Samuel Akrah


2022

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DuluthNLP at SemEval-2022 Task 7: Classifying Plausible Alternatives with Pre–trained ELECTRA
Samuel Akrah | Ted Pedersen
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper describes the DuluthNLP system that participated in Task 7 of SemEval-2022 on Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts. Given an instructional text with an omitted token, the task requires models to classify or rank the plausibility of potential fillers. To solve the task, we fine–tuned the models BERT, RoBERTa, and ELECTRA on training data where potential fillers are rated for plausibility. This is a challenging problem, as shown by BERT-based models achieving accuracy less than 45%. However, our ELECTRA model with tuned class weights on CrossEntropyLoss achieves an accuracy of 53.3% on the official evaluation test data, which ranks 6 out of the 8 total submissions for Subtask A.

2021

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DuluthNLP at SemEval-2021 Task 7: Fine-Tuning RoBERTa Model for Humor Detection and Offense Rating
Samuel Akrah
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents the DuluthNLP submission to Task 7 of the SemEval 2021 competition on Detecting and Rating Humor and Offense. In it, we explain the approach used to train the model together with the process of fine-tuning our model in getting the results. We focus on humor detection, rating, and of-fense rating, representing three out of the four subtasks that were provided. We show that optimizing hyper-parameters for learning rate, batch size and number of epochs can increase the accuracy and F1 score for humor detection
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