Search Results for author:
Found 9 papers, 5 papers with code
Lazy Low-Resource Coreference Resolution: a Study on Leveraging Black-Box Translation Tools
Large annotated corpora for coreference resolution are available for few languages.
Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study
The brittleness of finetuned language model performance on out-of-distribution (OOD) test samples in unseen domains has been well-studied for English, yet is unexplored for multi-lingual models.
Cross-Lingual Sentiment Classification
Sentiment Analysis
+3
Personality Style Recognition via Machine Learning: Identifying Anaclitic and Introjective Personality Styles from Patients' Speech
In disentangling the heterogeneity observed in psychopathology, personality of the patients is considered crucial.
CAW-coref: Conjunction-Aware Word-level Coreference Resolution
State-of-the-art coreference resolutions systems depend on multiple LLM calls per document and are thus prohibitively expensive for many use cases (e. g., information extraction with large corpora).
Ranked #3 on
Coreference Resolution
on OntoNotes
Distractor generation for multiple-choice questions with predictive prompting and large language models
We also show the gains of our approach 1 in generating high-quality distractors by comparing it with a zero-shot ChatGPT and a few-shot ChatGPT prompted with static examples.
Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning
Since performing exercises (including, e. g., practice tests) forms a crucial component of learning, and creating such exercises requires non-trivial effort from the teacher, there is a great value in automatic exercise generation in digital tools in education.
Learning to Reuse Distractors to support Multiple Choice Question Generation in Education
This paper studies how a large existing set of manually created answers and distractors for questions over a variety of domains, subjects, and languages can be leveraged to help teachers in creating new MCQs, by the smart reuse of existing distractors.
EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain
Thus, our versatile dataset can be used for both question and distractor generation, as well as to explore new challenges such as question format conversion.
Predicting Suicide Risk from Online Postings in Reddit The UGent-IDLab submission to the CLPysch 2019 Shared Task A
This paper describes IDLab{'}s text classification systems submitted to Task A as part of the CLPsych 2019 shared task.
