Automatically Inferring Implicit Properties in Similes

Qadir, Ashequl; Riloff, Ellen; Walker, Marilyn A.

doi:10.18653/v1/n16-1146

Cited by 19 publications

(24 citation statements)

References 10 publications

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“…While most computational work has focused on simile detection (Niculae and Danescu-Niculescu-Mizil, 2014;Mpouli, 2017;Qadir et al, 2015Qadir et al, , 2016Zeng et al, 2019;Liu et al, 2018), 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.…”

Section: The City Was Beautifulmentioning

confidence: 99%

“…For example the Closed Simile, "The boy was as strong as an ox" gives strong as the PROPERTY shared by the boy and ox. But most similes do not give an explicit PROP-ERTY such as the Open Simile (e.g., "The boy was like an ox") leaving the reader to infer that the boy is strong/large/fast (Qadir et al, 2016). Due to their implicit nature, generating open similes is often more challenging and hence we resort to only using like a 7 as a comparator instead of as...as.…”

Section: Automatic Parallel Corpus Creationmentioning

confidence: 99%

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

Chakrabarty¹,

Muresan²,

Peng³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language, such as a simile, goes beyond plain expressions to give readers new insights and inspirations. 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 fine-tune a pretrained sequence to sequence model, BART (Lewis et al., 2019), on the literal-simile pairs to 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% 1 of the times, and three baseline systems including a recent metaphor generation model 71% 2 of the times when compared pairwise. 3 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. * The research was conducted when the author was at USC/ISI. 1 We average 32.6% and 41.3% for 2 humans. 2 We average 82% ,63% and 68% for three baselines. 3 The simile in the title is generated by our best model. Input: Generating similes effortlessly, output: Generating similes like a Pro.

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Section: The City Was Beautifulmentioning

confidence: 99%

Section: Automatic Parallel Corpus Creationmentioning

confidence: 99%

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

Chakrabarty¹,

Muresan²,

Peng³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…Qadir et al [26] use lexical features, semantic features and sentiment features to infer the affective polarity of simile in twitters to build classifiers. Qadir et al [27] infer implicit properties by using syntactic structure, dictionary definitions, statistical co-occurrence and word embeddings.…”

Section: Metaphor Analysismentioning

confidence: 99%

A hybrid representation-based simile component extraction

Zhang

et al. 2020

Neural Comput & Applic

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Simile, a special type of metaphor, can help people to express their ideas more clearly. Simile component extraction is to extract tenors and vehicles from sentences. This task has a realistic significance since it is useful for building cognitive knowledge base. With the development of deep neural networks, researchers begin to apply neural models to component extraction. Simile components should be in cross-domain. According to our observations, words in cross-domain always have a different concept. Thus, concept is important when identifying whether two words are simile components or not. However, existing models do not integrate concept into their models. It is difficult for these models to identify the concept of a word. What's more, corpus about simile component extraction is limited. There are a number of rare words or unseen words and the representations of these words are always nor proper enough. Exiting models can hardly extract simile components accurately when there are low frequency words in sentences. To solve

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“…A simile is a figure of speech that directly compares two things using connecting words such as ''like'' or ''as'' [10]. Recently, there are more and more researches about similes, which include sentiment analysis [12], [13], implicit properties inference [14] and components recognition in simile sentences [10]. In this paper, we focus on the simile recognition task.…”

Section: Related Work a Simile Analysismentioning

confidence: 99%

Combining Part-of-Speech Tags and Self-Attention Mechanism for Simile Recognition

et al. 2019

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Simile recognition is to find simile sentences and extract the tenor and vehicle from these sentences. Previous works illustrate that tenors and vehicles are typically noun phrases. A word may have different part-of-speech (POS) labels (e.g., adjectives, adverbs, nouns, and verbs) in different sentences. It is important for the simile recognition task to identify a certain POS information for each word in a sentence. However, existing models use the same word embedding to represent a word, which cannot accurately represent the POS information of this word in different sentences. In this paper, we propose a neural network framework explicitly integrating the POS information into simile recognition task, with additional self-attention mechanism to better capture long term dependencies between any two tokens in sentences. The experimental results show that our proposed models significantly outperform previous state-of-the-art methods in the simile recognition task. We also present an analysis showing that the POS information and self-attention mechanism are effective for the simile recognition task. INDEX TERMS Simile recognition, part-of-speech, self-attention mechanism.

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Automatically Inferring Implicit Properties in Similes

Cited by 19 publications

References 10 publications

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

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

A hybrid representation-based simile component extraction

Combining Part-of-Speech Tags and Self-Attention Mechanism for Simile Recognition

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