RoMa at HAHA-2021: Deep Reinforcement Learning to Improve a Transformed-based Model for Humor Detection

Abstract

[EN] In this paper, we describe our system we participated in the shared task ¿Humor Analysis based on Human Annotation (HAHA) at IberLEF-2021 with. Our system relies on data representations learned through fine-tuned neural language models. The representations are used to train a Siamese Neural Network (SNN) which learns to verify whether or not a pair of tweets belong to the same or distinct classes. A key point in our model is the heuristic used to create the pair of messages in the training and test phases. For that, we used a Deep Reinforcement Learning (DRL) strategy that aims at identifying a set of optimal prototypes in each class. In general, the results achieved are encouraging and give us a starting point for further improvements.