Opinions are buildings: Metaphors in secondary education foreign language learning

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.