@inproceedings{yayavaram-etal-2024-bert,
    title = "{BERT}-based Idiom Identification using Language Translation and Word Cohesion",
    author = "Yayavaram, Arnav  and
      Yayavaram, Siddharth  and
      Upadhyay, Prajna Devi  and
      Das, Apurba",
    editor = {Bhatia, Archna  and
      Bouma, Gosse  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Evang, Kilian  and
      Garcia, Marcos  and
      Giouli, Voula  and
      Han, Lifeng  and
      Nivre, Joakim  and
      Rademaker, Alexandre},
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.mwe-1.26",
    pages = "220--230",
    abstract = "An idiom refers to a special type of multi-word expression whose meaning is figurative and cannot be deduced from the literal interpretation of its components. Idioms are prevalent in almost all languages and text genres, necessitating explicit handling by comprehensive NLP systems. Such phrases are referred to as Potentially Idiomatic Expressions (PIEs) and automatically identifying them in text is a challenging task. In this paper, we propose using a BERT-based model fine-tuned with custom objectives, to improve the accuracy of detecting PIEs in text. Our custom loss functions capture two important properties (word cohesion and language translation) to distinguish PIEs from non-PIEs. We conducted several experiments on 7 datasets and showed that incorporating custom objectives while training the model leads to substantial gains. Our models trained using this approach also have better sequence accuracy over DISC, a state-of-the-art PIE detection technique, along with good transfer capabilities.",
}

An idiom refers to a special type of multi-word expression whose meaning is figurative and cannot be deduced from the literal interpretation of its components. Idioms are prevalent in almost all languages and text genres, necessitating explicit handling by comprehensive NLP systems. Such phrases are referred to as Potentially Idiomatic Expressions (PIEs) and automatically identifying them in text is a challenging task. In this paper, we propose using a BERT-based model fine-tuned with custom objectives, to improve the accuracy of detecting PIEs in text. Our custom loss functions capture two important properties (word cohesion and language translation) to distinguish PIEs from non-PIEs. We conducted several experiments on 7 datasets and showed that incorporating custom objectives while training the model leads to substantial gains. Our models trained using this approach also have better sequence accuracy over DISC, a state-of-the-art PIE detection technique, along with good transfer capabilities.
Yayavaram, Arnav Yayavaram, Siddharth Upadhyay, Prajna Devi Das, Apurba
BERT-based Idiom Identification using Language Translation and Word Cohesion