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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.

ACL 2023

Hybrid Models for Sentence Readability Assessment

hybrid models

sentence readability

automatic readability assessment

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

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.

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.

Training for Grammatical Error Correction Without Human-Annotated L2 Learners' Corpora

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.

Japanese Lexical Complexity for Non-Native Readers: A New Dataset

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 empirically check the hypothesis that the 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 in a real learning setting for the best-performing error types. However, it struggles to assess answers which were tagged by human annotators as {\em 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.

Grammatical Error Correction for Sentence-level Assessment in Language Learning

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.1 Building on these findings, we develop the first contextual lexical simplification model for Dutch and publish a pilot dataset for evaluation. We go beyond previous 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.

"Geen makkie": Interpretable Classification and Simplification of Dutch Text Complexity

We present a novel approach to lexical complexity prediction (LCP), based on rich contextual representations. We show that our system is capable of:
• classifying word senses in context;
• predicting complexity of both words and phrases;
• producing results in French with no or limited training data

CEFR-based Contextual Lexical Complexity Classifier in English and French

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 https://github.com/ddemszky/classroom-transcript-analysis

The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts

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.

Auto-req: Automatic detection of pre-requisite dependencies between academic videos

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 different 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.

Comparing Neural Question Generation Architectures for Reading Comprehension

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.

Towards automatically extracting morphosyntactical error patterns from L1-L2 parallel dependency treebanks

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. In this paper, we particularly focus on automatic creation of gapfilling exercises for language learning, specifically grammar exercises. Since providing any annotation in this domain requires human expert effort, we aim to avoid it entirely and explore the task of converting existing texts into new gap-filling exercises, purely based on an example exercise, without explicit instruction or detailed annotation of the intended grammar topics. We contribute (i) a novel neural network architecture specifically designed for aforementioned gap-filling exercise generation task, and (ii) a real-world benchmark dataset for French grammar. We show that our model for this French grammar gap-filling exercise generation outperforms a competitive baseline classifierby8%inF1percentagepoints,achievinganaverageF1scoreof82%. Ourmodelimplementation and the dataset are made publicly available1 to foster future research, thus offering a standardized evaluation and baseline solution of the proposed partially annotated data prediction task in grammar exercise creation.

Next from ACL 2023

Training for Grammatical Error Correction Without Human-Annotated L2 Learners' Corpora

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A Benchmark for Neural Readability Assessment of Texts in Spanish

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