## 3 Materials and Methods
### Datasets  
In this section, we present the datasets used in the experiments. We wanted to confirm the validity of our hypothesis that utilizing lexical definitions of target words would improve the algorithm's performance and in order to do so we have adopted the same datasets as in the work of [PERSON] et al. [10]. The original data is available at the authors' drive, a link to which can be found on their GitHub ([[https://drive.google.com/file/d/1738](https://drive.google.com/file/d/1738) aqPObjfcQg2O7/knrElmUHulNhoqRz/view7 usp=sharing]([https://drive.google.com/file/d/1738](https://drive.google.com/file/d/1738) aqPObjfcQg2O7/knrElmUHulNhoqRz/view7 usp=sharing) via [[https://github.com/jin530/[PERSON]](https://github.com/jin530/[PERSON])]([https://github.com/jin530/[PERSON]](https://github.com/jin530/[PERSON])); last accessed on 21 December 2021). The downloadable repository consists of MOH-X, TroFi, VUA-20 (variant of VUA-ALL-POS known from Metaphor Detection Shared Task [15; 44]), VUA-18 (variant of VUA-SEQ known from [PERSON] et al. [35] and [PERSON] et al. [36]), VUA-VERB, 8 subsets of VUA-18, 4 of which are selected based on the POS tags of the target words (nouns, verbs, adjectives, and adverbs), and another 4 on the genre to which the sentence comprising target word belongs (academic, conversation, fiction, and news). Both Genres and POS are used only for testing. The same datasets enriched with the Wiktionary definitions can be downloaded directly from our GitHub ([[https://github.com/languagemedicalaboratory/ms_wilde/tree/main/data](https://github.com/languagemedicalaboratory/ms_wilde/tree/main/data)]([https://github.com/languagemedicalaboratory/ms_wilde/tree/main/data](https://github.com/languagemedicalaboratory/ms_wilde/tree/main/data)); last accessed on 21 December 2021). MOH-X ([PERSON] et al. [2016] dataset) [27] and TroFi (Trope Finder) [24] are relatively small datasets annotated only for verbs. MOH-X is built with the example sentences taken from WordNet [45], and TroFi with the ones from the Wall Street Journal [46]. For the sake of fair comparison with [PERSON] et al., we use these two datasets only as the test sets for the models trained beforehand on the VUA-20, in the same way as performed by the authors of MelBERT. As they note in the paper, this can be viewed as the zero-shot transfer learning. VUAMC (Vrije Universiteit Amsterdam Metaphor Corpus [20,47], [[http://www.vismet.org/meteor/documentation/home.html](http://www.vismet.org/meteor/documentation/home.html)]([http://www.vismet.org/meteor/documentation/home.html](http://www.vismet.org/meteor/documentation/home.html)); last accessed on 21 December 2021) is the biggest publicly available corpus annotated for token-level metaphor detection and it is seemingly the most popular one in the field. It comprises text fragments sampled from the British National Corpus ([[http://www.natorp.ox.ac.uk/](http://www.natorp.ox.ac.uk/)]([http://www.natorp.ox.ac.uk/](http://www.natorp.ox.ac.uk/)); last accessed on 21 December 2021). Sentences it contains were labeled in accordance with MIPVU (Metaphor Identification Procedure VU University Amsterdam), the refined and adjusted version of the already described MIP (Metaphor Identification Procedure). Both VUA-ALL-POS (all-part-of-speech) and VUA-SEQ (Sequential) are based on VUAMC, which has been used in the Metaphor Detection Shared Task, first in 2018 and later in 2020 [15,44]. The repository prepared for the Metaphor Detection Shared Task is provided under the following URL: [[https://github.com/EducationalTestingService/metaphor/tree/master/VUA-shared-taskl](https://github.com/EducationalTestingService/metaphor/tree/master/VUA-shared-taskl)]([https://github.com/EducationalTestingService/metaphor/tree/master/VUA-shared-taskl](https://github.com/EducationalTestingService/metaphor/tree/master/VUA-shared-taskl)) (last accessed on 21 December 2021).