Mexico

Comparative judgment (CJ) is a method that can be used to assess the writing quality of student essays based on repeated pairwise comparisons by multiple assessors. Although the assessment method is known to have high validity and reliability, it can be particularly inefficient, as assessors must make many judgments before the scores become reliable. Prior research has investigated methods to improve the efficiency of CJ, yet these methods introduce additional challenges, notably stemming from the initial lack of information at the start of the assessment, which is known as a cold-start problem. This paper reports on a study in which we predict the initial quality scores of essays to establish a warm start for CJ. To achieve this, we construct informative prior distributions for the quality scores based on the predicted initial quality scores. Through simulation studies, we demonstrate that our approach increases the efficiency of CJ: On average, assessors need to make 30% fewer judgments for each essay to reach an overall reliability level of 0.70.

NAACL 2024

Predicting Initial Essay Quality Scores to Increase the Efficiency of Comparative Judgment Assessments

comparative judgment

bradley-terry-luce model

automated essay scoring

psychometrics

human-in-the-loop

### 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 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
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Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
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2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

This paper presents a cutting-edge method that harnesses contextualized language models (LMs) to significantly enhance the prediction of early academic performance in STEM fields. Our approach uniquely tackles the challenge of transfer learning with limited-domain data. Specifically, we overcome this challenge by contextualizing students' cognitive trajectory data through the integration of both distal background factors (comprising academic information, demographic details, and socioeconomic indicators) and proximal non-cognitive factors (such as emotional engagement). By tapping into the rich prior knowledge encoded within pre-trained LMs, we effectively reframe academic performance forecasting as a task ideally suited for natural language processing.

Our research rigorously examines three key aspects: the impact of data contextualization on prediction improvement, the effectiveness of our approach compared to traditional numeric-based models, and the influence of LM capacity on prediction accuracy. The results underscore the significant advantages of utilizing larger LMs with contextualized inputs, representing a notable advancement in the precision of early performance forecasts. These findings emphasize the importance of employing contextualized LMs to enhance artificial intelligence-driven educational support systems and overcome data scarcity challenges.

Improving Transfer Learning for Early Forecasting of Academic Performance by Contextualizing Language Models

Automated essay scoring (AES) to evaluate second language (L2) proficiency has been a firmly established technology used in educational contexts for decades. Although holistic scoring has seen advancements in AES that match or even exceed human performance, analytic scoring still encounters issues as it inherits flaws and shortcomings from the human scoring process. The recent introduction of large language models presents new opportunities for automating the evaluation of specific aspects of L2 writing proficiency. In this paper, we perform a series of experiments using GPT-4 in a zero-shot fashion on a publicly available dataset annotated with holistic scores based on the Common European Framework of Reference and aim to extract detailed information about their underlying analytic components. We observe significant correlations between the automatically predicted analytic scores and multiple features associated with the individual proficiency components.

Can GPT-4 do L2 analytic assessment?

Leading students to engage in argumentation-focused discussions is a challenge for elementary school teachers, as doing so requires facilitating group discussions with student-to-student interaction. The Mystery Powder (MP) Task was designed to be used in online simulated classrooms to develop teachers' skill in facilitating small group science discussions. In order to provide timely and scaleable feedback to teachers facilitating a discussion in the simulated classroom, we employ a hybrid modeling approach that successfully combines fine-tuned large language models with features capturing important elements of the discourse dynamic to evaluate MP discussion transcripts. To our knowledge, this is the first application of a hybrid model to automate evaluation of teacher discourse.

Automated Evaluation of Teacher Encouragement of Student-to-Student Interactions in a Simulated Classroom Discussion

Legal judgment prediction encompasses the automated prediction of case outcomes by leveraging historical facts and opinions. While this approach holds the potential to enhance the efficiency of the legal system, it also raises critical concerns regarding the perpetuation of biases. Abstract Meaning Representation has shown promise as an intermediate text representation in various downstream NLP tasks due to its ability to capture semantically meaningful information in a graph-like structure. In this paper, we employ this ability of AMR in the legal judgement prediction task and assess to what extent it encodes biases, or conversely, abstracts away from them. Our study reveals that while AMR-based models exhibit worse overall performance than transformer-based models, they are less biased for attributes like age and defendant state compared to gender. By shedding light on these findings, this paper contributes to a more nuanced understanding of AMR's potential benefits and limitations in legal NLP.

