UMBCLU at SemEval-2024 Task 1A and 1C: Semantic Textual Relatedness with and without machine translation

1 code implementation • 20 Feb 2024 • Shubhashis Roy Dipta, Sai Vallurupalli

The aim of SemEval-2024 Task 1, "Semantic Textual Relatedness for African and Asian Languages" is to develop models for identifying semantic textual relatedness (STR) between two sentences using multiple languages (14 African and Asian languages) and settings (supervised, unsupervised, and cross-lingual).

Machine Translation Natural Language Understanding +7

HU at SemEval-2024 Task 8A: Can Contrastive Learning Learn Embeddings to Detect Machine-Generated Text?

1 code implementation • 19 Feb 2024 • Shubhashis Roy Dipta, Sadat Shahriar

This paper describes our system developed for SemEval-2024 Task 8, ``Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection'' Machine-generated texts have been one of the main concerns due to the use of large language models (LLM) in fake text generation, phishing, cheating in exams, or even plagiarizing copyright materials.

Contrastive Learning Data Augmentation +2

SeeBel: Seeing is Believing

1 code implementation • 18 Dec 2023 • Sourajit Saha, Shubhashis Roy Dipta

While the significance of discovering and introspecting the correlation between dataset statistics and AI model performance for dense prediction computer vision tasks such as semantic segmentation is well established in the computer vision literature, to the best of our knowledge, no visualization tools have been proposed to view and analyze the aforementioned tasks.

Segmentation Semantic Segmentation

Semantically-informed Hierarchical Event Modeling

1 code implementation • 20 Dec 2022 • Shubhashis Roy Dipta, Mehdi Rezaee, Francis Ferraro

Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches.