Fantastic Expressions and Where to Find Them: C hinese Simile Generation with Multiple Constraints (2023)

Abstract: Similes occur in the creative context of describing a concept (i.e., tenor) by making a literally false yet figuratively meaningful comparison to another (i.e., vehicle). Previous efforts form simile generation as a context-free generation task, focusing on simile-style transfer or writing a simile from a given prefix. However, generated texts under such settings might be undesirable, such as hardly meeting the simile definition (e.g., missing vehicle) or difficult to address certain preferences of content as humans wish (e.g., describe the color of apples through the simile). We believe that a simile could be more qualified and user-oriented if incorporated with pre-specified constraints. To this end, we introduce controllable simile generation (CSG), a new task that requires the model to generate a simile with multiple simile elements, e.g., context and vehicle. To facilitate this task, we present GraCe, including 61.3k simile-element annotated Chinese similes. Based on it, we propose a CSG model Similor to benchmark this task, including a vehicle retrieval module Scorer to obtain the explicable comparison for a given tenor in the vehicle-unknown situation. Both statistical and experimental analyses show that GraCe is of high quality beyond all other Chinese simile datasets, in terms of the number (8 vs. 3) of annotation elements, Is-Simile accuracy (98.9% vs. 78.7%), and increasing model-performance gains for both uncontrollable and controllable simile generation. Meanwhile, Similor can serve as a strong baseline for CSG, especially with Scorer, which beats model-based retrieval methods without any re-training.

## introduction similes are widely-used and stimulate people's creativity @xcite . according to rhetoric's classical terms @xcite , a simile uses comparison words (i.e., comparator) to make a literally false comparison between a concept (i.e., tenor) and another (i.e., vehicle). it also ensures this comparison pair is figuratively meaningful by examining whether they have shared properties (i.e., ground) @xcite . notably, ground can be expressed in an explicit or implicit way @xcite . as shown in figure 1 qualified samples. "maple leaves are like torches of fired red." has the explicit ground that the tenor "maple leaves" and the vehicle "torches" have the similar color of "fired red", while "maple leaves are like small palms." implies the ground that they have a similar pentagram shape. although simile detection has been widely explored @xcite @xcite , simile generation is still in its fledgling stage. existing efforts focus on context-free simile generation, including: 1) style-transfer-based and 2) prefix-based simile generation. the former paraphrases a literal sentence into its simile version @xcite and the latter aims at writing a simile from a prespecified tenor @xcite . despite great progress, such experiment settings may result in undesirable results, such as unqualified similes or being unable to meet the content dataset # nums # avg. % is-simile topic comparator tenor vehicle ground context w / f w / f above / below poetry (2019b) 43,051 23 -/ / / lyrics (2019b) 246,669 23 -/ / / cs (2021) 5,490,721 61 29.3% / / / cmc (2022) 2,787 35 78.7% / / / grace 61,360 89 98.9% / / / to solve these problems, we explore incorporating various constraints into simile generation. specifically, we introduce a new task of controllable simile generation (csg) -generating a simile with multiple simile elements (e.g., vehicle, context, etc.) from a given prefix (i.e., topic). we collect a fine-grained annotated chinese simile dataset (grace), containing annotated 61.3k similes from 260k cleaned text of student compositions. as shown in linguistics @xcite . to benchmark csg, we build the model similor, which first retrieves vehicle (if it is unknown) by the module scorer (a shared cognitive-property-based retrieval method ) for the given tenor, then incorporates all constraints and the input prefix (i.e., topic) to generate the simile. both statistical and experimental analyses show that grace is of high quality beyond previous chinese simile datasets. meanwhile, similor can successfully incorporate the constraints in the outputs. especially in vehicle-unknown setup, scorer beats the model-based retrieval method both in automatic and human evaluations without any re-training. 2 ## related work different from metaphor @xcite @xcite that using implicit comparators, similes are much easier to be located. however, existing efforts mainly focus on simile detection @xcite @xcite , leaving simile generation under-explored. previous work on context-free simile generation can be divided into: 1) styletransfer-based and 2) prefix-based simile generation. the first forms this task as paraphrasing a literal sentence into a simile-style sentence, and automatically edits self-labeled similes to their literal version for building pairs of (literal sentence, simile). for example, scope @xcite uses commonsense properties words @xcite of the vehicle to replace it in a simile, then removes the comparator to form the final literal sentence. wps @xcite + context + comparator + topic + tenor + vehicle step 1: rule-based filtering method (filter samples without comparator) step 2: model-based filtering method (classify non-simile samples containing comparator) !""#@xmath0,-.%/012345 (it's spring. the flowers are all in bloom. it's really beautiful!) ⅹ ✓ 6!7@xmath15 (it's spring. flowers begin to boom, like a fire, fiery red. it looks great!) 6!7@xmath25 (it's spring. flowers begin to boom, like a fire, fiery red. it looks great!) step 3: labeling tensor and vehicle (utilize a roberta-based sequence labeling model ) + ground + cognitive properties step 4: search cognitive properties and labeling ground (use the cogbank dataset) 8(flowers) @ab (a fire) fg(burning), … , c(red), h (hot) cogbank query ;<(booming), … , c(red), i (beautiful) 6!7@xmath35 (it's spring. flowers begin to boom, like a fire, fiery red. it looks great!) matching 6!7@xmath45 (it's spring. flowers begin to boom, like a fire, fiery red. it looks great!) @xmath5. he still likes playing basketball as before, and looks in excellent health.) ✓ ✓ ⅹ @xmath6. he still likes playing basketball as before, and looks in excellent health.) deletes a span from a simile to obtain the literal sentence. the second focuses on generating the comparator and tenor from a pre-specified tenor. liu et al. (2019b) uses a continuous latent variable as a rhetoric controller to generate chinese poetry. cmc (li et al., 2022) provides a multi-task framework that leverages unlabeled data to enhance performance. chen et al. (2022) use three words triple (tenor, attribute, vehicle) and a relationship pattern to hint the model for generating simile. different from all of them, we focus on controllable simile generation -generating a simile with multiple constraints. to make it a computationally feasible task, we build a high-quality dataset grace and a csg model similor with scorer to ensure explicable tenor-vehicle pairs in generated similes. as shown in chinese sentences, we get about 5.48 million sentences. at most two sentences above and below each sample are used as the context element. ## experiments in this section, we first experimentally evaluate the quality of the grace dataset by applying it to prefixbased simile generation ( § 5.1). since the setup of this uncontrollable generation task does not need additional annotations on the training samples, we can compare grace with previous chinese simile datasets. based on it, we then evaluate the proposed similor on the new csg task ( § 5.2). specifically, we first compare different model varieties of similor constrained by comparator and vehicle, and then evaluate the performances of similor under more extensive constraints. finally, we explore whether scorer helps similor to generate similes in the vehicle-unknown setup. ## conclusion in this paper, we introduce a new task setup for simile generation: controllable simile generation (csg). to facilitate it, we build grace, a finegrained annotated chinese simile dataset, and benchmark this task with the proposed csg model similor, which includes a vehicle-retrieval module scorer. our work takes the first attempt to expand the elements of simile from the aspect of cognitive linguistics @xcite (i.e, ground and context), and tentatively gives a successful implementation of probing simile interpretation from the cognitive property. we hope this idea can provide novel insights to future works of the creative generation, such as puns, hyperbole, and poetry, etc.