📰 BEA 2024: all accepted papers

58 papers · the most cited first · ← BEA 2024

1. A World CLASSE Student Summary Corpus ↗Scott Crossley, Perpetual Baffour, Mihai Dascalu et al.
2. ANU at MLSP-2024: Prompt-based Lexical Simplification for English and Sinhala ↗Sandaru Seneviratne, Hanna Suominen
3. Anna Karenina Strikes Again: Pre-Trained LLM Embeddings May Favor High-Performing Learners ↗Abigail Gurin Schleifer, Beata Beigman Klebanov, Moriah Ariely et al.
4. Archaeology at MLSP 2024: Machine Translation for Lexical Complexity Prediction and Lexical Simplification ↗Petru Cristea, Sergiu Nisioi
5. Assessing Student Explanations with Large Language Models Using Fine-Tuning and Few-Shot Learning ↗Dan Carpenter, Wookhee Min, Seung Lee et al.
6. Automated Essay Scoring Using Grammatical Variety and Errors with Multi-Task Learning and Item Response Theory ↗Kosuke Doi, Katsuhito Sudoh, Satoshi Nakamura
7. Automated Evaluation of Teacher Encouragement of Student-to-Student Interactions in a Simulated Classroom Discussion ↗Michael Ilagan, Beata Beigman Klebanov, Jamie Mikeska
8. Automated Scoring of Clinical Patient Notes: Findings From the Kaggle Competition and Their Translation into Practice ↗Victoria Yaneva, King Yiu Suen, Le An Ha et al.
9. Automated Sentence Generation for a Spaced Repetition Software ↗Benjamin Paddags, Daniel Hershcovich, Valkyrie Savage
10. Automatic Crossword Clues Extraction for Language Learning ↗Santiago Berruti, Arturo Collazo, Diego Sellanes et al.
11. BERT-IRT: Accelerating Item Piloting with BERT Embeddings and Explainable IRT Models ↗Kevin P. Yancey, Andrew Runge, Geoffrey LaFlair et al.
12. Beyond Flesch-Kincaid: Prompt-based Metrics Improve Difficulty Classification of Educational Texts ↗Donya Rooein, Paul Röttger, Anastassia Shaitarova et al.
13. Building Robust Content Scoring Models for Student Explanations of Social Justice Science Issues ↗Allison Bradford, Kenneth Steimel, Brian Riordan et al.
14. Can GPT-4 do L2 analytic assessment? ↗Stefano Bannò, Hari K. Vydana, Kate M. Knill et al.
15. Can Language Models Guess Your Identity? Analyzing Demographic Biases in AI Essay Scoring ↗Alexander Kwako, Christopher Ormerod
16. Error Tracing in Programming: A Path to Personalised Feedback ↗Martha Shaka, Diego Carraro, Kenneth Brown
17. Evaluating Vocabulary Usage in LLMs ↗Matthew Durward, Christopher Thomson
18. Explainable AI in Language Learning: Linking Empirical Evidence and Theoretical Concepts in Proficiency and Readability Modeling of Portuguese ↗Luisa Ribeiro-Flucht, Xiaobin Chen, Detmar Meurers
19. Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation ↗Maja Stahl, Leon Biermann, Andreas Nehring et al.
20. Fairness in Automated Essay Scoring: A Comparative Analysis of Algorithms on German Learner Essays from Secondary Education ↗Nils-Jonathan Schaller, Yuning Ding, Andrea Horbach et al.
21. Findings from the First Shared Task on Automated Prediction of Difficulty and Response Time for Multiple-Choice Questions ↗Victoria Yaneva, Kai North, Peter Baldwin et al.
22. From Miscue to Evidence of Difficulty: Analysis of Automatically Detected Miscues in Oral Reading for Feedback Potential ↗Beata Beigman Klebanov, Michael Suhan, Tenaha O'Reilly et al.
23. GMU at MLSP 2024: Multilingual Lexical Simplification with Transformer Models ↗Dhiman Goswami, Kai North, Marcos Zampieri
24. Harnessing GPT to Study Second Language Learner Essays: Can We Use Perplexity to Determine Linguistic Competence? ↗Ricardo Muñoz Sánchez, Simon Dobnik, Elena Volodina
25. How Good are Modern LLMs in Generating Relevant and High-Quality Questions at Different Bloom's Skill Levels for Indian High School Social Science Curriculum? ↗Nicy Scaria, Suma Dharani Chenna, Deepak Subramani
26. ISEP_Presidency_University at MLSP 2024 Shared Task: Using GPT-3.5 to Generate Substitutes for Lexical Simplification ↗Benjamin Dutilleul, Mathis Debaillon, Sandeep Mathias
27. ITEC at BEA 2024 Shared Task: Predicting Difficulty and Response Time of Medical Exam Questions with Statistical, Machine Learning, and Language Models ↗Anaïs Tack, Siem Buseyne, Changsheng Chen et al.
28. ITEC at MLSP 2024: Transferring Predictions of Lexical Difficulty from Non-Native Readers ↗Anaïs Tack
