ymoslem/acl-abstracts huggingface dataset:
ALEXSIS+: Improving Substitute Generation and Selection for Lexical Simplification with Information Retrieval (2023)

Lexical simplification (LS) automatically replaces words that are deemed difficult to understand for a given target population with simpler alternatives, whilst preserving the meaning of the original sentence. The TSAR-2022 shared task on LS provided participants with a multilingual lexical simplification test set. It contained nearly 1,200 complex words in English, Portuguese, and Spanish and presented multiple candidate substitutions for each complex word. The competition did not make training data available; therefore, teams had to use either off-the-shelf pre-trained large language models (LLMs) or out-domain data to develop their LS systems. As such, participants were unable to fully explore the capabilities of LLMs by re-training and/or fine-tuning them on in-domain data. To address this important limitation, we present ALEXSIS+, a multilingual dataset in the aforementioned three languages, and ALEXSIS++, an English monolingual dataset that together contains more than 50,000 unique sentences retrieved from news corpora and annotated with cosine similarities to the original complex word and sentence. Using these additional contexts, we are able to generate new high-quality candidate substitutions that improve LS performance on the TSAR-2022 test set regardless of the language or model.
Generating Better Items for Cognitive Assessments Using Large Language Models (2023)

Writing high-quality test questions (items) is critical to building educational measures but has traditionally also been a time-consuming process. One promising avenue for alleviating this is automated item generation, whereby methods from artificial intelligence (AI) are used to generate new items with minimal human intervention. Researchers have explored using large language models (LLMs) to generate new items with equivalent psychometric properties to human-written ones. But can LLMs generate items with improved psychometric properties, even when existing items have poor validity evidence? We investigate this using items from a natural language inference (NLI) dataset. We develop a novel prompting strategy based on selecting items with both the best and worst properties to use in the prompt and use GPT-3 to generate new NLI items. We find that the GPT-3 items show improved psychometric properties in many cases, whilst also possessing good content, convergent and discriminant validity evidence. Collectively, our results demonstrate the potential of employing LLMs to ease the item development process and suggest that the careful use of prompting may allow for iterative improvement of item quality.
Span Identification of Epistemic Stance-Taking in Academic Written English (2023)

Responding to the increasing need for automated writing evaluation (AWE) systems to assess language use beyond lexis and grammar (Burstein et al., 2016), we introduce a new approach to identify rhetorical features of stance in academic English writing. Drawing on the discourse-analytic framework of engagement in the Appraisal analysis (Martin {\&} White, 2005), we manually annotated 4,688 sentences (126,411 tokens) for eight rhetorical stance categories (e.g., PROCLAIM, ATTRIBUTION) and additional discourse elements. We then report an experiment to train machine learning models to identify and categorize the spans of these stance expressions. The best-performing model (RoBERTa + LSTM) achieved macro-averaged F1 of .7208 in the span identification of stance-taking expressions, slightly outperforming the intercoder reliability estimates before adjudication (F1 = .6629).
ACTA: Short-Answer Grading in High-Stakes Medical Exams (2023)

This paper presents the ACTA system, which performs automated short-answer grading in the domain of high-stakes medical exams. The system builds upon previous work on neural similarity-based grading approaches by applying these to the medical domain and utilizing contrastive learning as a means to optimize the similarity metric. ACTA is evaluated against three strong baselines and is developed in alignment with operational needs, where low-confidence responses are flagged for human review. Learning curves are explored to understand the effects of training data on performance. The results demonstrate that ACTA leads to substantially lower number of responses being flagged for human review, while maintaining high classification accuracy.
Hybrid Models for Sentence Readability Assessment (2023)

Automatic readability assessment (ARA) predicts how difficult it is for the reader to understand a text. While ARA has traditionally been performed at the passage level, there has been increasing interest in ARA at the sentence level, given its applications in downstream tasks such as text simplification and language exercise generation. Recent research has suggested the effectiveness of hybrid approaches for ARA, but they have yet to be applied on the sentence level. We present the first study that compares neural and hybrid models for sentence-level ARA. We conducted experiments on graded sentences from the Wall Street Journal (WSJ) and a dataset derived from the OneStopEnglish corpus. Experimental results show that both neural and hybrid models outperform traditional classifiers trained on linguistic features. Hybrid models obtained the best accuracy on both datasets, surpassing the previous best result reported on the WSJ dataset by almost 13{\%} absolute.
Training for Grammatical Error Correction Without Human-Annotated L2 Learners' Corpora (2023)

