SlowGuess/NAACL_2021_OCR huggingface dataset:
Table 1: Summary metaphor identification systems, sorted by dataset and performance. S: sentence. Chunk: sentence fragment. Conc: concreteness. Image: imageability. Emb: embeddings. A: accuracy.  $\star$ : use of a cognitively-inspired method.
sine similarity in a task-specific vector space. This allows the model to learn which dimensions of similarity are most relevant in particular metaphoric comparisons. The system outperformed Shutova et al. (2016) without the use of visual representations. Mao et al. (2018) dealt with word-level metaphor identification in sentences. Given a target word in a sentence, the system searches WordNet for the synonym or direct hypernym of the target word most similar to the context words. The target word is metaphorically used if its cosine similarity with the selected word is below a threshold.
Metaphor and emotion Motivated by the close relationship between metaphor use and the expression of emotions, Gargett and Barnden (2015) successfully used emotion features, amongst others, for metaphor identification. Kozareva (2013) and Strzalkowski et al. (2014) model the affect carried by metaphors in texts in different languages. Most recently, Dankers et al. (2019) employed multitask learning (MTL) to train models of metaphor identification and emotion prediction jointly. Models were based on BiLSTM and BERT, with a range of MTL architectures. The emotion prediction task used the Valence-Arousal-Dominance model
(Mehrabian, 1996), and each of these was considered separately in a MTL setup. The best performance was achieved with the BERT architecture. Dankers et al. (2019) found that while predicting dominance was the most challenging task on the emotion side, it also provided the greatest and most consistent improvements to metaphor identification, and vice versa.
4.2 Automated metaphor interpretation
Recent research on automated metaphor interpretation mainly followed Shutova (2010) in treating the problem as a paraphrasing task. Su et al. (2017) proposed a property transfer process for the interpretation of copula metaphors in Chinese and English. Given a target-source pair, the system extracts source-domain properties from hand-crafted databases<sup>2</sup>, represented as adjectives. It then selects the property that contributes the most to the semantic relatedness of the target-source pair. The resultant pair of target-domain word and property is taken as interpretation of the copula metaphor. The metaphor LOVE IS TIDE, for instance, was interpreted as The love is unstoppable. Su et al.
2For instance, the adjective taxonomy provided by Sardonicus: http://bonnat.ucd.ie/sardonicus/.
(2017) thus took into account the emergent meaning of metaphors. Note that the literal explanations they obtained can be regarded as explanations of conceptual metaphors as well.
The above-mentioned system of Mao et al. (2018) performs both metaphor interpretation and identification: the synonym or hypernym of the metaphoric target word that matches the context can be considered the literal counterpart of the target word. The output interpretations were evaluated through a English-Chinese machine translation task: words classified as metaphorically used were paraphrased prior to translation. The system improved the accuracy of both Google Translation (0.60 vs 0.34) and Bing Translation (0.66 vs 0.42) on the metaphorical sentences in MOH. The experiment thus demonstrated the value of metaphor interpretation for machine translation.
Bizzoni and Lappin (2018) presented a neural model that detects paraphrases of sentences containing metaphor use. Given a metaphorical sentence and a candidate paraphrase, the system uses parallel CNN-LSTM blocks to encode the two sentences separately. The sentence representations are then merged and passed through fully-connected layers to produce a prediction. The system reached an F-score of 0.746 in a binary classification task and a Pearson correlation of 0.553 in a paraphrase ordering task, on the authors' own dataset.
Metaphor identification Word-level metaphor identification is the most studied topic in automated metaphor processing. The systems utilise the latest NLP techniques and findings about metaphor in cognitive sciences; the state-of-the-art performance has been advanced quickly. A limitation in these studies, however, is that the systems do not deal with different types of metaphors. The high percentage of conventional metaphors in VUA (Steen et al., 2010a,b) implies that systems trained on the corpus may not capture conventional and novel metaphor uses equally well. It is the processing of novel metaphors that is a particular challenge for NLU systems, and the current experimental setup does not allow us to explicitly benchmark metaphor identification systems in this ability. The same goes for deliberate metaphor use, which is essential for modelling metaphor in communication and applies to both conventional and novel metaphors.
Whilst there have been huge advances in perfor
mance in word-level metaphor identification, the broader tasks of identifying conceptual metaphors, extended metaphors, and metaphoric framing, have been largely ignored. Conceptual metaphors form the framework for both extended metaphors and metaphoric framing, from a cognitive perspective, and these latter two are a key part of the intentional use of metaphor in communication.
Metaphor interpretation Recent studies on automated metaphor interpretation largely focused on literal paraphrases of metaphorical sentences. The inference of emergent meaning is, however, an integral part of metaphor comprehension and should be captured by metaphor interpretation systems. To capture emergent meaning is to reveal the properties and relations of the target domain emphasised by the metaphor and any additional connotations and emotional effects that arise in the interpretation process. In this respect, the existing paraphrase datasets (Shutova, 2010; Mohammad et al., 2016) are unlikely to reflect the emergent meaning of the metaphors. Bizzoni and Lappin (2018) consider the sentiment poles of metaphorical expressions, but it is questionable to what extent the apt paraphrases also reflect other dimensions, such as the intensity of an emotion.
