Austria

Automatic Short Answer Grading (ASAG) refers to automated scoring of open-ended textual responses to specific questions, both in natural language form. In this paper, we propose a method to tackle this task in a setting where annotated data is unavailable. Crucially, our method is competitive with the state-of-the-art while being lighter and interpretable. 
We crafted a unique dataset containing a highly diverse set of questions and a small amount of answers to these questions; making it more challenging compared to previous tasks. 
Our method uses weak labels generated from other methods proven to be effective in this task, which are then used to train a white-box (linear) regression based on a few interpretable features. The latter are extracted expert features and learned representations that are interpretable \textit{per se} and aligned with manual labeling. 
We show the potential of our method by evaluating it on a small annotated portion of the dataset, and demonstrate that its ability compares with that of strong baselines and state-of-the-art methods, comprising an LLM that in contrast to our method comes with a high computational price and an opaque reasoning process. 
We further validate our model on a public Automatic Essay Scoring dataset in English, and obtained competitive results compared to other unsupervised baselines, outperforming the LLM. To gain further insights of our method, we conducted an interpretability analysis revealing sparse weights in our linear regression model, and alignment between our features and human ratings.\footnote{Code available at \href{https://github.com/furrutiav/unasages-bea2025}{furrutiav/unasages-bea2025}}

ACL 2025

Unsupervised Automatic Short Answer Grading and Essay Scoring: A Weakly Supervised Explainable Approach

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Multiple-Choice Tasks are one of the most common types of assessment item, due to their feature of being easy to automatically and objectively grade. A key component of Multiple-Choice Tasks are distractors -- i.e., the wrong answer options -- since poor distractors affect the overall quality of the item: e.g., if they are obviously wrong, they are never selected. Thus, previous research has focused extensively on techniques for automatically generating distractors, which can be especially helpful in settings where large pools of questions are desirable or needed. However, there is no agreement within the community about the techniques that are most suited to evaluate generated distractors, and the ones used in the literature are sometimes not aligned with how distractors perform in real exams. In this review paper, we perform a comprehensive study of the approaches which are used in the literature for evaluating generated distractors, propose a taxonomy to categorise them, discuss if and how they are aligned with distractors performance in exam settings, and what are the differences for different question types and educational domains.

A Survey on Automated Distractor Evaluation in Multiple-Choice Tasks

This paper investigates the potentials of Large Language Models (LLMs) as adaptive tutors in the context of second-language learning. In particular, we evaluate whether system prompting can reliably constrain LLMs to generate only text appropriate to the student's competence level. We simulate full teacher-student dialogues in Spanish using instruction-tuned, open-source LLMs ranging in size from 7B to 12B parameters. Dialogues are generated by having an LLM alternate between tutor and student roles with separate chat histories. The output from the tutor model is then used to evaluate the effectiveness of CEFR-based prompting to control text difficulty across three proficiency levels (A1, B1, C1). Our findings suggest that while system prompting can be used to constrain model outputs, prompting alone is too brittle for sustained, long-term interactional contexts - a phenomenon we term alignment drift. Our results provide insights into the feasibility of LLMs for personalized, proficiency aligned adaptive tutors and provide a scalable method for low-cost evaluation of model performance without human participants.

Alignment Drift in CEFR-prompted LLMs for Interactive Spanish Tutoring

Large Language Models (LLMs) offer many opportunities for scalably improving the teaching and learning process, for example, by simulating students for teacher training or lesson preparation. However, design requirements for building high-fidelity LLM-based simulations are poorly understood. This study aims to address this gap from the perspective of key stakeholders—teachers who have tutored LLM-simulated students. We use a mixed-method approach and conduct semi-structured interviews with these teachers, grounding our interview design and analysis in the Community of Inquiry and Scaffolding frameworks. Our findings indicate several challenges in LLM-simulated students, including authenticity, high language complexity, lack of emotions, unnatural attentiveness, and logical inconsistency. We end by categorizing four types of real-world student behaviors and provide guidelines for the design and development of LLM-based student simulations. These include introducing diverse personalities, modeling knowledge building, and promoting questions.

Can LLMs Effectively Simulate Human Learners? Teachers' Insights from Tutoring LLM Students

Decoder-only large language models have shown superior performance in the fluency-edit English Grammatical Error Correction, but their adaptation for minimal-edit English GEC is still underexplored. To improve their effectiveness in the minimal-edit approach, we explore the error rate adaptation topic and propose a novel training schedule method. Our experiments set a new state-of-the-art result for a single-model system on the BEA-test set. We also detokenize the most common English GEC datasets to match the natural way of writing text. During the process, we find that there are errors in them. Our experiments analyze whether training on detokenized datasets impacts the results and measure the impact of the usage of the datasets with corrected erroneous examples. To facilitate reproducibility, we have released the source code used to train our models.

Adapting LLMs for Minimal-edit Grammatical Error Correction

In this work, we assess the potential of using synthetic data to train models for content scoring. We generate a parallel corpus of LLM-generated data for the SRA dataset. In our experiments, we train three different kinds of models (Logistic Regression, BERT, SBERT) with this data, examining their respective ability to bridge between generated training data and student-authored test data. We also explore the effects of generating larger volumes of training data than what is available in the original dataset. Overall, we find that training models from LLM-generated data outperforms zero-shot scoring of the test data with an LLM. Still, the fine-tuned models perform much worse than models trained on the original data, largely because the LLM-generated answers often do not to conform to the desired labels. However, once the data is manually relabeled, competitive models can be trained from it. With a similarity-based scoring approach, the relabeled (larger) amount of synthetic answers consistently yields a model that surpasses performance of training on the (limited) amount of answers available in the original dataset.

