Search Results for author:
Found 24 papers, 7 papers with code
NLPOP: a Dataset for Popularity Prediction of Promoted NLP Research on Twitter
Twitter has slowly but surely established itself as a forum for disseminating, analysing and promoting NLP research.
Claim Check-Worthiness Detection: How Well do LLMs Grasp Annotation Guidelines?
The increasing threat of disinformation calls for automating parts of the fact-checking pipeline.
From Robustness to Improved Generalization and Calibration in Pre-trained Language Models
Enhancing generalization and uncertainty quantification in pre-trained language models (PLMs) is crucial for their effectiveness and reliability.
LLMs for Targeted Sentiment in News Headlines: Exploring Different Levels of Prompt Prescriptiveness
Drawing parallels with annotation paradigms for subjective tasks, we explore the influence of prompt design on the performance of LLMs for TSA of news headlines.
Are ELECTRA's Sentence Embeddings Beyond Repair? The Case of Semantic Textual Similarity
While BERT produces high-quality sentence embeddings, its pre-training computational cost is a significant drawback.
Looking Right is Sometimes Right: Investigating the Capabilities of Decoder-only LLMs for Sequence Labeling
While fine-tuned MLM-based encoders consistently outperform causal language modeling decoders of comparable size, recent decoder-only large language models (LLMs) perform on par with smaller MLM-based encoders.
Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness
Effective out-of-distribution (OOD) detection is crucial for reliable machine learning models, yet most current methods are limited in practical use due to requirements like access to training data or intervention in training.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection
We address the problem of negative transfer in TD by coupling triggers between domains using subject-object relations obtained from a rule-based open information extraction (OIE) system.
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings
Pre-trained language models (PLMs) have ignited a surge in demand for effective fine-tuning techniques, particularly in low-resource domains and languages.
Paragraph-level Citation Recommendation based on Topic Sentences as Queries
Citation recommendation (CR) models may help authors find relevant articles at various stages of the paper writing process.
On Dataset Transferability in Active Learning for Transformers
We link the AL dataset transferability to the similarity of instances queried by the different PLMs and show that AL methods with similar acquisition sequences produce highly transferable datasets regardless of the models used.
Data Augmentation for Neural NLP
Data scarcity is a problem that occurs in languages and tasks where we do not have large amounts of labeled data but want to use state-of-the-art models.
You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis
Expressing attitude or stance toward entities and concepts is an integral part of human behavior and personality.
Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis
Developed to alleviate prohibitive labeling costs, active learning (AL) methods aim to reduce label complexity in supervised learning.
Easy to Decide, Hard to Agree: Reducing Disagreements Between Saliency Methods
By connecting our findings to instance categories based on training dynamics, we show that the agreement of saliency method explanations is very low for easy-to-learn instances.
ALANNO: An Active Learning Annotation System for Mortals
Supervised machine learning has become the cornerstone of today's data-driven society, increasing the need for labeled data.
Large-scale Evaluation of Transformer-based Article Encoders on the Task of Citation Recommendation
As a remedy for the limitations of the existing benchmarks, we propose a new benchmark dataset for evaluating scientific article representations: Multi-Domain Citation Recommendation dataset (MDCR), which covers different scientific fields and contains challenging candidate pools.
A Topic Coverage Approach to Evaluation of Topic Models
When topic models are used for discovery of topics in text collections, a question that arises naturally is how well the model-induced topics correspond to topics of interest to the analyst.
Staying True to Your Word: (How) Can Attention Become Explanation?
The attention mechanism has quickly become ubiquitous in NLP.
PANDORA Talks: Personality and Demographics on Reddit
Personality and demographics are important variables in social sciences, while in NLP they can aid in interpretability and removal of societal biases.
Not Just Depressed: Bipolar Disorder Prediction on Reddit
Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide.
Iterative Recursive Attention Model for Interpretable Sequence Classification
Natural language processing has greatly benefited from the introduction of the attention mechanism.
Leveraging Lexical Substitutes for Unsupervised Word Sense Induction
Word sense induction is the most prominent unsupervised approach to lexical disambiguation.
Social Media Argumentation Mining: The Quest for Deliberateness in Raucousness
Argumentation mining from social media content has attracted increasing attention.