29. Identifying Fairness Issues in Automatically Generated Testing Content ↗Kevin Stowe, Benny Longwill, Alyssa Francis et al.
30. Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank ↗Alexander Scarlatos, Wanyong Feng, Digory Smith et al.
31. Improving Readability Assessment with Ordinal Log-Loss ↗Ho Hung Lim, John Lee
32. Improving Socratic Question Generation using Data Augmentation and Preference Optimization ↗Nischal Ashok Kumar, Andrew Lan
33. Improving Transfer Learning for Early Forecasting of Academic Performance by Contextualizing Language Models ↗Ahatsham Hayat, Bilal Khan, Mohammad Hasan
34. Item Difficulty and Response Time Prediction with Large Language Models: An Empirical Analysis of USMLE Items ↗Okan Bulut, Guher Gorgun, Bin Tan
35. LLMs in Short Answer Scoring: Limitations and Promise of Zero-Shot and Few-Shot Approaches ↗Imran Chamieh, Torsten Zesch, Klaus Giebermann
36. Large Language Model-based Pipeline for Item Difficulty and Response Time Estimation for Educational Assessments ↗Hariram Veeramani, Surendrabikram Thapa, Natarajan Balaji Shankar et al.
37. Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction ↗Masamune Kobayashi, Masato Mita, Mamoru Komachi
38. Leveraging Physical and Semantic Features of text item for Difficulty and Response Time Prediction of USMLE Questions ↗Gummuluri Venkata Ravi Ram, Ashinee Kesanam, Anand Kumar M
39. Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models ↗Kostiantyn Omelianchuk, Andrii Liubonko, Oleksandr Skurzhanskyi et al.
40. Predicting Initial Essay Quality Scores to Increase the Efficiency of Comparative Judgment Assessments ↗Michiel De Vrindt, Anaïs Tack, Renske Bouwer et al.
41. Predicting Item Difficulty and Item Response Time with Scalar-mixed Transformer Encoder Models and Rational Network Regression Heads ↗Sebastian Gombert, Lukas Menzel, Daniele Di Mitri et al.
42. RETUYT-INCO at MLSP 2024: Experiments on Language Simplification using Embeddings, Classifiers and Large Language Models ↗Ignacio Sastre, Leandro Alfonso, Facundo Fleitas et al.
43. Scoring with Confidence? – Exploring High-confidence Scoring for Saving Manual Grading Effort ↗Marie Bexte, Andrea Horbach, Lena Schützler et al.
44. Synthetic Data Generation for Low-resource Grammatical Error Correction with Tagged Corruption Models ↗Felix Stahlberg, Shankar Kumar
45. TMU-HIT at MLSP 2024: How Well Can GPT-4 Tackle Multilingual Lexical Simplification? ↗Taisei Enomoto, Hwichan Kim, Tosho Hirasawa et al.
46. The BEA 2024 Shared Task on the Multilingual Lexical Simplification Pipeline ↗Matthew Shardlow, Fernando Alva-Manchego, Riza Batista-Navarro et al.
47. The British Council submission to the BEA 2024 shared task ↗Mariano Felice, Zeynep Duran Karaoz
48. Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond ↗Masato Mita, Keisuke Sakaguchi, Masato Hagiwara et al.
49. Towards Fine-Grained Pedagogical Control over English Grammar Complexity in Educational Text GenerationDominik Glandorf, Detmar Meurersour summary
50. Transfer Learning of Argument Mining in Student Essays ↗Yuning Ding, Julian Lohmann, Nils-Jonathan Schaller et al.
51. UNED team at BEA 2024 Shared Task: Testing different Input Formats for predicting Item Difficulty and Response Time in Medical Exams ↗Alvaro Rodrigo, Sergio Moreno-Álvarez, Anselmo Peñas
52. UPN-ICC at BEA 2024 Shared Task: Leveraging LLMs for Multiple-Choice Questions Difficulty Prediction ↗George Duenas, Sergio Jimenez, Geral Mateus Ferro
53. UnibucLLM: Harnessing LLMs for Automated Prediction of Item Difficulty and Response Time for Multiple-Choice Questions ↗Ana-Cristina Rogoz, Radu Tudor Ionescu
54. Using Adaptive Empathetic Responses for Teaching English ↗Li Siyan, Teresa Shao, Julia Hirschberg et al.
55. Using Large Language Models to Assess Young Students' Writing Revisions ↗Tianwen Li, Zhexiong Liu, Lindsay Matsumura et al.
56. Using Machine Learning to Predict Item Difficulty and Response Time in Medical Tests ↗Mehrdad Yousefpoori-Naeim, Shayan Zargari, Zahra Hatami
57. Using Program Repair as a Proxy for Language Models' Feedback Ability in Programming Education ↗Charles Koutcheme, Nicola Dainese, Arto Hellas
58. Utilizing Machine Learning to Predict Question Difficulty and Response Time for Enhanced Test Construction ↗Rishikesh Fulari, Jonathan Rusert

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