Transformer-based Detection of Multiword Expressions in Flower and Plant Names
This work addresses MWE detection for NLP applications like machine translation, but it is incremental as it applies existing transformers to a new domain-specific dataset.
The paper tackled the problem of detecting multiword expressions (MWEs) in flower and plant names using transformer models, showing that they outperform previous LSTM-based neural models.
Multiword expression (MWE) is a sequence of words which collectively present a meaning which is not derived from its individual words. The task of processing MWEs is crucial in many natural language processing (NLP) applications, including machine translation and terminology extraction. Therefore, detecting MWEs in different domains is an important research topic. In this paper, we explore state-of-the-art neural transformers in the task of detecting MWEs in flower and plant names. We evaluate different transformer models on a dataset created from Encyclopedia of Plants and Flower. We empirically show that transformer models outperform the previous neural models based on long short-term memory (LSTM).