## 2 Related Work  
### Research in Natural Language Processing  
Metaphor detection using deep learning has gained much popularity over the last few years, as shown in the related survey [13]. To some degree, this growth in interest can be attributed to the emergence of language models like BERT (Bidirectional Encoder Representations from Transformers) [14] achieving state-of-the-art performance irrespective of the NLP task they are being used for. This tendency can be noticed by looking at the list of models participating in the second Metaphor Detection Shared Task [15], where almost all of them use the implementations of ELMo (Embeddings from Language Model) [16], BERT, or some of its derivatives, such as RoBERTa (Robustly Optimized BERT Pretraining Approach) [17] or ALBERT (A Lite BERT) [18]. Presumably, this constatation led [PERSON] et al. to publish their analysis [19] of recent metaphor recognition systems based on the language models. The authors argue that although the new models yield very satisfactory results, their design often shows considerable gaps from a linguistic perspective, indicated by the fact that they perform substantially worse on unconventional metaphors than on conventional ones. Subsequently, they present another finding that should be of great value to the whole community. First, the reader should know that VUAMC (Vrije Universiteit Amsterdam Metaphor Corpus) [20] is the corpus underlying the two most frequently used datasets in metaphor detection research, specifically VUA-ALL-POS (All Parts of Speech) and VUA-SEQ (Sequential). [PERSON] et al. reveal in their paper [19] that in recent research, some authors compare their results achieved using VUA-SEQ to the results gained on VUA-ALL-POS. As they point out, the underlying corpus remains the same, but the VUA-SEQ is substantially easier to do well on than VUA-ALL-POS, and thus such comparisons are inherently unfair. Later in the paper, the authors present results for both VUA-SEQ and VUA-ALL-POS using a number of models published by other researchers and their own model as well. Implementation of their method achieves an F1-score of 77.5% on VUA-SEQ and only 69.7% on VUA-ALL-POS, which indeed proves that conflating these two cannot be considered good practice. DeepMet [21] is the winner of the second Metaphor Detection Shared Task mentioned before. It managed to outperform all the other models on any given data subset, often by a large margin. DeepMet uses RoBERTa as its structural foundation, and siamese architecture with two Transformer [22] encoder layers to process different features. The authors reformulate a metaphor detection task from a classification or sequence labeling problem to a reading comprehension task. DeepMet utilizes 5 categories of input features: global text context, local text context, query word, general POS (parts of speech), and fine-grained POS generated using SpaCy ([[https://spacy.io/](https://spacy.io/)]([https://spacy.io/](https://spacy.io/)); last accessed on 21 December 2021). The overall performance is boosted by using ensemble learning and metaphor preference parameter \(\alpha\), helping the model to achieve a better recall score. This parameter is introduced due to the fact that the metaphor datasets are highly unbalanced, meaning they comprise many more target words belonging to the non-metaphorical class. Similar to our approach, in the work of [PERSON] et al. [23], dictionary definitions are also used to improve the performance of the proposed BERT-based model. It is noteworthy that the authors perform not only metaphor detection but metaphor interpretation as well. In order to do so, they utilize every definition of the given word that is available in the Merriam Webster dictionary ([[https://www.merriam-webster.com/dictionary/](https://www.merriam-webster.com/dictionary/)]([https://www.merriam-webster.com/dictionary/](https://www.merriam-webster.com/dictionary/)); last accessed on 21 December 2021).