The identification of narrative similarity is a complex NLP challenge that requires modeling deeper plot and thematic alignment rather than relying solely on lexical overlap. In this paper, we detail the participation of team NLP-FSDM in SemEval-2026 Task 4. Our approach utilizes the bge-large-en-v1.5 encoder. For Track A, we fine-tune it using Multiple Negatives Ranking Loss (MNRL), while for Track B we rely on the pretrained encoder to generate fixed narrative representations. We achieved an accuracy of 65.50% in Track A and 62.50% in Track B. This paper provides an extensive comparison of our results with competitive baselines and top-performing systems, analyzing the efficacy of dense encoders in low-resource narrative contexts.

This paper describes the NLP-FSDM system for SemEval-2026 Task 2, Subtask 1 on longitudinal affect assessment. The task requires predicting Valence and Arousal (V & A) scores for sequences of ecological essays and feeling words written over time. We adopt ModernBERT-large as a text encoder and formulate the task as a joint regression problem optimized using a Concordance Correlation Coefficient (CCC) loss combined with a lightly weighted Mean Absolute Error (MAE) term. To reduce variance induced by fine-tuning large transformers on relatively small user-specific datasets, we employ a three-seed ensemble. Finally, we introduce a lightweight post-inference temporal smoothing mechanism applied per user to improve within-user consistency. Our system achieves an rcomposite of 0.546 for Valence and 0.453 for Arousal, demonstrating stable cross-dimensional performance without explicitly modeling sequential dependencies.

This paper presents our system and findings for SemEval 2024 Task 1 Track A Supervised Semantic Textual Relatedness. The main objective of this task was to detect the degree of semantic relatedness between pairs of sentences. Our submitted models (ranked 6/24 in Algerian Arabic, 7/25 in Spanish, 12/23 in Moroccan Arabic, and 13/36 in English) consist of various transformer-based models including MARBERT-V2, mDeBERTa-V3-Base, DarijaBERT, and DeBERTa-V3-Large, fine-tuned using different loss functions including Huber Loss, Mean Absolute Error, and Mean Squared Error.

This paper presents our systems and findings for SemEval-2023 Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages. The main objective of this task was to determine the polarity of a tweet (positive, negative, or neutral). Our submitted models (highest rank is 1 and lowest rank is 21 depending on the target Track) consist of various Transformer-based approaches.

This paper presents our system and findings for SemEval 2023 Task 9 Tweet Intimacy Analysis. The main objective of this task was to predict the intimacy of tweets in 10 languages. Our submitted model (ranked 28/45) consists of a transformer-based approach with data augmentation via machine translation.