---
_id: '55406'
abstract:
- lang: eng
  text: 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.
author:
- first_name: Meghdut
  full_name: Sengupta, Meghdut
  id: '99459'
  last_name: Sengupta
- first_name: Milad
  full_name: Alshomary, Milad
  id: '73059'
  last_name: Alshomary
- first_name: Ingrid
  full_name: Scharlau, Ingrid
  id: '451'
  last_name: Scharlau
  orcid: 0000-0003-2364-9489
- first_name: Henning
  full_name: Wachsmuth, Henning
  id: '3900'
  last_name: Wachsmuth
citation:
  ama: 'Sengupta M, Alshomary M, Scharlau I, Wachsmuth H. Modeling Highlighting of
    Metaphors in Multitask Contrastive Learning Paradigms. In: Bouamor H, Pino J,
    Bali K, eds. <i>Findings of the Association for Computational Linguistics: EMNLP
    2023</i>. Association for Computational Linguistics; 2023:4636–4659. doi:<a href="https://doi.org/10.18653/v1/2023.findings-emnlp.308">10.18653/v1/2023.findings-emnlp.308</a>'
  apa: 'Sengupta, M., Alshomary, M., Scharlau, I., &#38; Wachsmuth, H. (2023). Modeling
    Highlighting of Metaphors in Multitask Contrastive Learning Paradigms. In H. Bouamor,
    J. Pino, &#38; K. Bali (Eds.), <i>Findings of the Association for Computational
    Linguistics: EMNLP 2023</i> (pp. 4636–4659). Association for Computational Linguistics.
    <a href="https://doi.org/10.18653/v1/2023.findings-emnlp.308">https://doi.org/10.18653/v1/2023.findings-emnlp.308</a>'
  bibtex: '@inproceedings{Sengupta_Alshomary_Scharlau_Wachsmuth_2023, place={Singapore},
    title={Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms},
    DOI={<a href="https://doi.org/10.18653/v1/2023.findings-emnlp.308">10.18653/v1/2023.findings-emnlp.308</a>},
    booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
    publisher={Association for Computational Linguistics}, author={Sengupta, Meghdut
    and Alshomary, Milad and Scharlau, Ingrid and Wachsmuth, Henning}, editor={Bouamor,
    Houda and Pino, Juan and Bali, Kalika}, year={2023}, pages={4636–4659} }'
  chicago: 'Sengupta, Meghdut, Milad Alshomary, Ingrid Scharlau, and Henning Wachsmuth.
    “Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms.”
    In <i>Findings of the Association for Computational Linguistics: EMNLP 2023</i>,
    edited by Houda Bouamor, Juan Pino, and Kalika Bali, 4636–4659. Singapore: Association
    for Computational Linguistics, 2023. <a href="https://doi.org/10.18653/v1/2023.findings-emnlp.308">https://doi.org/10.18653/v1/2023.findings-emnlp.308</a>.'
  ieee: 'M. Sengupta, M. Alshomary, I. Scharlau, and H. Wachsmuth, “Modeling Highlighting
    of Metaphors in Multitask Contrastive Learning Paradigms,” in <i>Findings of the
    Association for Computational Linguistics: EMNLP 2023</i>, 2023, pp. 4636–4659,
    doi: <a href="https://doi.org/10.18653/v1/2023.findings-emnlp.308">10.18653/v1/2023.findings-emnlp.308</a>.'
  mla: 'Sengupta, Meghdut, et al. “Modeling Highlighting of Metaphors in Multitask
    Contrastive Learning Paradigms.” <i>Findings of the Association for Computational
    Linguistics: EMNLP 2023</i>, edited by Houda Bouamor et al., Association for Computational
    Linguistics, 2023, pp. 4636–4659, doi:<a href="https://doi.org/10.18653/v1/2023.findings-emnlp.308">10.18653/v1/2023.findings-emnlp.308</a>.'
  short: 'M. Sengupta, M. Alshomary, I. Scharlau, H. Wachsmuth, in: H. Bouamor, J.
    Pino, K. Bali (Eds.), Findings of the Association for Computational Linguistics:
    EMNLP 2023, Association for Computational Linguistics, Singapore, 2023, pp. 4636–4659.'
date_created: 2024-07-26T13:09:20Z
date_updated: 2024-07-26T13:19:53Z
department:
- _id: '600'
- _id: '660'
doi: 10.18653/v1/2023.findings-emnlp.308
editor:
- first_name: Houda
  full_name: Bouamor, Houda
  last_name: Bouamor
- first_name: Juan
  full_name: Pino, Juan
  last_name: Pino
- first_name: Kalika
  full_name: Bali, Kalika
  last_name: Bali
language:
- iso: eng
page: 4636–4659
place: Singapore
project:
- _id: '127'
  name: 'TRR 318 - C4: TRR 318 - Subproject C4 - Metaphern als Werkzeug des Erklärens'
publication: 'Findings of the Association for Computational Linguistics: EMNLP 2023'
publisher: Association for Computational Linguistics
status: public
title: Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms
type: conference
user_id: '3900'
year: '2023'
...