Grammatical error correction (GEC) is a challenging task for non-native second language (L2) learners and learning machines. Data-driven GEC learning requires as much human-annotated genuine training data as possible. However, it is difficult to produce larger-scale human-annotated data, and synthetically generated large-scale parallel training data is valuable for GEC systems. In this paper, we propose a method for rebuilding a corpus of synthetic parallel data using target sentences predicted by a GEC model to improve performance. Experimental results show that our proposed pre-training outperforms that on the original synthetic datasets. Moreover, it is also shown that our proposed training without human-annotated L2 learners' corpora is as practical as conventional full pipeline training with both synthetic datasets and L2 learners' corpora in terms of accuracy.
Exploring a New Grammatico-functional Type of Measure as Part of a Language Learning Expert System (2023)

This paper explores the use of L2-specific grammatical microsystems as elements of the domain knowledge of an Intelligent Computer-assisted Language Learning (ICALL) system. We report on the design of new grammatico-functional measures and their association with proficiency. We illustrate the approach with the design of the IT, THIS, THAT proform microsystem. The measures rely on the paradigmatic relations between words of the same linguistic functions. They are operationalised with one frequency-based and two probabilistic methods, i.e., the relative proportions of the forms and their likelihood of occurrence. Ordinal regression models show that the measures are significant in terms of association with CEFR levels, paving the way for their introduction in a specific proform microsystem expert model.
Japanese Lexical Complexity for Non-Native Readers: A New Dataset (2023)

Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale. It plays a vital role in simplifying or annotating complex words to assist readers. To study lexical complexity in Japanese, we construct the first Japanese LCP dataset. Our dataset provides separate complexity scores for Chinese/Korean annotators and others to address the readers' L1-specific needs. In the baseline experiment, we demonstrate the effectiveness of a BERT-based system for Japanese LCP.
Grammatical Error Correction for Sentence-level Assessment in Language Learning (2023)

The paper presents experiments on using a Grammatical Error Correction (GEC) model to assess the correctness of answers that language learners give to grammar exercises. We explored whether a GEC model can be applied in the language learning context for a language with complex morphology. We empirically check a hypothesis that a GEC model corrects only errors and leaves correct answers unchanged. We perform a test on assessing learner answers in a real but constrained language-learning setup: the learners answer only fill-in-the-blank and multiple-choice exercises. For this purpose, we use ReLCo, a publicly available manually annotated learner dataset in Russian (Katinskaia et al., 2022). In this experiment, we fine-tune a large-scale T5 language model for the GEC task and estimate its performance on the RULEC-GEC dataset (Rozovskaya and Roth, 2019) to compare with top-performing models. We also release an updated version of the RULEC-GEC test set, manually checked by native speakers. Our analysis shows that the GEC model performs reasonably well in detecting erroneous answers to grammar exercises and potentially can be used for best-performing error types in a real learning setup. However, it struggles to assess answers which were tagged by human annotators as alternative-correct using the aforementioned hypothesis. This is in large part due to a still low recall in correcting errors, and the fact that the GEC model may modify even correct words{---}it may generate plausible alternatives, which are hard to evaluate against the gold-standard reference.
\textquotedblleftGeen makkie\textquotedblright: Interpretable Classification and Simplification of Dutch Text Complexity (2023)

An inclusive society needs to facilitate access to information for all of its members, including citizens with low literacy and with non-native language skills. We present an approach to assess Dutch text complexity on the sentence level and conduct an interpretability analysis to explore the link between neural models and linguistic complexity features. Building on these findings, we develop the first contextual lexical simplification model for Dutch and publish a pilot dataset for evaluation. We go beyondprevious work which primarily targeted lexical substitution and propose strategies for adjusting the model`s linguistic register to generate simpler candidates. Our results indicate that continual pre-training and multi-task learning with conceptually related tasks are promising directions for ensuring the simplicity of the generated substitutions.
CEFR-based Contextual Lexical Complexity Classifier in English and French (2023)

This paper describes a CEFR-based classifier of single-word and multi-word lexical complexity in context from a second language learner perspective in English and in French, developed as an analytical tool for the pedagogical team of the language learning application Mauril. We provide an overview of the required corpora and the way we transformed it into rich contextual representations that allow the disambiguation and accurate labelling in context of polysemous occurrences of a given lexical item. We report evaluation results for all models, including two multi-lingual lexical classifiers evaluated on novel French datasets created for this experiment. Finally, we share the perspective of Mauril`s pedagogical team on the limitations of such systems.
The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts (2023)

