Search Results for author: Semere Kiros Bitew

Found 9 papers, 5 papers with code

Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study

no code implementations11 Nov 2023 Maarten De Raedt, Semere Kiros Bitew, Fréderic Godin, Thomas Demeester, Chris Develder

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

CAW-coref: Conjunction-Aware Word-level Coreference Resolution

1 code implementation9 Oct 2023 Karel D'Oosterlinck, Semere Kiros Bitew, Brandon Papineau, Christopher Potts, Thomas Demeester, Chris Develder

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).

coreference-resolution

Distractor generation for multiple-choice questions with predictive prompting and large language models

2 code implementations30 Jul 2023 Semere Kiros Bitew, Johannes Deleu, Chris Develder, Thomas Demeester

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.

Distractor Generation Multiple-choice

Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning

1 code implementation2 Jun 2023 Semere Kiros Bitew, Johannes Deleu, A. Seza Doğruöz, Chris Develder, Thomas Demeester

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

1 code implementation25 Oct 2022 Semere Kiros Bitew, Amir Hadifar, Lucas Sterckx, Johannes Deleu, Chris Develder, Thomas Demeester

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.

Multiple-choice Question Generation +1

EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain

1 code implementation12 Oct 2022 Amir Hadifar, Semere Kiros Bitew, Johannes Deleu, Chris Develder, Thomas Demeester

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.

Distractor Generation Multiple-choice +3

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