One way to deal with the inference of linguistic metaphors is to convert it to the task of paraphrasing copula metaphors. As is the case with Su et al. (2017) and Bizzoni and Lappin (2018), the paraphrasing of copula metaphors has been dealt with as obtaining the property of the target domain that is indicated by the metaphor. We can thus deal with the inference of any linguistic metaphor by first identifying its conceptual metaphor and then treating the conceptual metaphor as a copula metaphor. To tackle the task, we will need datasets of the emergent meaning of a wide range of conceptual metaphors. Such datasets can be built from data obtained in psycholinguistic studies about emergent meaning, in which participants were asked to provide concepts or properties that were related to metaphors as well as their target and source domains alone (e.g., Gineste et al., 2000).
A more comprehensive model of metaphor interpretation would also consider the complexity of linguistic metaphors, which essentially arises from the social aspects of metaphor use, such as the various roles that metaphor plays in communication.
Metaphor generation To the best of our knowledge, automated metaphor generation has largely remained an unexplored area. As metaphor is an important tool for reasoning and linguistic expression, the task is a crucial step toward the generation of human-like language. Note, however, that despite the scarcity of explicit attempts at metaphor generation, it is likely to be an inherent part of existing natural language generation (NLG) systems already. Metaphorical use of function words, for instance, are unavoidable for linguistic expression. Highly conventionalised metaphorical use of some content words may also be captured by current NLG techniques. For instance, the use of bright for the description of colours (e.g., bright blue eyes) is considered metaphorical in VUA whereas it is one of the most frequent usages of the adjective. It is likely that data-driven NLG (including neural models) instantiate this usage of the word without 'knowing' that it is a metaphor.
It is thus in the intentional use of metaphors that the difficulty of metaphor generation lies. The development of metaphor generation systems therefore relies on the modelling of metaphor use in communication, which we discuss below.
Implications for other NLP tasks That linguistic metaphors have to do with concepts in two different domains implies that their automated processing is potentially indispensable for resolving referents in text. This is important for a number of NLP applications, such as document summarisation, question answering, and machine translation. The resolution of the actual referents of MRWs is mainly concerned with indirect metaphors, which use source-domain words to refer to target-domain concepts. Note that implicit metaphors can be indirect metaphors as well. It is therefore necessary to take the metaphoricity of function words such as pronouns and determiners into account when it comes to resolving the referent of metaphorically used words. We suggest that the processing of implicit metaphors, especially substitutions, can be facilitated by combining metaphor identification with coreference resolution, as the metaphorically used pronouns essentially refer to the same referents as a preceding metaphorical expression.
Furthermore, there has not been much discussion of the form of metaphor interpretation that would be most beneficial for downstream NLP tasks. Most research on automated metaphor interpretation focuses on producing human-readable
literal paraphrases. However, it is likely that internal model representations that capture the rich meaning of metaphorical language can be more elegantly and successfully integrated in the task models. Consider, for instance, machine translation. After obtaining an internal representation of a metaphorical sentence in the source language, the system can then search for a literal or metaphorical expression in the target language that best matches the representation. We suggest that the development of such a system can begin with investigating the hidden states of existing deep learning models of metaphor identification: the vectors used to output metaphoricity labels may already capture the emergent meaning of the input sentences.
Metaphor in communication In addition to the semantic aspects, metaphor use often also has social implications. Metaphor processing is an essential component of opinion mining: since metaphorical language tends to express stronger emotions than literal language, the presence of metaphors and their inferences would contribute to the sentiment conveyed by a text. It is also worth considering distinguishing deliberate and non-deliberate metaphor uses: focusing on the former may achieve a better approximation of the speaker/writer's sentiment and communicative goals.
Metaphor processing should thus also be incorporated into dialogue modelling. Metaphors could be extended or even evolve with the turn taking in a conversation, as speakers seek mutual understanding, or the resolution of a difference of opinion in argumentative discourse such as debates. Systems that are concerned with speaker's intent will therefore benefit from the identification and interpretation of metaphorical language.
Given the relationship between metaphor and persuasiveness, it may be beneficial to model metaphor use in other types of argumentative discourse, such as political speeches and news articles. A recent development in this regard is a MTL approach to modelling metaphor, emotion, and framing in political texts (Huguet Cabot et al., 2020).
In this paper, we reviewed recent advances in automated metaphor processing and brought to light its less explored aspects, pertaining to cognitive processing of metaphor, its role in communication and its connection with downstream NLP applications. We hope that our work will encourage further in
terdisciplinary research on metaphor and facilitate progress in this important and fascinating area.
We are grateful to the NAACL reviewers for their helpful feedback. Martha Lewis is supported by NWO Veni 'Metaphorical Meanings for Artificial Agents'
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