A Computational System of Metaphor Generation with Evaluation Mechanism

Terai, Asuka; Nakagawa, Masanori

doi:10.1007/978-3-642-15822-3_18

Cited by 19 publications

(11 citation statements)

References 3 publications

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“…Some early works made contributions to use template and heuristic-based methods (Abe et al, 2006;Terai and Nakagawa, 2010) to generate "A is like B" sentences, more popularly referred to as similes. concentrated on simile generation, applying seq2seq model to paraphrase a literal sentence into a simile.…”

Section: Metaphor Generationmentioning

confidence: 99%

MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding

Chakrabarty¹,

Zhang²,

Muresan³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Generating metaphors is a challenging task as it requires a proper understanding of abstract concepts, making connections between unrelated concepts, and deviating from the literal meaning. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Based on a theoretically-grounded connection between metaphors and symbols, we propose a method to automatically construct a parallel corpus by transforming a large number of metaphorical sentences from the Gutenberg Poetry corpus (Jacobs, 2018) to their literal counterpart using recent advances in masked language modeling coupled with commonsense inference. For the generation task, we incorporate a metaphor discriminator to guide the decoding of a sequence to sequence model finetuned on our parallel data to generate high quality metaphors. Human evaluation on an independent test set of literal statements shows that our best model generates metaphors better than three well-crafted baselines 66% of the time on average. Moreover, a task-based evaluation shows that human-written poems enhanced with metaphors proposed by our model are preferred 68% of the time compared to poems without metaphors.

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Section: Metaphor Generationmentioning

confidence: 99%

MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding

Chakrabarty¹,

Zhang²,

Muresan³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…However, their dataset has a small number (171) of metaphoric sentences extracted from WordNet. Early works on metaphor generation mainly focus on phrase level metaphor and template-based generation (Terai and Nakagawa, 2010;Ovchinnikova et al, 2014). Recent works also explore the power of neural networks (Mao et al, 2018;Yu and Wan, 2019;Stowe et al, 2020).…”

Section: Metaphoric Expression Generationmentioning

confidence: 99%

PIE: A Parallel Idiomatic Expression Corpus for Idiomatic Sentence Generation and Paraphrasing

Zhou¹,

Gong²,

Bhat³

2021

Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)

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Idiomatic expressions (IE) play an important role in natural language, and have long been a "pain in the neck" for NLP systems. Despite this, text generation tasks related to IEs remain largely under-explored. In this paper, we propose two new tasks of idiomatic sentence generation and paraphrasing to fill this research gap. We introduce a curated dataset of 823 IEs, and a parallel corpus with sentences containing them and the same sentences where the IEs were replaced by their literal paraphrases as the primary resource for our tasks. We benchmark existing deep learning models, which have state-of-the-art performance on related tasks using automated and manual evaluation with our dataset to inspire further research on our proposed tasks. By establishing baseline models, we pave the way for more comprehensive and accurate modeling of IEs, both for generation and paraphrasing. 1

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“…Metaphor Generation. Early work in metaphor generation was based in heuristics, learning to generate relatively simple "A is like B" representations (Abe et al, 2006;Terai and Nakagawa, 2010). In a similar vein, Veale (2016) uses template-like structures to generate creative and metaphoric tweets.…”

Section: Related Workmentioning

confidence: 99%

Metaphor Generation with Conceptual Mappings

Stowe¹,

Chakrabarty²,

Peng³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Generating metaphors is a difficult task as it requires understanding nuanced relationships between abstract concepts. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Guided by conceptual metaphor theory, we propose to control the generation process by encoding conceptual mappings between cognitive domains to generate meaningful metaphoric expressions. To achieve this, we develop two methods: 1) using FrameNetbased embeddings to learn mappings between domains and applying them at the lexical level (CM-Lex), and 2) deriving source/target pairs to train a controlled seq-to-seq generation model (CM-BART). We assess our methods through automatic and human evaluation for basic metaphoricity and conceptual metaphor presence. We show that the unsupervised CM-Lex model is competitive with recent deep learning metaphor generation systems, and CM-BART outperforms all other models both in automatic and human evaluations. 1

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A Computational System of Metaphor Generation with Evaluation Mechanism

Cited by 19 publications

References 3 publications

MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding

MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding

PIE: A Parallel Idiomatic Expression Corpus for Idiomatic Sentence Generation and Paraphrasing

Metaphor Generation with Conceptual Mappings

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