---
res:
  bibo_abstract:
  - Metaphorical language, such as {“}spending time together{”}, projects meaning
    from a source domain (here, $money$) to a target domain ($time$). Thereby, it
    highlights certain aspects of the target domain, such as the $effort$ behind the
    time investment. Highlighting aspects with metaphors (while hiding others) bridges
    the two domains and is the core of metaphorical meaning construction. For metaphor
    interpretation, linguistic theories stress that identifying the highlighted aspects
    is important for a better understanding of metaphors. However, metaphor research
    in NLP has not yet dealt with the phenomenon of highlighting. In this paper, we
    introduce the task of identifying the main aspect highlighted in a metaphorical
    sentence. Given the inherent interaction of source domains and highlighted aspects,
    we propose two multitask approaches - a joint learning approach and a continual
    learning approach - based on a finetuned contrastive learning model to jointly
    predict highlighted aspects and source domains. We further investigate whether
    (predicted) information about a source domain leads to better performance in predicting
    the highlighted aspects, and vice versa. Our experiments on an existing corpus
    suggest that, with the corresponding information, the performance to predict the
    other improves in terms of model accuracy in predicting highlighted aspects and
    source domains notably compared to the single-task baselines.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Meghdut
      foaf_name: Sengupta, Meghdut
      foaf_surname: Sengupta
      foaf_workInfoHomepage: http://www.librecat.org/personId=99459
  - foaf_Person:
      foaf_givenName: Milad
      foaf_name: Alshomary, Milad
      foaf_surname: Alshomary
      foaf_workInfoHomepage: http://www.librecat.org/personId=73059
  - foaf_Person:
      foaf_givenName: Ingrid
      foaf_name: Scharlau, Ingrid
      foaf_surname: Scharlau
      foaf_workInfoHomepage: http://www.librecat.org/personId=451
    orcid: 0000-0003-2364-9489
  - foaf_Person:
      foaf_givenName: Henning
      foaf_name: Wachsmuth, Henning
      foaf_surname: Wachsmuth
      foaf_workInfoHomepage: http://www.librecat.org/personId=3900
  bibo_doi: 10.18653/v1/2023.findings-emnlp.308
  dct_date: 2023^xs_gYear
  dct_language: eng
  dct_publisher: Association for Computational Linguistics@
  dct_title: Modeling Highlighting of Metaphors in Multitask Contrastive Learning
    Paradigms@
...