Search Results for author: Gabriela Ferraro

Found 18 papers, 2 papers with code

Transformer Semantic Parsing

no code implementations ALTA 2020 Gabriela Ferraro, Hanna Suominen

In neural semantic parsing, sentences are mapped to meaning representations using encoder-decoder frameworks.

Question Answering Semantic Parsing

Explore BiLSTM-CRF-Based Models for Open Relation Extraction

no code implementations26 Apr 2021 Tao Ni, Qing Wang, Gabriela Ferraro

Extracting multiple relations from text sentences is still a challenge for current Open Relation Extraction (Open RE) tasks.

Relation Extraction

Learning to Continually Learn Rapidly from Few and Noisy Data

1 code implementation6 Mar 2021 Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier, Christian Walder, Gabriela Ferraro, Hanna Suominen

Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution.

Continual Learning Meta-Learning

Highway-Connection Classifier Networks for Plastic yet Stable Continual Learning

no code implementations1 Jan 2021 Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier, Christian Walder, Gabriela Ferraro, Hanna Suominen

Catastrophic forgetting occurs when a neural network is trained sequentially on multiple tasks – its weights will be continuously modified and as a result, the network will lose its ability in solving a previous task.

Continual Learning

MTL2L: A Context Aware Neural Optimiser

1 code implementation18 Jul 2020 Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier, Christian Walder, Gabriela Ferraro, Hanna Suominen

Learning to learn (L2L) trains a meta-learner to assist the learning of a task-specific base learner.

Multi-Task Learning

Lightme: Analysing Language in Internet Support Groups for Mental Health

no code implementations2 Jul 2020 Gabriela Ferraro, Brendan Loo Gee, Shenjia Ji, Luis Salvador-Carulla

Background: Assisting moderators to triage harmful posts in Internet Support Groups is relevant to ensure its safe use.

Text Classification

EINS: Long Short-Term Memory with Extrapolated Input Network Simplification

no code implementations25 Sep 2019 Nicholas I-Hsien Kuo, Mehrtash T. Harandi, Nicolas Fourrier, Gabriela Ferraro, Christian Walder, Hanna Suominen

This paper contrasts the two canonical recurrent neural networks (RNNs) of long short-term memory (LSTM) and gated recurrent unit (GRU) to propose our novel light-weight RNN of Extrapolated Input for Network Simplification (EINS).

Image Generation Imputation +1

Transfer Learning for Hate Speech Detection in Social Media

no code implementations10 Jun 2019 Marian-Andrei Rizoiu, Tianyu Wang, Gabriela Ferraro, Hanna Suominen

Models based on machine learning and natural language processing provide a way to detect this hate speech in web text in order to make discussion forums and other media and platforms safer.

Social and Information Networks Computers and Society

DecayNet: A Study on the Cell States of Long Short Term Memories

no code implementations27 Sep 2018 Nicholas I.H. Kuo, Mehrtash T. Harandi, Hanna Suominen, Nicolas Fourrier, Christian Walder, Gabriela Ferraro

It is unclear whether the extensively applied long-short term memory (LSTM) is an optimised architecture for recurrent neural networks.

Named Entity Recognition for Novel Types by Transfer Learning

no code implementations EMNLP 2016 Lizhen Qu, Gabriela Ferraro, Liyuan Zhou, Weiwei Hou, Timothy Baldwin

In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly.

Named Entity Recognition Transfer Learning

Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks

no code implementations21 Apr 2015 Lizhen Qu, Gabriela Ferraro, Liyuan Zhou, Weiwei Hou, Nathan Schneider, Timothy Baldwin

Word embeddings -- distributed word representations that can be learned from unlabelled data -- have been shown to have high utility in many natural language processing applications.

Chunking NER +3

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