United States

We present a modular pipeline for educational analogy generation, decomposed into four stages -- source finding, sub-concept generation, explanation generation, and evaluation -- grounded in Structure Mapping Theory. Evaluating 12 LLMs across six model families on SCAR and ParallelPARC, we find that sub-concept grounding substantially improves retrieval precision and explanation quality but offers limited benefit in open-ended generation. We further validate an LLM-as-a-judge methodology against human annotations, finding that Claude Sonnet 4.6 aligns more reliably with human rankings than with absolute scores. Our results highlight cross-stage interactions that isolated studies cannot capture, and position sub-concept grounding as a key driver of analogy quality.

ACL 2026

Teaching Through Analogies: A Modular Pipeline for Educational Analogy Generation

edunlp

analogy

## Welcome to EMNLP 2022!
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Adaptive language-learning systems often rely on correctness in constrained exercise formats, but dialogue-based tutors require richer evidence derived from learners' own language use. This raises a learner-modeling problem: which aspects of learner production in dialogue can serve as useful signals for adaptive progression? We address this question using pilot data from an LLM-based English grammar tutor, comprising 40 pre/post tasks, treatment interactions, and 2,406 learner messages. We report an exploratory analysis of candidate productive-use signals for future adaptive decisions. Results suggest that different adaptive goals require different evidence: short-term prediction of independent target use can be supported by a simple trace of prior target use, whereas broader productive control is more strongly associated with lexical diversity within the construction. Finer-grained typed evidence adds relatively little predictive value, but is relevant for interpreting how different forms of learner behavior relate to later target use. We discuss implications for adaptive dialogue systems that aim to move beyond fixed sequencing.

Tracing Evidence of Productive Use for Learner Modeling in an LLM-Based Grammar Tutor

This paper presents team SAAKTH's system for the BEA 2026 Shared Task on Vocabulary Difficulty Prediction (Closed Track). We address the key challenge that English word difficulty is not fixed but varies with English learners' native language. Our approach combines a fine-tuned XLM-RoBERTa-large encoder with handcrafted psycholinguistic features engineered separately for each L1 group. These features are integrated via a shallow multilayer perceptron and optimized separately per L1, with five-seed ensembling and XGBoost-based blending for stability. Our system achieves RMSEs of 0.997 (es), 1.002 (de), and 0.932 (cn) on the development set, improving 20–25% over the baseline. Results highlight the effectiveness of L1-aware modeling under limited data.

L1-Aware English Vocabulary Difficulty Prediction with Hybrid Transformer and Psycholinguistic Features

Open-source LLMs with simple, zero-shot prompts are at best middling graders on the BEA 2026 Automated Grading Shared Task -- blunt-edge models, in fact. However, they are good enough to support human graders and save them time. We demonstrate the application of a hybrid grading approach that first transparently defines the success criteria and then pairs a zero-shot LLM grader with human review. The hybrid approach outperforms the LLM grader on its own and has the added advantage of keeping the human in the loop.

HFT at BEA 2026 Shared Task 2: Blunt-Edge Models for Hybrid Grading

This system description paper presents a submission to the BEA 2026 Shared Task on Rubric-based Short Answer Scoring for German. It describes a multi-strategy scoring pipeline combining rubric-conditioned prompting, LoRA fine-tuning of Qwen-family models, and prediction aggregation. The system ranks second on three official tracks and third on the remaining track.

Multi-Strategy Rubric-Based Short Answer Scoring for German

This paper describes our submission to the BEA 2026 shared task on vocabulary item difficulty prediction in multilingual settings. We investigated whether transformer-based representations learned directly from item content can support the prediction of vocabulary item difficulty across different L1 groups. Our approach adopted a multilingual BERT-based architecture, specifically the mmBERT, with representation augmentation at both the layer and token levels, followed by a multi-task cascade learning that incorporates part-of-speech information as an auxiliary structural signal. Results showed that multi-task mmBERT consistently outperforms the shared-task XLM-RoBERTa baseline across languages, while gains from more complex aggregation are not uniform. The findings showed that strong multilingual representations provide a competitive foundation for vocabulary item difficulty prediction, while the benefits of additional architectural complexity depend on the language and training setting.