Classroom discourse is a core medium of instruction analyzing it can provide a window into teaching and learning as well as driving the development of new tools for improving instruction. We introduce the largest dataset of mathematics classroom transcripts available to researchers, and demonstrate how this data can help improve instruction. The dataset consists of 1,660 45-60 minute long 4th and 5th grade elementary mathematics observations collected by the National Center for Teacher Effectiveness (NCTE) between 2010-2013. The anonymized transcripts represent data from 317 teachers across 4 school districts that serve largely historically marginalized students. The transcripts come with rich metadata, including turn-level annotations for dialogic discourse moves, classroom observation scores, demographic information, survey responses and student test scores. We demonstrate that our natural language processing model, trained on our turn-level annotations, can learn to identify dialogic discourse moves and these moves are correlated with better classroom observation scores and learning outcomes. This dataset opens up several possibilities for researchers, educators and policymakers to learn about and improve K-12 instruction. The dataset can be found at \url{https://github.com/ddemszky/classroom-transcript-analysis}.
Auto-req: Automatic detection of pre-requisite dependencies between academic videos (2023)

Online learning platforms offer a wealth of educational material, but as the amount of content on these platforms grows, students may struggle to determine the most efficient order in which to cover the material to achieve a particular learning objective. In this paper, we propose a feature-based method for identifying pre-requisite dependencies between academic videos. Our approach involves using a transcript engine with a language model to transcribe domain-specific terms and then extracting novel similarity-based features to determine pre-requisite dependencies between video transcripts. This approach succeeds due to the development of a novel corpus of K-12 academic text, which was created using a proposed feature-based document parser. We evaluate our method on hand-annotated datasets for transcript extraction, video pre-requisites determination, and textbook parsing, which we have released. Our method for pre-requisite edge determination shows significant improvement (+4.7{\%}-10.24{\%} F1-score) compared to existing methods.
Transformer-based Hebrew NLP models for Short Answer Scoring in Biology (2023)

Pre-trained large language models (PLMs) are adaptable to a wide range of downstream tasks by fine-tuning their rich contextual embeddings to the task, often without requiring much task-specific data. In this paper, we explore the use of a recently developed Hebrew PLM aleph-BERT for automated short answer grading of high school biology items. We show that the alephBERT-based system outperforms a strong CNN-based baseline, and that it general-izes unexpectedly well in a zero-shot paradigm to items on an unseen topic that address the same underlying biological concepts, opening up the possibility of automatically assessing new items without item-specific fine-tuning.
Comparing Neural Question Generation Architectures for Reading Comprehension (2023)

In recent decades, there has been a significant push to leverage technology to aid both teachers and students in the classroom. Language processing advancements have been harnessed to provide better tutoring services, automated feedback to teachers, improved peer-to-peer feedback mechanisms, and measures of student comprehension for reading. Automated question generation systems have the potential to significantly reduce teachers' workload in the latter. In this paper, we compare three differ- ent neural architectures for question generation across two types of reading material: narratives and textbooks. For each architecture, we explore the benefits of including question attributes in the input representation. Our models show that a T5 architecture has the best overall performance, with a RougeL score of 0.536 on a narrative corpus and 0.316 on a textbook corpus. We break down the results by attribute and discover that the attribute can improve the quality of some types of generated questions, including Action and Character, but this is not true for all models.
A dynamic model of lexical experience for tracking of oral reading fluency (2023)

We present research aimed at solving a problem in assessment of oral reading fluency using children`s oral reading data from our online book reading app. It is known that properties of the passage being read aloud impact fluency estimates; therefore, passage-based measures are used to remove passage-related variance when estimating growth in oral reading fluency. However, passage-based measures reported in the literature tend to treat passages as independent events, without explicitly modeling accumulation of lexical experience as one reads through a book. We propose such a model and show that it helps explain additional variance in the measurements of children`s fluency as they read through a book, improving over a strong baseline. These results have implications for measuring growth in oral reading fluency.
Rating Short L2 Essays on the CEFR Scale with GPT-4 (2023)

Essay scoring is a critical task used to evaluate second-language (L2) writing proficiency on high-stakes language assessments. While automated scoring approaches are mature and have been around for decades, human scoring is still considered the gold standard, despite its high costs and well-known issues such as human rater fatigue and bias. The recent introduction of large language models (LLMs) brings new opportunities for automated scoring. In this paper, we evaluate how well GPT-3.5 and GPT-4 can rate short essay responses written by L2 English learners on a high-stakes language assessment, computing inter-rater agreement with human ratings. Results show that when calibration examples are provided, GPT-4 can perform almost as well as modern Automatic Writing Evaluation (AWE) methods, but agreement with human ratings can vary depending on the test-taker`s first language (L1).
Towards automatically extracting morphosyntactical error patterns from L1-L2 parallel dependency treebanks (2023)