Can Abstract Meaning Representation Facilitate Fair Legal Judgement Predictions?

With the rapid development of LLMs, it is natural to ask how to harness their capabilities efficiently. In this paper, we explore whether it is feasible to direct each input query to a single most suitable LLM. To this end, we propose LLM routing for challenging reasoning tasks. Our extensive experiments suggest that such routing shows promise but is not feasible in all scenarios, so more robust approaches should be investigated to fill this gap.

Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing

This research investigates the impact of preference annotation acquisition methods on the performance of LLM alignment algorithms, including Direct Preference Optimization (DPO), Identity Preference Optimization (IPO), and Conservative DPO (cDPO), compared to Supervised Fine-Tuning (SFT) in NLP tasks. We analyze the influence of LLM and human-based preferences on algorithm performance, considering data volume and quality. Additionally, we assess DPO's vulnerability to overfitting and IPO's resilience against it, addressing four main research questions. Using the GAIR dataset and Zephyr-7b as the SFT model, we reveal unexpected negative outcomes. Specifically, DPO trained on LLM preferences outperforms human preferences, contrary to expectations. Moreover, there's no correlation between preference data volume or quality and algorithm performance. Contrary to expectations, DPO shows no overfitting in both human and LLM preference datasets. Surprisingly, cDPO doesn't fare better than DPO under flip noise. Our findings highlight the complexities of preference annotation methods and underscore the importance of scrutinizing negative results in NLP algorithm research.

The Paradox of Preference: A Study on LLM Alignment Algorithms and Data Acquisition Methods

Paraphrase identification (PI) and natural language inference (NLI) are two important tasks in natural language processing. Despite their distinct objectives, an underlying connection exists, which has been notably under-explored in empirical investigations. We formalize the relationship between these semantic tasks and introduce a method for solving PI using an NLI system, including the adaptation of PI datasets for fine-tuning NLI models. Through extensive evaluations on six PI benchmarks, across both zero-shot and fine-tuned settings, we showcase the efficacy of NLI models for PI through our proposed reduction. Remarkably, our fine-tuning procedure enables NLI models to outperform dedicated PI models on PI datasets. In addition, our findings provide insights into the limitations of current PI benchmarks.

Paraphrase Identification via Textual Inference

Abstract Meaning Representations (AMRs) encode the semantics of sentences in the form of graphs. Vertices represent instances of concepts, and labeled edges represent semantic relations between those instances. Language models (LMs) operate by computing weights of edges of per layer complete graphs whose vertices are words in a sentence or a whole paragraph. In this work, we investigate the ability of the attention heads of two LMs, RoBERTa and GPT2, to detect the semantic relations encoded in an AMR. This is an attempt to show semantic capabilities of those models without finetuning. To do so, we apply both unsupervised and supervised learning techniques.

Exploring Semantics in Pretrained Language Model Attention

This paper addresses the problem of word sense induction (WSI) via clustering of word embeddings. It starts from the hypothesis that contextualized word representations obtained from pre-trained language models (LMs), while being a valuable source for WSI, encode more information than what is necessary for the identification of word senses and some of this information affect the performance negatively in unsupervised settings. We investigate whether using contextualized representations that are invariant to these 'nuisance features' can increase WSI performance. For this purpose, we propose an adaptation of the adversarial training framework proposed by Jaiswal et al. (2020) to erase specific information from the representations of LMs, thereby creating feature-invariant representations. We experiment with erasing (i) morphological and (ii) syntactic features. The results of subsequent clustering for WSI show that these features indeed act like noise: Using feature-invariant representations, compared to using the original representations, increases clustering-based WSI performance. Furthermore, we provide an in-depth analysis of how the information about the syntactic and morphological features of words relate to and affect WSI performance.

Improving Word Sense Induction through Adversarial Forgetting of Morphosyntactic Information

The symbol grounding problem—how to connect a symbolic system to the outer world—is a longstanding question in AI that has recently gained prominence with the progress made in NLP in general and surrounding large language models in particular. In this article, we study the emergence of semantic categories in the communication protocol developed by neural agents involved in a well-established type of signaling game. In its basic form, the game requires one agent to retrieve an image based on a message produced by a second agent. We first show that the agents are able to, and do, learn to communicate high-level semantic concepts rather than low-level features of the images even from very indirect training signal to that end. Second, we demonstrate that the introduction of an adversarial agent in the game fosters the emergence of semantics by producing an appropriate training signal when no other method is available.

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