Humour Translation with Transformers

Abstract

This paper presents the solution proposed by team FAST-MT to the shared tasks of JOKER CLEF 2022 Automatic pun and humour translation. State-of-the-art Transformer-based models are used to solve the three tasks introduced in the JOKER CLEF workshop. The Transformer model is a kind of neural network that tries to learn the contextual information from the sequential data by implicitly comprehending the existing relationships. In task 1, given a piece of text, we need to classify/explain any instance of wordplay is present in it or not. The proposed solution to task 1 combines the pipeline of token classification, text classification, and text generation. In task 2, we need to translate single words (nouns) containing a wordplay. This task is mapped to the problem of question answering (Q/A) on programmatically extracted texts from the OPUS parallel corpus. In task 3, contestants are required to translate the entire phrase containing the wordplay. Sequence to sequence translation models are used to solve this task. The team has adopted different strategies for each task as they suited to the requirements therein. The paper reports proposed solutions, implementation details, experimental studies, and results obtained in JOKER CLEF 2022 automatic pun and humour translation tasks.