Precalibration of Vocabulary Item Difficulty with Multilingual Transformers and Multi-Task Learning

Humor is a complex form of communication that remains challenging for machines. Despite its broadness, most existing research on computational humor traditionally focused on modeling one specific type of humor. In this work, we wish to understand whether competence on specific humor tasks confers any ability to transfer to novel, unseen types; in other words, is this fragmentation inevitable? This question is especially timely as new humor types continuously emerge in online contexts (e.g., memes, anti-humor, AI fails). If LLMs are to keep up with this evolving landscape, they must be able to capture deeper, transferable mechanisms. 
To investigate this, we conduct a series of transfer learning experiments across four datasets, representing different humor tasks. We explore varied diversity settings (varying between 1-3 datasets in training, testing on a novel one). Experiments show that models are capable of some transfer, reaching up to 75% accuracy on binary unseen datasets; training on diverse sources improves transferability (1.88-4.05%) with minimal-to-no drop in in-domain performance. Somewhat surprisingly, the one dataset (Dad Jokes) emerges as the best enabler of transfer, but the hardest one to transfer to. We release data and code.

One Joke to Rule them All? On the (Im)possibility of Generalizing Humor Detection

Stand-up comedy, and humor in general, are often studied through their verbal content. Yet live performance relies just as much on embodied presence and audience feedback. We introduce TIC-TALK, a multimodal resource with 5,400+ temporally aligned topic segments capturing language, gesture, and audience response across 90 professionally filmed stand-up comedy specials (2015–2024). The pipeline combines BERTopic for 60 s thematic segmentation with dense sentence embeddings, Whisper-AT for 0.8 s laughter detection, a fine-tuned YOLOv8-cls shot classifier, and YOLOv8s-pose for raw keypoint extraction at 1 fps. Raw 17-joint skeletal coordinates are retained without prior clustering, enabling the computation of continuous kinematic signals—arm spread, kinetic energy, and trunk lean—that serve as proxies for performance dynamics. All streams are aligned by hierarchical temporal containment without resampling, and each topic segment stores its sentence-BERT embedding for downstream similarity and clustering tasks. As a concrete use case, we study laughter dynamics across 24 thematic topics: kinetic energy negatively predicts audience laughter rate (r=−0.75,N= 24), consistent with a stillness-before-punchline pattern; personal and bodily content elicits more laughter than geopolitical themes; and shot close-up proportion correlates positively with laughter (r= +0.28), consistent with reactive montage.

Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics (TIC-TALK): Pipeline and Database for the Multimodal Study of Comedic Timing

Humor is one of the most culturally embedded and socially significant dimensions of human communication, yet it remains largely unexplored as a dimension of Large Language Model (LLM) alignment. In this study, five frontier language models play the same Cards Against Humanity games (CAH) as human players. The models select the funniest response from a slate of ten candidate cards across 9,894 rounds. While all models exceed the random baseline, alignment with human preference remains modest. More striking is that models agree with each other substantially more often than they agree with humans. We show that this preference is partly explained by systematic position biases and content preferences, raising the question whether LLM humor judgment reflects genuine preference or structural artifacts of inference and alignment.

Cards Against LLMs: Benchmarking Humor Alignment in Large Language Models

We present exploratory experiments in the comedic roasting capabilities of GPT4o. Specifically, @ComedyCentral roasts were scraped to design a survey in which participants blindly evaluated snippets of human and AI roasts, and had to predict the author (AI/human) in a second round of reviewing. The results show that there is no significant difference in how the barbs in human- and AI-generated roasts are rated. Further, a qualitative analysis showed that although the model utilizes specific recurrent phrases to imitate the style of human comedians, both generative LLM detectors and humans performed suboptimally in predicting the true author of the roasts.

The Roast of GPT4o: Experiments in Generating, Detecting and Evaluating Celebrity Roast Comedy

We investigate whether scaling model parameters improves humor generation through a controlled ablation study. Using five Qwen3 variants (8B–235B, dense and MoE), we generate jokes across 50 themes. Beyond evaluating humor scaling, this work serves as an empirical study into the nature of LLM versus human evaluations on highly subjective creative tasks. While an automated judge yields a perfect monotonic ranking between parameter count and win rate, human annotators find no significant aggregate difference in humor quality. Restricting to themes where annotators agree reveals a significant preference for the largest model (p = 0.039), suggesting scaling effects exist but are masked by a "quality floor." Crucially, our analysis of bias characteristics shows that the automated judge exhibits severe positional and length biases compared to human evaluators, further suggesting that LLMs may systematically distort quality differences on subjective tasks.

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Tracing Evidence of Productive Use for Learner Modeling in an LLM-Based Grammar Tutor

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Scientific and Creative Analogies in Pretrained Language Models