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Found 18 papers, 2 papers with code
Transformer Semantic Parsing
In neural semantic parsing, sentences are mapped to meaning representations using encoder-decoder frameworks.
Designing Less Forgetful Networks for Continual Learning
Neural networks usually excel in learning a single task.
Explore BiLSTM-CRF-Based Models for Open Relation Extraction
Extracting multiple relations from text sentences is still a challenge for current Open Relation Extraction (Open RE) tasks.
Learning to Continually Learn Rapidly from Few and Noisy Data
Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution.
Highway-Connection Classifier Networks for Plastic yet Stable Continual Learning
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.
MTL2L: A Context Aware Neural Optimiser
Learning to learn (L2L) trains a meta-learner to assist the learning of a task-specific base learner.
Lightme: Analysing Language in Internet Support Groups for Mental Health
Background: Assisting moderators to triage harmful posts in Internet Support Groups is relevant to ensure its safe use.
EINS: Long Short-Term Memory with Extrapolated Input Network Simplification
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).
Transfer Learning for Hate Speech Detection in Social Media
This paper uses a transfer learning technique to leverage two independent datasets jointly and builds a single representation of hate speech.
Social and Information Networks Computers and Society
DecayNet: A Study on the Cell States of Long Short Term Memories
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
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.
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks
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.