L1-L2 parallel dependency treebanks are UD-annotated corpora of learner sentences paired with correction hypotheses. Automatic morphosyntactical annotation has the potential to remove the need for explicit manual error tagging and improve interoperability, but makes it more challenging to locate grammatical errors in the resulting datasets. We therefore propose a novel method for automatically extracting morphosyntactical error patterns and perform a preliminary bilingual evaluation of its first implementation through a similar example retrieval task. The resulting pipeline is also available as a prototype CALL application.
Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning (2023)

Since performing exercises (including, e.g.,practice tests) forms a crucial component oflearning, and creating such exercises requiresnon-trivial effort from the teacher. There is agreat value in automatic exercise generationin digital tools in education. In this paper, weparticularly focus on automatic creation of gap-filling exercises for language learning, specifi-cally grammar exercises. Since providing anyannotation in this domain requires human ex-pert effort, we aim to avoid it entirely and ex-plore the task of converting existing texts intonew gap-filling exercises, purely based on anexample exercise, without explicit instructionor detailed annotation of the intended gram-mar topics. We contribute (i) a novel neuralnetwork architecture specifically designed foraforementioned gap-filling exercise generationtask, and (ii) a real-world benchmark datasetfor French grammar. We show that our modelfor this French grammar gap-filling exercisegeneration outperforms a competitive baselineclassifier by 8{\%} in F1 percentage points, achiev-ing an average F1 score of 82{\%}. Our model im-plementation and the dataset are made publiclyavailable to foster future research, thus offeringa standardized evaluation and baseline solutionof the proposed partially annotated data predic-tion task in grammar exercise creation.
Evaluating Reading Comprehension Exercises Generated by LLMs: A Showcase of ChatGPT in Education Applications (2023)

The recent advancement of pre-trained Large Language Models (LLMs), such as OpenAI`s ChatGPT, has led to transformative changes across fields. For example, developing intelligent systems in the educational sector that leverage the linguistic capabilities of LLMs demonstrates a visible potential. Though researchers have recently explored how ChatGPT could possibly assist in student learning, few studies have applied these techniques to real-world classroom settings involving teachers and students. In this study, we implement a reading comprehension exercise generation system that provides high-quality and personalized reading materials for middle school English learners in China. Extensive evaluations of the generated reading passages and corresponding exercise questions, conducted both automatically and manually, demonstrate that the system-generated materials are suitable for students and even surpass the quality of existing human-written ones. By incorporating first-hand feedback and suggestions from experienced educators, this study serves as a meaningful pioneering application of ChatGPT, shedding light on the future design and implementation of LLM-based systems in the educational context.
Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction (2023)

Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript segments based on classroom observation instruments, (B)identifying highlights and missed opportunities for good instructional strategies, and (C) providing actionable suggestions for eliciting more student reasoning. We recruit expert math teachers to evaluate the zero-shot performance of ChatGPT on each of these tasks for elementary math classroom transcripts. Our results reveal that ChatGPT generates responses that are relevant to improving instruction, but they are often not novel or insightful. For example, 82{\%} of the model`s suggestions point to places in the transcript where the teacher is already implementing that suggestion. Our work highlights the challenges of producing insightful, novel and truthful feedback for teachers while paving the way for future research to address these obstacles and improve the capacity of generative AI to coach teachers.
Does BERT Exacerbate Gender or L1 Biases in Automated English Speaking Assessment? (2023)

In English speaking assessment, pretrained large language models (LLMs) such as BERT can score constructed response items as accurately as human raters. Less research has investigated whether LLMs perpetuate or exacerbate biases, which would pose problems for the fairness and validity of the test. This study examines gender and native language (L1) biases in human and automated scores, using an off-the-shelf (OOS) BERT model. Analyses focus on a specific type of bias known as differential item functioning (DIF), which compares examinees of similar English language proficiency. Results show that there is a moderate amount of DIF, based on examinees' L1 background in grade band 912. DIF is higher when scored by an OOS BERT model, indicating that BERT may exacerbate this bias; however, in practical terms, the degree to which BERT exacerbates DIF is very small. Additionally, there is more DIF for longer speaking items and for older examinees, but BERT does not exacerbate these patterns of DIF.