Opinions are buildings: Metaphors in secondary education foreign language learning
Conference contribution (Article) › Research › Peer reviewed
Publication data
| By | Anna Hülsing, Andrea Horbach |
| Original language | English |
| Published in | Thomas Gaillat, Cyriel Mallart, Fabienne Moreau, Jen-Yu Li, Griselda Drouet, David Alfter, Elena Volodina, Arne Jönsson (Eds.), Proceedings of the 13th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2024) |
| Pages | 78-95 |
| Editor (Publisher) | LiU Electronic Press |
| ISBN | 978-91-8075-774-4 |
| DOI/Link | https://aclanthology.org/2024.nlp4call-1.7/ |
| Publication status | Published – 10.2024 |
Automatic metaphor detection has been an active field of research for years. Yet, it was rarely investigated how automatic metaphor detection can aid language learning. We therefore present MEWSMET, a corpus of argumentative
essays (MEWS1) written by English as Foreign Language (EFL) learners annotated
for metaphors. We differentiate between two kinds of metaphors: metaphors that are comprehensible to native speakers, even though they themselves would not use them (comprehensible metaphors, CMs) and metaphors that native speakers would use (target language metaphors, TLMs). We use MEWSMET in two ways: Firstly, we analyze our annotations and find out that there is a positive linear correlation
between essay score and the number of TLMs, while no correlation is found between
essay score and the number of CMs. Secondly, we explore how metaphor detection
models perform on MEWSMET. We find that metaphor detection is a hard task given
our noisy learner data, and that metaphor detection models tend to be better at identifying all metaphors (TLMs+CMs) instead of just TLMs, even though only TLMs can be used as a feature for automatic essay-scoring.
