Proceedings of the Workshop on Figurative Language Processing

Beata Beigman Klebanov, Ekaterina Shutova, Patricia Lichtenstein, Smaranda Muresan, Chee Wee (Editors)

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New Orleans, Louisiana
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Proceedings of the Workshop on Figurative Language Processing
Beata Beigman Klebanov | Ekaterina Shutova | Patricia Lichtenstein | Smaranda Muresan | Chee Wee

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Linguistic Features of Sarcasm and Metaphor Production Quality
Stephen Skalicky | Scott Crossley

Using linguistic features to detect figurative language has provided a deeper in-sight into figurative language. The purpose of this study is to assess whether linguistic features can help explain differences in quality of figurative language. In this study a large corpus of metaphors and sarcastic responses are collected from human subjects and rated for figurative language quality based on theoretical components of metaphor, sarcasm, and creativity. Using natural language processing tools, specific linguistic features related to lexical sophistication and semantic cohesion were used to predict the human ratings of figurative language quality. Results demonstrate linguistic features were able to predict small amounts of variance in metaphor and sarcasm production quality.

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Catching Idiomatic Expressions in EFL EssaysEFL Essays
Michael Flor | Beata Beigman Klebanov

This paper presents an exploratory study on large-scale detection of idiomatic expressions in essays written by non-native speakers of English. We describe a computational search procedure for automatic detection of idiom-candidate phrases in essay texts. The study used a corpus of essays written during a standardized examination of English language proficiency. Automatically-flagged candidate expressions were manually annotated for idiomaticity. The study found that idioms are widely used in EFL essays. The study also showed that a search algorithm that accommodates the syntactic and lexical exibility of idioms can increase the recall of idiom instances by 30 %, but it also increases the amount of false positives.

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Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks
Yuri Bizzoni | Shalom Lappin

We propose a new annotated corpus for metaphor interpretation by paraphrase, and a novel DNN model for performing this task. Our corpus consists of 200 sets of 5 sentences, with each set containing one reference metaphorical sentence, and four ranked candidate paraphrases. Our model is trained for a binary classification of paraphrase candidates, and then used to predict graded paraphrase acceptability. It reaches an encouraging 75 % accuracy on the binary classification task, and high Pearson (.75) and Spearman (.68) correlations on the gradient judgment prediction task.

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A Report on the 2018 VUA Metaphor Detection Shared TaskVUA Metaphor Detection Shared Task
Chee Wee (Ben) Leong | Beata Beigman Klebanov | Ekaterina Shutova

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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An LSTM-CRF Based Approach to Token-Level Metaphor DetectionLSTM-CRF Based Approach to Token-Level Metaphor Detection
Malay Pramanick | Ashim Gupta | Pabitra Mitra

Automatic processing of figurative languages is gaining popularity in NLP community for their ubiquitous nature and increasing volume. In this era of web 2.0, automatic analysis of sarcasm and metaphors is important for their extensive usage. Metaphors are a part of figurative language that compares different concepts, often on a cognitive level. Many approaches have been proposed for automatic detection of metaphors, even using sequential models or neural networks. In this paper, we propose a method for detection of metaphors at the token level using a hybrid model of Bidirectional-LSTM and CRF. We used fewer features, as compared to the previous state-of-the-art sequential model. On experimentation with VUAMC, our method obtained an F-score of 0.674.

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Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor DetectionBiLSTMs Two Neural Networks for Sequential Metaphor Detection
Yuri Bizzoni | Mehdi Ghanimifard

We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text : a bi-LSTM model and a new structure based on recursive feed-forward concatenation of the input. We discuss different versions of such models and the effect that input manipulation-specifically, reducing the length of sentences and introducing concreteness scores for words-have on their performance.

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Neural Metaphor Detecting with CNN-LSTM ModelCNN-LSTM Model
Chuhan Wu | Fangzhao Wu | Yubo Chen | Sixing Wu | Zhigang Yuan | Yongfeng Huang

Metaphors are figurative languages widely used in daily life and literatures. It’s an important task to detect the metaphors evoked by texts. Thus, the metaphor shared task is aimed to extract metaphors from plain texts at word level. We propose to use a CNN-LSTM model for this task. Our model combines CNN and LSTM layers to utilize both local and long-range contextual information for identifying metaphorical information. In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task. We also incorporated some additional features such as part of speech (POS) tags and word cluster to improve the performance of model. Our best model achieved 65.06 % F-score in the all POS testing subtask and 67.15 % in the verbs testing subtask.

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Di-LSTM Contrast : A Deep Neural Network for Metaphor DetectionLSTM Contrast : A Deep Neural Network for Metaphor Detection
Krishnkant Swarnkar | Anil Kumar Singh

The contrast between the contextual and general meaning of a word serves as an important clue for detecting its metaphoricity. In this paper, we present a deep neural architecture for metaphor detection which exploits this contrast. Additionally, we also use cost-sensitive learning by re-weighting examples, and baseline features like concreteness ratings, POS and WordNet-based features. The best performing system of ours achieves an overall F1 score of 0.570 on All POS category and 0.605 on the Verbs category at the Metaphor Shared Task 2018.

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Conditional Random Fields for Metaphor Detection
Anna Mosolova | Ivan Bondarenko | Vadim Fomin

We present an algorithm for detecting metaphor in sentences which was used in Shared Task on Metaphor Detection by First Workshop on Figurative Language Processing. The algorithm is based on different features and Conditional Random Fields.

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Detecting Figurative Word Occurrences Using Recurrent Neural Networks
Agnieszka Mykowiecka | Aleksander Wawer | Malgorzata Marciniak

The paper addresses detection of figurative usage of words in English text. The chosen method was to use neural nets fed by pretrained word embeddings. The obtained results show that simple solutions, based on words embeddings only, are comparable to complex solutions, using many sources of information which are not available for languages less-studied than English.

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Multi-Module Recurrent Neural Networks with Transfer Learning
Filip Skurniak | Maria Janicka | Aleksander Wawer

This paper describes multiple solutions designed and tested for the problem of word-level metaphor detection. The proposed systems are all based on variants of recurrent neural network architectures. Specifically, we explore multiple sources of information : pre-trained word embeddings (Glove), a dictionary of language concreteness and a transfer learning scenario based on the states of an encoder network from neural network machine translation system. One of the architectures is based on combining all three systems : (1) Neural CRF (Conditional Random Fields), trained directly on the metaphor data set ; (2) Neural Machine Translation encoder of a transfer learning scenario ; (3) a neural network used to predict final labels, trained directly on the metaphor data set. Our results vary between test sets : Neural CRF standalone is the best one on submission data, while combined system scores the highest on a test subset randomly selected from training data.

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Using Language Learner Data for Metaphor Detection
Egon Stemle | Alexander Onysko

This article describes the system that participated in the shared task on metaphor detection on the Vrije University Amsterdam Metaphor Corpus (VUA). The ST was part of the workshop on processing figurative language at the 16th annual conference of the North American Chapter of the Association for Computational Linguistics (NAACL2018). The system combines a small assertion of trending techniques, which implement matured methods from NLP and ML ; in particular, the system uses word embeddings from standard corpora and from corpora representing different proficiency levels of language learners in a LSTM BiRNN architecture. The system is available under the APLv2 open-source license.