Is Lunch Free Yet? Overcoming the Cold-Start Problem in Supervised Content Scoring using Zero-Shot LLM-Generated Training Data

Establishing the difficulty of test items is an essential part of the language assessment development process. However, traditional item calibration methods are often time-consuming and difficult to scale. To address this, recent research has explored natural language processing (NLP) approaches for automatically predicting item difficulty from text. This paper investigates the use of transformer models to predict the difficulty of second language (L2) English vocabulary test items that have multilingual prompts. We introduce an extended versions of the British Council's Knowledge-based Vocabulary Lists (KVL), containing 6,768 English words paired with difficulty scores and question prompts written in Spanish, German, and Mandarin Chinese. Using this dataset for fine-tuning, we explore various transformer-based architectures. Our findings show that a multilingual model jointly trained on all L1 subsets of the KVL achieve the best results, with insights suggesting that the model is able to learn global patterns of cross-linguistic influence on target word difficulty. This study establishes a foundation for NLP-based item difficulty estimation using the KVL dataset, providing actionable insights for developing multilingual test items.

Transformer Architectures for Vocabulary Test Item Difficulty Prediction

Heterogeneity in student populations poses a challenge in formal education, with adaptive textbooks offering a potential solution by taioring content based on individual learner models. However, creating domain models for textbooks typically demands significant manual effort. Recent work by Chau et al. (2021) demonstrated automated concept extraction from digital textbooks, but relied on costly domain-specific manual annotations. This paper introduces a novel, scalable method that minimizes manual effort by combining contextualized word embeddings with weakly supervised machine learning. Our approach clusters word embeddings from textbooks and identifies domain-specific concepts using a machine learner trained on concept seeds automatically extracted from Wikipedia. We evaluate this method using 28 economics textbooks, comparing its performance against a tf-idf baseline, a supervised machine learning baseline, the RAKE keyword extraction method, and human domain experts. Results demonstrate that our weakly supervised method effectively balances accuracy with reduced annotation effort, offering a practical solution for automated concept extraction in adaptive learning environments.

Automatic concept extraction for learning domain modeling: A weakly supervised approach using contextualized word embeddings

This paper presents an automatic speech assessment system designed for Swedish language learners. We introduce a novel hybrid approach that integrates Microsoft Azure speech services with open-source Large Language Models (LLMs). Our system is implemented as a web-based application that provides real-time quick assessment with a game-like experience. Through testing against COREFL English corpus data and Swedish L2 speech data, our system demonstrates effectiveness in distinguishing different language proficiencies, closely aligning with CEFR levels. This ongoing work addresses the gap in current low-resource language assessment technologies with a pilot system developed for automated speech analysis.

Towards a Real-time Swedish Speech Analyzer for Language Learning Games: A Hybrid AI Approach to Language Assessment

Evaluation of Grammatical Error Correction (GEC) systems is becoming increasingly challenging as the quality of such systems increases and traditional automatic metrics fail to adequately capture such nuances as fluency versus minimal edits, alternative valid corrections compared to the `ground truth', and the difference between corrections that are useful in a language learning scenario versus those preferred by native readers. Previous work has suggested using human post-editing of GEC system outputs, but this is very labor-intensive. We investigate the use of Large Language Models (LLMs) as post-editors of English and Swedish texts, and perform a meta-analysis of a range of different evaluation setups using a set of recent GEC systems. We find that for the two languages studied in our work, automatic evaluation based on post-editing agrees well with both human post-editing and direct human rating of GEC systems. Furthermore, we find that a simple n-gram overlap metric is sufficient to measure post-editing distance, and that including human references when prompting the LLMs generally does not improve agreement with human ratings. The resulting evaluation metric is reference-free and requires no language-specific training or additional resources beyond an LLM capable of handling the given language.
Evaluation of Grammatical Error Correction (GEC) systems is becoming increasingly challenging as the quality of such systems increases and traditional automatic metrics fail to adequately capture such nuances as fluency versus minimal edits, alternative valid corrections compared to the `ground truth', and the difference between corrections that are useful in a language learning scenario versus those preferred by native readers. Previous work has suggested using human post-editing of GEC system outputs, but this is very labor-intensive. We investigate the use of Large Language Models (LLMs) as post-editors of English and Swedish texts, and perform a meta-analysis of a range of different evaluation setups using a set of recent GEC systems. We find that for the two languages studied in our work, automatic evaluation based on post-editing agrees well with both human post-editing and direct human rating of GEC systems. Furthermore, we find that a simple n-gram overlap metric is sufficient to measure post-editing distance, and that including human references when prompting the LLMs generally does not improve agreement with human ratings. The resulting evaluation metric is reference-free and requires no language-specific training or additional resources beyond an LLM capable of handling the given language.

LLM-based post-editing as reference-free GEC evaluation

We present an approach to the automated scoring of a German Written Elicited Imitation Test, designed to assess literacy-dependent procedural knowledge in German as a foreign language. In this test, sentences are briefly displayed on a screen and, after a short pause, test-takers are asked to reproduce the sentence in writing as accurately as possible. Responses are rated on a 5-point ordinal scale, with grammatical errors typically penalized more heavily than lexical deviations. We compare a rule-based model that implements the categories of the scoring rubric through hand-crafted rules, and a deep learning model trained on pairs of stimulus sentences and written responses. Both models achieve promising performance with quadratically weighted kappa (QWK) values around .87. However, their strengths differ – the rule-based model performs better on previously unseen stimulus sentences and at the extremes of the rating scale, while the deep learning model shows advantages in scoring mid-range responses, for which explicit rules are harder to define.

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A Survey on Automated Distractor Evaluation in Multiple-Choice Tasks

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A Survey on Automated Distractor Evaluation in Multiple-Choice Tasks

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Automatic scoring of short answers using justification cues estimated by BERT

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