Metaphor Detection using Context and Concreteness (2020)

Abstract: We report the results of our system on the Metaphor Detection Shared Task at the Second Workshop on Figurative Language Processing 2020. Our model is an ensemble, utilising contextualised and static distributional semantic representations, along with word-type concreteness ratings. Using these features, it predicts word metaphoricity with a deep multi-layer perceptron. We are able to best the state-of-the-art from the 2018 Shared Task by an average of 8.0% F1, and finish fourth in both sub-tasks in which we participate.

## introduction metaphor detection is the task of assigning a label to a word (or sometimes a sentence) in a piece of text, to indicate whether or not it is metaphorical. some metaphors occur so frequently as to be considered word senses in their own right (so-called conventional metaphors), whilst others are creative, and involve the use of words in unexpected ways (novel metaphors). sometimes whole phrases or even sentences can lend themselves to metaphorical or literal interpretations. 1 for these reasons and others, human annotators might disagree about what constitutes a metaphor-computational metaphor detection is no doubt a challenging problem. in this work, we participate in the 2020 metaphor detection shared task @xcite . first, we offer a description of metaphoricity, framing it in terms of the concreteness of a word in different contexts. concreteness of a word in context is not a quantity for which there exists large-scale annotated data. in lieu of this, we train a metaphor detection model using input features which we expect to 1 consider drowning student, which could refer to students submerged in water, or students struggling with coursework @xcite , or the more idiomatic phrase, they stabbed him in the back, which could be taken literally or (more likely) metaphorically, depending on its context. ## concreteness and context metaphor is a device which allows one to project structure from a source domain to a target domain @xcite . for instance, in the sentence "he attacked the government", attacked can be seen as a conventional metaphor, which applies structure from the source domain of war to the target domain of argument. intuitively, it seems that the context in which a word appears tells us about the target domain, whilst the word itself (and some knowledge about how it is used nonmetaphorically) tells us about the source. several existing models have exploited this difference (e.g. @xcite . usually, the target domain is something intangible, whilst the source domain relates more closely to our real-world experience. concreteness refers to the extent to which a word denotes something that can be experienced by the senses, and is gener-ally measured by asking annotators to rate words on a numeric scale @xcite ; abstractness is then the inverse of concreteness. using concreteness ratings for metaphor identification is clearly well motivated, as evidenced by previous work (e.g. @xcite @xcite . for a word to be metaphorical in a particular context, then, it needs to have a concrete sense and an abstract sense, with the abstract sense activated in that context. the concrete sense would belong to the source domain, and the abstract sense to the target domain. for instance, the meaning of the word attacked in "she attacked the soldier" is concrete, but in "she attacked the problem" it is abstract-and thus that usage is metaphorical. polysemy of the word is a necessary condition; the existence of an abstract sense is not enough, otherwise a monosemously abstract word such as considered in "he considered the problem" would be metaphorical. figure 1 shows examples of words with different maximum concreteness levels elicited by certain senses (columns) appearing in contexts which result in different values of concreteness for that particular sense (rows). the most concrete sense of a word is a lexical property, and thus context independent. the metaphors (green) are found in the top right quadrant-they have an abstract meaning in context, but a concrete sense exists (as evidenced by the examples in the bottom right quadrant). the bottom left quadrant is greyed out, since it is conceptually impossible for a word to exist there-the concreteness of one sense of a word cannot be greater than the concreteness of any of its senses. ## model architecture we now describe a model which uses semantic representations of a word in and out of context to predict metaphoricity. ideally, we would only provide the model with a representation of the concreteness of a word in context (since we believe that would do most of the lifting), but to our knowledge, no large-scale annotated datasets exist for contextdependent concreteness. in most popular datasets of concreteness annotation (e.g. @xcite , concreteness is a property assigned to each word type-but we would need the concreteness of a word instance. in this respect, our work resembles the abstractness classifier in @xcite -although this work uses word senses instead of instances as we do. because contextualised concreteness data is unavailable, we instead choose features which, when given to a multi-layer perceptron (mlp), should provide enough information about a word for the mlp to be able to differentiate between each cell in figure 1 . we first provide the model with contextualised word embeddings, which we expect will provide some information about the target domain of the metaphor. in the contextualised representations, we expect there to exist a space of concrete meanings and some space of abstract meanings-which would help the network differentiate between the top and bottom rows of figure 1 . along with this, we provide static word embeddings, to provide information about the source domain. since these static type-level embeddings will clearly contain information about both source and target, we compliment them with type-level concreteness ratings. such ratings should reflect the concreteness of the most concrete sense of the word, thus allowing the network to differentiate between the left and right columns of figure 1 . figure 2 shows an overview of our architecture. in the following paragraphs, we detail each individual component of the model. contextual word embedding for contextualised embeddings, we fine-tune bert @xcite . bert is a sentence encoder which utilises a transformer architecture @xcite , and is trained with two separate tasksmasked language modelling (a cloze task), and next-sentence prediction. the latent space (the final hidden state of the encoder) contains vector representations of each input token, which change in different contexts. several pre-trained bert models are available-we use bert large. 2 concreteness model we define a simple model which represents the concreteness of a word as a linear interpolation between two vectors, representing maximal concreteness and abstractness, v con and v abs respectively. for each word w we obtain a real number estimate of its concreteness, c, from concreteness. the output vector of the model is defined as out-of-vocabulary words have their own vector, v unk . each of v abs , v con , and v unk are randomly initialised and learned from data. the dimensionality of these vectors is a hyperparameter which is tuned-a higher dimensionalality will likely place more emphasis on this feature when it is fed into the mlp as part of the ensemble model. ## conclusion we participated in the 2020 metaphor identification shared task @xcite . our model was designed to try and exploit knowledge of lexical concreteness and contextual meaning to identify metaphors. our results improved over the previous best performing system by an average of 8.0% f 1 , but trailed behind the leader of the task by 5.0%. in future work, we are keen to explore first training a model to identify concreteness in context, then fine-tuning this to metaphor identification, based on the reasoning presented in §2.