In this system paper for SemEval-2024 Task 1 subtask A, we present our approach to evaluating the semantic relatedness of sentence pairs in nine languages. We use a mix of statistical methods combined with fine-tuned BERT transformer models for English and use the same model and machine-translated data for the other languages. This simplistic approach shows consistently reliable scores and achieves above-average rank in all languages.

In this system paper for SemEval-2024 Task3 subtask 2, I present my simple textual approach to emotion classification and emotioncause analysis in conversations using machinelearning and next sentence prediction. I train aSpaCy model for emotion classification and usenext sentence prediction with BERT for emotion cause analysis. While speaker names andaudio-visual clips are given in addition to textof the conversations, my approach uses textualdata only to test my methodology to combinemachine learning with next sentence prediction.This paper reveals both strengths and weaknesses of my trial, suggesting a direction offuture studies to improve my introductory solution.

In this system paper for SemEval-2024 Task4 subtask 2b, I present my approach to identifying propagandistic memes in multiple languages. I firstly establish a baseline for Englishand then implement the model into other languages (Bulgarian, North Macedonian and Arabic) by using machine translation. Data fromother subtasks (subtask 1, subtask 2a) are alsoused in addition to data for this subtask, andadditional data from Kaggle are concatenatedto these in order to enhance the model. Theresults show a high reliability of my Englishbaseline and a room for improvement of itsimplementation.

In this system paper for SemEval-2024 Task10 subtask 1 (ERC), I present my approach torecognizing emotions in Hindi-English codemixed conversations. I train a SpaCy modelwith English translated data and classify emotions behind Hindi-English code-mixed utterances by using the model and translating theminto English. I use machine translation to translate all the data in Hindi-English mixed language into English due to an easy access to existing data for emotion recognition in English.Some additional data in English are used to enhance my model. This English based approachdemonstrates a fundamental possibility and potential of simplifying code-mixed language intoone major language for emotion recognition.