Mexico

In this paper, we present an approach for solving SemEval-2024 Task 3: The Competition of Multimodal Emotion Cause Analysis in Conversations. The task includes two subtasks that focus on emotion-cause pair extraction using text, video, and audio modalities. Our approach is composed of encoding all modalities (MFCC and Wav2Vec for audio, 3D-CNN for video, and transformer-based models for text) and combining them in an utterance-level fusion module. The model is then optimized for link and emotion prediction simultaneously. Our approach achieved 6th place in both subtasks. The full leaderboard can be found at \url{https://codalab.lisn.upsaclay.fr/competitions/16141#results}.

NAACL 2024

UWBA at SemEval-2024 Task 3: Dialogue Representation and Multimodal Fusion for Emotion Cause Analysis

emotion-cause pairs

dialogues

multimodal fusion

multimodality

emotion

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.
 
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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 summarizes our methodology and results for the BEA 2024 Shared Task. This competition focused on predicting item difficulty and response time for retired multiple-choice items from the United States Medical Licensing Examination® (USMLE®). We extracted linguistic features from the item stem and response options using multiple methods, including the BiomedBERT model, FastText embeddings, and Coh-Metrix. The extracted features were combined with additional features available in item metadata (e.g., item type) to predict item difficulty and average response time. The results showed that the BiomedBERT model was the most effective in predicting item difficulty, while the fine-tuned model based on FastText word embeddings was the best model for predicting response time.

Item Difficulty and Response Time Prediction with Large Language Models: An Empirical Analysis of USMLE Items

This paper presents our system developed for the Shared Task on Automated Prediction of Item Difficulty and Item Response Time for USMLE questions, organized by the Association for Computational Linguistics (ACL) Special Interest Group for building Educational Applications (BEA SIGEDU). The Shared Task, held as a workshop at the North American Chapter of the Association for Computational Linguistics (NAACL) 2024 conference, aimed to advance the state-of-the-art in predicting item characteristics directly from item text, with implications for the fairness and validity of standardized exams. We compared various methods ranging from BERT for regression to Random forest, Gradient Boosting(GB), Linear Regression, Support Vector Regressor (SVR), k-nearest neighbours (KNN) Regressor, MultiLayer Perceptron(MLP) to custom-ANN using BioBERT and Word2Vec embeddings and provided inferences on which performed better. This paper also explains the importance of data augmentation to balance the data in order to get better results. We also proposed five hypotheses regarding factors impacting difficulty and response time for a question and also verified it thereby helping researchers to derive meaningful numerical attributes for accurate prediction. We achieved a RSME score of 0.315 for Difficulty prediction and 26.945 for Response Time.

Leveraging Physical and Semantic Features of text item for Difficulty and Response Time Prediction of USMLE Questions

This work presents a novel framework for the automated prediction of item difficulty and response time within educational assessments. Utilizing data from the BEA 2024 Shared Task, we integrate Named Entity Recognition, Semantic Role Labeling, and linguistic features to prompt a Large Language Model (LLM). Our best approach achieves an RMSE of 0.308 for item difficulty and 27.474 for response time prediction, improving on the provided baseline. The framework's adaptability is demonstrated on audio recordings of 3rd-8th graders from the Atlanta, Georgia area responding to the Test of Narrative Language. These results highlight the framework's potential to enhance test development efficiency.

Large Language Model-based Pipeline for Item Difficulty and Response Time Estimation for Educational Assessments

We report the findings of the 2024 Multilingual Lexical Simplification Pipeline shared task. We released a new dataset comprising 5,927 instances of lexical complexity prediction and lexical simplification on common contexts across 10 languages, split into trial (300) and test (5,627). 10 teams participated across 2 tracks and 10 languages with 233 runs evaluated across all systems. Five teams participated in all languages for the lexical complexity prediction task and 4 teams participated in all languages for the lexical simplification task. Teams employed a range of strategies, making use of open and closed source large language models for lexical simplification, as well as feature-based approaches for lexical complexity prediction. The highest scoring team on the combined multilingual data was able to obtain a Pearson's correlation of 0.6241 and an ACC@1@Top1 of 0.3772, both demonstrating that there is still room for improvement on two difficult sub-tasks of the lexical simplification pipeline.

