Using Language Learner Data for Metaphor Detection

Stemle, Egon; Onysko, Alexander

doi:10.18653/v1/w18-0918

Cited by 17 publications

(15 citation statements)

References 16 publications

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“…At the recent VU Amsterdam (VUA) metaphor identification shared task (Leong et al, 2018), neural approaches dominated, with most teams using LSTMs trained on word embeddings and additional linguistic features, such as semantic classes and part of speech tags (Wu et al, 2018;Stemle and Onysko, 2018;Mykowiecka et al, 2018;Swarnkar and Singh, 2018). Most recently, Gao et al (2018) revisited this task, reporting state-of-the-art results with BiLSTMs and contextualized word embeddings (Peters et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Learning Outside the Box: Discourse-level Features Improve Metaphor Identification

Mu¹,

Yannakoudakis

Shutova³

2019

Proceedings of the 2019 Conference of the North

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Most current approaches to metaphor identification use restricted linguistic contexts, e.g. by considering only a verb's arguments or the sentence containing a phrase. Inspired by pragmatic accounts of metaphor, we argue that broader discourse features are crucial for better metaphor identification. We train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods, obtaining near state-of-the-art results on the 2018 VU Amsterdam metaphor identification task without the complex metaphorspecific features or deep neural architectures employed by other systems. A qualitative analysis further confirms the need for broader context in metaphor processing.

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Section: Related Workmentioning

confidence: 99%

Learning Outside the Box: Discourse-level Features Improve Metaphor Identification

Mu¹,

Yannakoudakis

Shutova³

2019

Proceedings of the 2019 Conference of the North

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“…bot.zen (Stemle and Onysko, 2018) used word embeddings from different standard corpora representing different levels of language mastery, encoding each word in a sentence into multiple vector-based embeddings which are then fed into an LSTM RNN network architecture. Specifically, the backpropagation step was performed using weightings computed based on the logarithmic function of the inverse of the count of the metaphors and non-metaphors.…”

Section: System Descriptionsmentioning

confidence: 99%

A Report on the 2018 VUA Metaphor Detection Shared Task

Leong¹,

Klebanov²,

Shutova³

2018

Proceedings of the Workshop on Figurative Language Processing

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As the community working on computational approaches to figurative language is growing and as methods and data become increasingly diverse, it is important to create widely shared empirical knowledge of the level of system performance in a range of contexts, thus facilitating progress in this area. One way of creating such shared knowledge is through benchmarking multiple systems on a common dataset. We report on the shared task on metaphor identification on the VU Amsterdam Metaphor Corpus conducted at the NAACL 2018 Workshop on Figurative Language Processing.

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“…As part of the NAACL 2018 Metaphor Shared Task (Leong et al, 2018), many researchers proposed neural models that mainly employ LSTMs (Hochreiter and Schmidhuber, 1997) with pre-trained word embeddings to identify metaphors on the word-level. The best performing systems are: THU NGN (Wu et al, 2018), OCOTA (Bizzoni and Ghanimifard, 2018) and bot.zen (Stemle and Onysko, 2018). Gao et al (2018) were the first to employ the deep contextualised word representation ELMo (Peters et al, 2018), combined with pre-trained GloVe (Pennington et al, 2014) embeddings to train bidirectional LSTM-based models.…”

Section: Related Workmentioning

confidence: 99%

Contextual Modulation for Relation-Level Metaphor Identification

Zayed¹,

McCrae²,

Buitelaar³

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Identifying metaphors in text is very challenging and requires comprehending the underlying comparison. The automation of this cognitive process has gained wide attention lately. However, the majority of existing approaches concentrate on word-level identification by treating the task as either single-word classification or sequential labelling without explicitly modelling the interaction between the metaphor components. On the other hand, while existing relation-level approaches implicitly model this interaction, they ignore the context where the metaphor occurs. In this work, we address these limitations by introducing a novel architecture for identifying relation-level metaphoric expressions of certain grammatical relations based on contextual modulation. In a methodology inspired by works in visual reasoning, our approach is based on conditioning the neural network computation on the deep contextualised features of the candidate expressions using feature-wise linear modulation. We demonstrate that the proposed architecture achieves state-of-the-art results on benchmark datasets. The proposed methodology is generic and could be applied to other textual classification problems that benefit from contextual interaction.

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Using Language Learner Data for Metaphor Detection

Cited by 17 publications

References 16 publications

Learning Outside the Box: Discourse-level Features Improve Metaphor Identification

Learning Outside the Box: Discourse-level Features Improve Metaphor Identification

A Report on the 2018 VUA Metaphor Detection Shared Task

Contextual Modulation for Relation-Level Metaphor Identification

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