Browsing by Subject "ULMFiT"

In the past two decades, an increasing amount of discussions are held via online platforms such as Facebook or Reddit. The most common form of disruption of these discussions are trolls. Traditional trolls try to digress the discussion into a nonconstructive argument. One strategy to achieve this is to give asymmetric responses, responses that don’t follow the conventional patterns. In this thesis we propose a modern machine learning NLP method called ULMFiT to automatically detect the discourse acts of online forum posts in order to detect these conversational patterns. ULMFiT finetunes the language model before training its classifier in order to create a more accurate language representation of the domain language. This task of discourse act recognition is unique since it attempts to classify the pragmatic role of each post within a conversation compared to the functional role which is related to tasks such as question-answer retrieval, sentiment analysis, or sarcasm detection. Furthermore, most discourse act recognition research has been focused on synchronous conversations where all parties can directly interact with each other while this thesis looks at asynchronous online conversations. Trained on a dataset of Reddit discussions, the proposed model achieves a matthew’s correlation coefficient of 0.605 and an F1-score of 0.69 to predict the discourse acts. Other experiments also show that this model is effective at question-answer classification as well as showing that language model fine-tuning has a positive effect on both classification performance along with the required size of the training data. These results could be beneficial for current trolling detection systems.