The BEA 2024 Shared Task on the Multilingual Lexical Simplification Pipeline

This paper presents the description and primary outcomes of our team's participation in the BEA 2024 shared task. Our primary exploration involved employing transformer-based systems, particularly BERT models, due to their suitability for Natural Language Processing tasks and efficiency with computational resources. We experimented with various input formats, including concatenating all text elements and incorporating only the clinical case. Surprisingly, our results revealed different impacts on predicting difficulty versus response time, with the former favoring clinical text only and the latter benefiting from including the correct answer. Despite moderate performance in difficulty prediction, our models excelled in response time prediction, ranking highest among all participants. This study lays the groundwork for future investigations into more complex approaches and configurations, aiming to advance the automatic prediction of exam difficulty and response time.

UNED team at BEA 2024 Shared Task: Testing different Input Formats for predicting Item Difficulty and Response Time in Medical Exams

Hallucinations in large language models (LLMs) have recently become a significant problem. A recent effort in this direction is a shared task at Semeval 2024 Task 6, SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes (Mickus et al., 2024). This paper describes our winning solution ranked 1st and 2nd in the 2 sub-tasks of model agnostic and model aware tracks respectively. We propose a metaregressor framework of LLMs for model evaluation and integration that achieves the highest scores on the leader board. We also experiment with various transformer based models and black box methods like ChatGPT, Vectara, and others. In addition, we perform an error analysis comparing GPT3.5 against our best model which shows the limitations of the former.

Halu-NLP at SemEval-2024 Task 6: MetaCheckGPT - A Multi-task Hallucination Detection using LLM uncertainty and meta-models

This presentation describes the approach of the UniBuc - NLP team in tackling the SemEval 2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. We explored transformer-based and hybrid deep learning architectures. Our transformer-based model achieved a strong result, demonstrating the architecture's suitability for the task of detecting exact model that generated the text. However, our hybrid deep learning model overfit during training, hindering its ability to detect transitions between human and machine-generated text.

Team Unibuc - NLP at SemEval-2024 Task 8: Transformer and Hybrid Deep Learning Based Models for Machine-Generated Text Detection

Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and OpenAI, LLMs are now more accessible, and people can easily use them. However, an important issue is how we can detect AI-generated texts from human-written ones. In this article, we have investigated the problem of AI-generated text detection from two different aspects: semantics and syntax. Finally, we presented an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks using the M4 dataset. According to our results, using a semantic approach would be more helpful for detection. However, there is a lot of room for improvement in the syntactic approach, and it would be a good approach for future work.

RFBES at SemEval-2024 Task 8: Investigating Syntactic and Semantic Features for Distinguishing AI-Generated and Human-Written Texts

This paper describes the BAMBAS team’s par- ticipation in SemEval-2024 Task 4 Subtask 1, which focused on the multilabel classification of persuasion techniques in the textual content of Internet memes. We explored a lightweight approach that does not consider the hierarchy of labels. First, we get the text embeddings lever- aging the multilingual tweets-based language model, Bernice. Next, we use those embed- dings to train a separate binary classifier for each label, adopting independent oversampling strategies in each model in a binary-relevance style. We tested our approach over the English dataset, exceeding the baseline by 21 percent- age points, while ranking in 23th in terms of hierarchical F1 and 11st in terms of hierarchical recall.

BAMBAS at SemEval-2024 Task 4: How far can we get without looking at hierarchies?

This study explores Task 2 in NumEval-2024, which is SemEval-2024(Semantic Evaluation) Task 7 , focusing on the Reading Comprehen sion of Numerals in Text (Chinese). The dataset utilized in this study is the Numeral-related Question Answering Dataset (NQuAD), and the model employed is BERT. The data un dergoes preprocessing, incorporating Numer als Augmentation and Feature Enhancement to numerical entities before model training. Addi tionally, fine-tuning will also be applied. The result was an accuracy rate of 77.09%, repre senting a 7.14% improvement compared to the initial NQuAD processing model, referred to as the Numeracy-Enhanced Model (NEMo).

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