## 4 Experiments
### Models for Comparison  
We compare the performance of MIss WiLDe's 3 variants with 9 other models, which are the following:  
* **MDGI-Joint-S** and **MDGI-Joint** (denoted as MDGI-J-S and MDGI-J). These are the two variants of the model designed by [PERSON] et al. in [23]. The outline of the model has already been presented in Section 2.1. The first variant of the model (MDGI-Joint-S) shares parameters between the context encoder and definition encoder, while the other one uses independent encoders. Although in the paper, authors present part of their results as achieved using VUA-ALL-POS, this seems to be inaccurate (cf. the datasets available at their GitHub: [[https://github.com/sysulic/MDGI](https://github.com/sysulic/MDGI)]([https://github.com/sysulic/MDGI](https://github.com/sysulic/MDGI)); last accessed on 21 December 2021), and therefore we place them in Table 2, which shows the results for VUA-SEQ/VUA-18. The results for VUA-VERB reported by the authors can be found in Table 3. As for TroFi, we use it only as the test set for the model trained on VUA-20 and thus the results reported in [23] are not comparable with ours.
* **BERT**. The model presented by [PERSON] et al. [19] using the uncased base BERT model as its backbone and some standard hyperparameters. In the following tables, we present the results reported by the authors.
* **RNN_HG** and **RNN_MHCA**. The two models built upon [PERSON] et al. [35] and presented by [PERSON] et al. in [36]. The first model follows the guidelines of MIP (Metaphor Identification Procedure), while the other follows SPV (Selectional Preference Violation). The first model uses GloVe (Global Vectors) embedding as the representation of the target word's literal meaning and the hidden state from the BiLSTM fed with the concatenation of GloVe and ELMo embeddings as the representation of its contextual meaning. In order to compute the contextual representation of the target word, RNN_MHCA uses multi-head contextual attention.
* **RoBERTa_BASE** (denoted as RoB_BASE). A vanilla version of the RoBERTa model designed for metaphor detection prepared as a baseline by [PERSON] et al. Unlike MelBERT, it utilizes all-to-all interaction architecture.
* **RoBERTa_SEQ** (denoted as RoB_SEQ). To paraphrase [PERSON] et al., this model takes one single sentence as an input, where the target word is marked as the input embedding token. The model predicts the metaphoricity of the target word using its embedding vector.  
* **DeepMet**. The model designed by [PERSON] et al. [21], the winner of the Metaphor Detection Shared Task 2020. Its outline has already been presented in Section 2.1.
* **MelBERT**. Its outline has already been presented in Section 2.1 and, in comparison with our model, in Section 3.1.
* **Miss WiLDe_base**, **Miss WiLDe_cos** and **Miss WiLDe_sbert** (denoted as MsW_base, MsW_cos, and MsW_sbert). These are the three variants of our model described in details in Section 3.1. As signalized by their names, the first one is a core model, the second one uses a cosine-similarity measure as an additional feature, and the third one uses the Sentence-BERT encoder in place of RoBERTa, which is utilized in the first two sub-models.