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Found 32 papers, 5 papers with code
An Approach to the Frugal Use of Human Annotators to Scale up Auto-coding for Text Classification Tasks
Driven by the Move-Step analytic framework theorized in the applied linguistics field, our study offers a rigorous approach to the frugal use of two human annotators to scale up auto-coding for text classification tasks.
Scoping natural language processing in Indonesian and Malay for education applications
Indonesian and Malay are underrepresented in the development of natural language processing (NLP) technologies and available resources are difficult to find.
CILex: An Investigation of Context Information for Lexical Substitution Methods
Methods based on lexical resources are likely to miss relevant substitutes whereas relying only on contextual word embedding models fails to provide adequate information on the impact of a substitute in the entire context and the overall meaning of the input.
m-Networks: Adapting the Triplet Networks for Acronym Disambiguation
Acronym disambiguation (AD) is the process of identifying the correct expansion of the acronyms in text.
Pretrained Knowledge Base Embeddings for improved Sentential Relation Extraction
In this work we put forward to combine pretrained knowledge base graph embeddings with transformer based language models to improve performance on the sentential Relation Extraction task in natural language processing.
An Automatic Vowel Space Generator for Language Learner Pronunciation Acquisition and Correction
Speech visualisations are known to help language learners to acquire correct pronunciation and promote a better study experience.
Automatic Gloss Dictionary for Sign Language Learners
A multi-language dictionary is a fundamental tool for language learning, allowing the learner to look up unfamiliar words.
Transformer Semantic Parsing
In neural semantic parsing, sentences are mapped to meaning representations using encoder-decoder frameworks.
Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation
We demonstrate significant performance improvements of up to 29. 6% in all tested motion scenarios in cross-dataset testing on MMPD, even in the presence of dynamic and unconstrained subject motion, emphasizing the benefits of disentangling motion through modeling the 3D facial surface for motion robust facial rPPG estimation.
Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models
In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings.
Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data
This study aims to compare multiple deep learning-based forecasters for the task of predicting volatility using multivariate data.
Designing Less Forgetful Networks for Continual Learning
Neural networks usually excel in learning a single task.
Enhancing Clinical Information Extraction with Transferred Contextual Embeddings
The Bidirectional Encoder Representations from Transformers (BERT) model has achieved the state-of-the-art performance for many natural language processing (NLP) tasks.
Analyzing the Granularity and Cost of Annotation in Clinical Sequence Labeling
Therefore we recommend emphasizing other features, like textual knowledge, for researchers and practitioners as a cost-effective source for increasing the sequence labeling performance.
ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring
Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions.
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.
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation
Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences.
A Token-wise CNN-based Method for Sentence Compression
Sentence compression is a Natural Language Processing (NLP) task aimed at shortening original sentences and preserving their key information.
MTL2L: A Context Aware Neural Optimiser
Learning to learn (L2L) trains a meta-learner to assist the learning of a task-specific base learner.
To compress or not to compress? A Finite-State approach to Nen verbal morphology
While the {`}Chunking{'} model is under half the size of the full de-composed counterpart, the decomposition displays higher structural order.
Applications of Natural Language Processing in Bilingual Language Teaching: An Indonesian-English Case Study
Multilingual corpora are difficult to compile and a classroom setting adds pedagogy to the mix of factors which make this data so rich and problematic to classify.
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).
PostAc : A Visual Interactive Search, Exploration, and Analysis Platform for PhD Intensive Job Postings
Employers{'} low awareness and interest in attracting PhD graduates means that the term {``}PhD{''} is rarely used as a keyword in job advertisements; 80{\%} of companies looking to employ similar researchers do not specifically ask for a PhD qualification.
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.
The Importance of Recommender and Feedback Features in a Pronunciation Learning Aid
An artificial intelligence system can take a role in these guided learning approaches as an enabler of an application for pronunciation learning with a recommender system to guide language learners through exercises and feedback system to correct their pronunciation.
Private Text Classification
Confidential text corpora exist in many forms, but do not allow arbitrary sharing.
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption
This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction.
