Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation (2020)

Abstract: Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language such as a simile go beyond plain expressions to give readers new insights and inspirations. In this paper, we tackle the problem of simile generation. Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their literal counterpart using structured common sense knowledge. We then propose to fine-tune a pre-trained sequence to sequence model, BART (Lewis et al 2019), on the literal-simile pairs to gain generalizability, so that we can generate novel similes given a literal sentence. Experiments show that our approach generates 88% novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37% of the time when compared pairwise. We also show how replacing literal sentences with similes from our best model in machine-generated stories improves evocativeness and leads to better acceptance by human judges.

## introduction comparisons are inherent linguistic devices that express the likeness of two entities, concepts or ideas. when used in a figurative sense, these comparisons are called similes. they are a figure of speech that the city was beautiful the city was like a painting compare two different kind of things, usually with the intent to make the description more emphatic or vivid, being often used in literature and poetry to spark the reader's imagination @xcite . take the following two examples: "the city was like a painting", and "if it falls into the wrong hands it would be as catastrophic as a nuclear bomb." in the first example, the comparison draws on the implicit "beauty" property being shared by the two very different entities, city and painting, while in the second the "catastrophic" property is shared by falling into the wrong hands and nuclear bomb. while most computational work has focused on simile detection @xcite @xcite @xcite , research on simile generation is under-explored. generating similes could impact many downstream applications such as creative writing assistance, and literary or poetic content creation. to tackle the generation problem, we take advantage of the relatively simple structure of similes that consists of five elements @xcite : the topic (usually a noun phrase that acts as the logical subject), the vehicle (the logical object of the comparison, usually a noun phrase), the property (what the two things being compared have in common, usually an adjective), the event (eventuality or state, usually a verb), and the comparator (the trigger word or phrase that marks the presence of a comparison, usually the preposition "like" or "as...as"). all elements of a simile are explicit, with the exception of property, which can be both implicit and explicit. if we take the first example above, its structure is: "[the city/topic] [was/event] [like/comparator] [a painting/vehicle]" (property is implicit). unlike metaphors, the semantic context of similes tends to be very shallow, transferring a single property @xcite . moreover, the explicit syntactic structure of similes allows, in exchange, for more lexical creativity @xcite . we focus on the task of generating a simile starting from a literal utterance that contains the topic, event and property. we frame this task as a style-transfer problem @xcite @xcite , where the author's intent is to make the description of the topic more emphatic by introducing a comparison with the vehicle via a shared property (see figure 1 for examples of literal descriptive sentences and the generated similes). we call our approach scope (style transfer through commonsense property). there are two main challenges we need to address: 1) the lack of training data that consists of pairs of literal utterances and their equivalent simile in order to train a supervised model; 2) ensuring that the generated simile makes a meaningful comparison between the topic and the vehicle via the shared property explicitly or implicitly expressed (e.g., figure 1 gensimile1 and gensim-ile2, respectively). to the best of our knowledge, this is the first work in attempting to generate similes. by framing the task as a style-transfer problem we make three contributions: 4 automatic creation of a parallel corpus of [literal sentence, simile] pairs. our constructed corpus contains 87,843 such pairs. as a first step, we use distant supervision to automatically collect a set of self-labeled similes using the phrase like a. we then convert these similes to their literal versions by removing the comparator and replacing the vehicle with the associated property by leveraging the structured common sense knowledge achieved from comet @xcite , a language model fine-tuned on conceptnet @xcite . for example, for the simile "love is like a unicorn" our method will generate "love is rare" (section 2.1). transfer learning from a pre-trained model for generating high quality similes. our system scope, fine-tunes bart @xcite a state of the art pre-trained denoising autoencoder built with a sequence to sequence model, on our automatically collected parallel corpus of [literal sentence, simile] pairs (section 2.2) to generate similes. human evaluations show that this approach generates similes that are better 37% of the time on average compared to 2 literary experts, 82% and 63% of times compared to two well-crafted baselines, and 68% of the times compared to a state of the art system for metaphor generation @xcite (section 4). a task-based evaluation. we show the effectiveness of the generated similes as a tool for enhancing creativity and evocativeness in machine generated stories. evaluation via amazon mechanical turk shows that stories containing similes generated by scope is preferred by turkers 42% of the times compared to stories without similes, which is preferred 25% of the times (section 6). ## scope: style transfer through commonsense property our style transfer approach for simile generation from literal descriptive sentences has two steps: 1) first convert self-labeled similes into literal sentences using structured common sense knowledge (section 2.1); and 2) given the [literal sentence, simile] pairs, fine-tune a seq2seq model on these pairs to generate a simile given a literal sentence (section 2.2). this two-step approach is shown in the upper half of figure 2 . ## experimental setup to compare the quality of the generated similes, we benchmark our scope model against human performance (i.e., the two creative writing experts human1 & human2 described in section 2.1) and three baseline systems described below ## qualitative analysis ## related work simile generation is a relatively new task. most prior work has focused on detection of similes. the closest task in nlp to simile generation is generating metaphors. however, it should be noted that the overlap between the expressive range of similes and metaphors is known to be only partial: there are similes that cannot be rephrased as metaphors, similarly the other way around @xcite . ## conclusion we establish a new task for nlg: simile generation from literal sentences. we propose a novel way of creating parallel corpora and a transfer-learning approach for generating similes. human and automatic evaluations show that our best model is successful at generating similes. our experimental results further show that to truly be able to generate similes based on actual metaphoric or conceptual mappings, it is important to incorporate some common sense knowledge about the topics and their properties. future directions include exploration of other knowledge bases to help the inference process and applying our simile generation approach to different creative nlg tasks such as pun @xcite , sarcasm @xcite , and hyperbole @xcite .