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Question generation (QG) for reading comprehension, a technology for automatically generating questions related to given reading passages, has been used in various applications, including in education. Recently, QG methods based on deep neural networks have succeeded in generating fluent questions that are pertinent to given reading passages. One example of how QG can be applied in education is a reading tutor that automatically offers reading comprehension questions related to various reading materials. In such an application, QG methods should provide questions with difficulty levels appropriate for each learner’s reading ability in order to improve learning efficiency. Several difficulty-controllable QG methods have been proposed for doing so. However, conventional methods focus only on generating questions and cannot generate answers to them. Furthermore, they ignore the relation between question difficulty and learner ability, making it hard to determine an appropriate difficulty for each learner. To resolve these problems, we propose a new method for generating question–answer pairs that considers their difficulty, estimated using item response theory. The proposed difficulty-controllable generation is realized by extending two pre-trained transformer models: BERT and GPT-2.

ACL 2023

Difficulty-Controllable Neural Question Generation for Reading Comprehension using Item Response Theory

adaptive testing

automated question generation for reading comprehension

adaptive learning

item response theory

natural language processing

deep neural networks

### 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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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

This paper presents Card-it, a web-based application for learning Italian verb conjugation. Card-it integrates a large-scale finite-state morphological~(FSM) analyzer and a flashcard application as a user-friendly way for learners to utilize the analyzer. While Card-it can be used by individual learners, to support classroom adoption, we implemented simple classroom management functionalities such as sharing flashcards to a class and tracking students' progression. We evaluated Card-it with teachers of Italian. Card-it was reported as engaging and supportive, especially by featuring two different quiz types combined with a verb form look-up feature. Teachers were optimistic about the potential of Card-it as a classroom supplementary tool for learners of Italian as L2. Future work includes sample sentences and a complete learners evaluation.

Evaluating Classroom Potential for Card-it: Digital Flashcards for Studying and Learning Italian Morphology

Open-ended constructed response math word problems ("math plus text", or MPT) are a powerful tool in the assessment of students' abilities to engage in mathematical reasoning and creative thinking. Such problems ask the student to compute a value or construct an expression and then explain, potentially in prose, what steps they took and why they took them. MPT items can be scored against highly structured rubrics, and we develop a novel technique for the automated scoring of MPT items that leverages these rubrics to provide explainable scoring. We show that our approach can be trained automatically and performs well on a large dataset of 34,417 responses across 14 MPT items.

Scalable and Explainable Automated Scoring for Open-Ended Constructed Response Math Word Problems

We develop an interactive web-based user interface for performing text--speech alignment and creating digital interactive "readalong" audio books that highlight words as they are spoken and allow users to replay individual words when clicked. We build on an existing Python library for zero-shot multilingual text-speech alignment (Littell et. al. 2022), extend it by exposing its functionality through a RESTful API, and rewrite the underlying speech recognition engine to run in the browser. The ReadAlong Studio Web App is open-source, user-friendly, prioritizes privacy and data sovereignty, allows for a variety of standard export formats, and is designed to work for the majority of the world's languages

ReadAlong Studio Web Interface for Digital Interactive Storytelling

The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course. Additionally, the breadth of QA derived from this exponential growth makes it an ideal scenario for teaching related NLP topics such as information retrieval, explainability, and adversarial attacks among others. In this paper, we introduce UKP-SQuARE as a platform for QA education. This platform provides an interactive environment where students can run, compare, and analyze various QA models from different perspectives, such as general behavior, explainability, and robustness. Therefore, students can get a first-hand experience in different QA techniques during the class. Thanks to this, we propose a learner-centered approach for QA education in which students proactively learn theoretical concepts and acquire problem-solving skills through interactive exploration, experimentation, and practical assignments, rather than solely relying on traditional lectures. To evaluate the effectiveness of UKP-SQuARE in teaching scenarios, we adopted it in a postgraduate NLP course and surveyed the students after the course. Their positive feedback shows the platform's effectiveness in their course and invites a wider adoption.

UKP-SQuARE: An Interactive Tool for Teaching Question Answering

Large-scale pre-trained language models such as GPT-3 have shown remarkable performance across various natural language processing tasks. However, applying prompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasks and their controllability remains underexplored. Controllability in GEC is crucial for real-world applications, particularly in educational settings, where the ability to tailor feedback according to learner levels and specific error types can significantly enhance the learning process. This paper investigates the performance and controllability of prompt-based methods with GPT-3 for GEC tasks using zero-shot and few-shot setting. We explore the impact of task instructions and examples on GPT-3's output, focusing on controlling aspects such as minimal edits, fluency edits, and learner levels. Our findings demonstrate that GPT-3 could effectively perform GEC tasks, outperforming existing supervised and unsupervised approaches. We also showed that GPT-3 could achieve controllability when appropriate task instructions and examples are given.

Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods

In various natural language processing tasks, such as named entity recognition and machine translation, example-based approaches have been used to improve performance by leveraging existing knowledge. However, the effectiveness of this approach for Grammatical Error Correction (GEC) is unclear. In this work, we explore how an example-based approach affects the accuracy and interpretability of the output of GEC systems and the trade-offs involved. The approach we investigate has shown great promise in machine translation by using the k nearest translation examples to improve the results of a pretrained Transformer model. We find that using this technique increases precision by reducing the number of false positives, but recall suffers as the model becomes more conservative overall. Increasing the number of example sentences in the datastore does lead to better performing systems, but with diminishing returns and a high decoding cost. Synthetic data can be used as examples, but the effectiveness varies depending on the base model. Finally, we find that finetuning on a set of data may be more effective than using that data during decoding as examples.

A Closer Look at k-Nearest Neighbors Grammatical Error Correction

By aligning the functional components derived from the activations of transformer models trained for Automated Essay Scoring (AES) with external knowledge such as human-understandable feature groups, the proposed method improves the interpretability of a Longformer AES system and provides tools for performing such analyses on further neural AES systems. The analysis focuses on models trained to score essays based on organization, main idea, support, and language. The findings provide insights into the models' decision-making processes, biases, and limitations, contributing to the development of more transparent and reliable AES systems.

Towards Extracting and Understanding the Implicit Rubrics of Transformer Based Automatic Essay Scoring Models

This paper analyzes winning solutions from the Feedback Prize competition series hosted from 2021-2022. The competitions sought to improve Assisted Writing Feedback Tools (AWFTs) by crowdsourcing Large Language Model (LLM) solutions for evaluating student writing. The winning LLM-based solutions are freely available for incorporation into educational applications, but the models need to be assessed for performance and other factors. This study reports the performance accuracy of Feedback Prize-winning models based on demographic factors such as student race/ethnicity, economic disadvantage, and English Language Learner status. Two competitions are analyzed. The first, which focused on identifying discourse elements, demonstrated minimal bias based on students' demographic factors. However, the second competition, which aimed to predict discourse effectiveness, exhibited moderate bias.


Analyzing Bias in Large Language Model Solutions for Assisted Writing Feedback Tools: Lessons from the Feedback Prize Competition Series

Reading comprehension is a crucial skill in many aspects of education, including language learning, cognitive development, and fostering early literacy skills in children. Automated answer-aware reading comprehension question generation has significant potential to scale up learner support in educational activities. One key technical challenge in this setting is that there can be multiple questions, sometimes very different from each other, with the same answer; a trained question generation method may not necessarily know which question human educators would prefer. To address this challenge, we propose 1) a data augmentation method that enriches the training dataset with diverse questions given the same context and answer and 2) an overgenerate-and-rank method to select the best question from a pool of candidates. We evaluate our method on the FairytaleQA dataset, showing a 5% absolute improvement in ROUGE-L over the best existing method. We also demonstrate the effectiveness of our method in generating harder, "implicit" questions, where the answers are not contained in the context as text spans.

Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank

The standard definition generation task requires to automatically produce mono-lingual definitions (e.g., English definitions for English words), but ignores that the generated definitions may also consist of unfamiliar words for language learners. In this work, we propose a novel task of Trans-Lingual Definition Generation (TLDG), which aims to generate definitions in another language, i.e., the native speaker’s language. Initially, we explore the unsupervised manner of this task and build up a simple implementation of fine-tuning the multilingual machine translation model. Then, we develop two novel methods, Prompt Combination and Contrastive Prompt Learning, for further enhancing the quality of the generation. Our methods are evaluated against the baseline Pipeline method in both rich- and low-resource settings, and we empirically establish its superiority in generating higher-quality trans-lingual definitions. The ablation studies and further analysis are also conducted to provide more hints on this new task.

Next from ACL 2023

Evaluating Classroom Potential for Card-it: Digital Flashcards for Studying and Learning Italian Morphology

Similar lecture

Advancing Question Generation with Joint Narrative and Difficulty Control

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