NLP @ UIT at F ig L ang- EMNLP 2022: A Divide-and-Conquer System For Shared Task On Understanding Figurative Language (2022)

Abstract: This paper describes our submissions to the EMNLP 2022 shared task on Understanding Figurative Language as part of the Figurative Language Workshop (FigLang 2022). Our systems based on pre-trained language model T5 are divide-and-conquer models which can address both two requirements of the task: 1) classification, and 2) generation. In this paper, we introduce different approaches in which each approach we employ a processing strategy on input model. We also emphasize the influence of the types of figurative language on our systems.

## introduction recent years have witnessed the great rise of artificial intelligence (ai). due to the performance of ai, many downstream tasks from any fields are solved efficiently. one of the central topic in ai is natural language understanding (nlu) in which natural language inference (nli) or recognizing textual entailment (rte) plays an important role, which was pointed out in @xcite . while rte was defined as a task of determining whether a natural language hypothesis h can be inferred from a given premise p (maccartney, 2009), figurative language understanding (flu) was considered as a task of determining whether any figure of speech depends on a non-literal meaning of some or all of the words used @xcite . therefore, flu can be framed as a kind of rte task @xcite . in addition, the emnlp 2022 shared task requires not only to generate the label (entail/contradict), but also to generate a plausible explanation for the prediction, whose example is shown in ## conclusion and future work in this paper, we have presented our system for the emnlp 2022 shared task on the figurative language understanding. our systems are built on fine-tuning pre-trained language model t5 with different input processing strategies, which is a divide-and-conquer model which integrated two or three components: nli predictor, type predictor, and generator. the performance of models are relied how successful the type predictor is, which means the attribute about types of figurative language should be considered as an integral factor of the input of model. due to limited time and resources, we had not conducted thorough enough experiments to get better results, but the system and the involvement in this challenge bring us a good groundwork for further study. in the future, we plan to expand the experiment by employing and fine-tuning other pretrained language models. furthermore, we may also explore different strategies making the most of what we have for the input of models.