING 2020 • Steven Derby, Paul Miller, Barry Devereux
Semantic models derived from visual information have helped to overcome some of the limitations of solely text-based distributional semantic models.
1 code implementation • CONLL 2020 • Steven Derby, Paul Miller, Barry Devereux
Researchers have recently demonstrated that tying the neural weights between the input look-up table and the output classification layer can improve training and lower perplexity on sequence learning tasks such as language modelling.
1 code implementation • IJCNLP 2019 • Steven Derby, Paul Miller, Barry Devereux
We propose a method for mapping human property knowledge onto a distributional semantic space, which adapts the word2vec architecture to the task of modelling concept features.
no code implementations • WS 2018 • Steven Derby, Paul Miller, Brian Murphy, Barry Devereux
Performance in language modelling has been significantly improved by training recurrent neural networks on large corpora.
no code implementations • CONLL 2018 • Steven Derby, Paul Miller, Brian Murphy, Barry Devereux
In this paper, we combine multimodal information from both text and image-based representations derived from state-of-the-art distributional models to produce sparse, interpretable vectors using Joint Non-Negative Sparse Embedding.