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Found 18 papers, 7 papers with code
Lightweight Transformers for Clinical Natural Language Processing
To our knowledge, this is the first comprehensive study specifically focused on creating efficient and compact transformers for clinical NLP tasks.
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture.
On the Effectiveness of Compact Biomedical Transformers
Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks.
Ranked #2 on
Named Entity Recognition (NER)
on BC2GM
SHOMA at Parseme Shared Task on Automatic Identification of VMWEs: Neural Multiword Expression Tagging with High Generalisation
This paper presents a language-independent deep learning architecture adapted to the task of multiword expression (MWE) identification.
MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers
Different strategies have been proposed in the literature to alleviate these problems, with the aim to create effective compact models that nearly match the performance of their bloated counterparts with negligible performance losses.
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression
This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases for both subtasks A and B.
Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints
Processing information locked within clinical health records is a challenging task that remains an active area of research in biomedical NLP.
WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony
This paper describes the systems submitted to SemEval 2018 Task 3 {``}Irony detection in English tweets{''} for both subtasks A and B.
Investigating the Opacity of Verb-Noun Multiword Expression Usages in Context
This study investigates the supervised token-based identification of Multiword Expressions (MWEs).
Combining Multiple Corpora for Readability Assessment for People with Cognitive Disabilities
Given the lack of large user-evaluated corpora in disability-related NLP research (e. g. text simplification or readability assessment for people with cognitive disabilities), the question of choosing suitable training data for NLP models is not straightforward.
Using Gaze Data to Predict Multiword Expressions
In recent years gaze data has been increasingly used to improve and evaluate NLP models due to the fact that it carries information about the cognitive processing of linguistic phenomena.
GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks
This paper describes the system submitted to the SemEval 2019 shared task 1 {`}Cross-lingual Semantic Parsing with UCCA{'}.
Cross-lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions
Recent developments in deep learning have prompted a surge of interest in the application of multitask and transfer learning to NLP problems.
Verbal Multiword Expressions for Identification of Metaphor
Metaphor is a linguistic device in which a concept is expressed by mentioning another.
Privacy-aware Early Detection of COVID-19 through Adversarial Training
One of our main contributions is that we specifically target the development of effective COVID-19 detection models with built-in mechanisms in order to selectively protect sensitive attributes against adversarial attacks.
Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing
In this context, our study investigates the potential of instruction tuning for biomedical language processing, applying this technique to two general LLMs of substantial scale.
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks
The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods.
