pwc-archive/papers-with-abstracts huggingface dataset:
Sky + Fire = Sunset. Exploring Parallels between Visually Grounded Metaphors and Image Classifiers
Authors: Yuri Bizzoni, Simon Dobnik
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

This work explores the differences and similarities between neural image classifiers{'} mis-categorisations and visually grounded metaphors - that we could conceive as intentional mis-categorisations. We discuss the possibility of using automatic image classifiers to approximate human metaphoric behaviours, and the limitations of such frame. We report two pilot experiments to study grounded metaphoricity. In the first we represent metaphors as a form of visual mis-categorisation. In the second we model metaphors as a more flexible, compositional operation in a continuous visual space generated from automatic classification systems.
Recognizing Euphemisms and Dysphemisms Using Sentiment Analysis
Authors: Christian Felt, Ellen Riloff
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sentiment Analysis

This paper presents the first research aimed at recognizing euphemistic and dysphemistic phrases with natural language processing. Euphemisms soften references to topics that are sensitive, disagreeable, or taboo. Conversely, dysphemisms refer to sensitive topics in a harsh or rude way. For example, {``}passed away{''} and {``}departed{''} are euphemisms for death, while {``}croaked{''} and {``}six feet under{''} are dysphemisms for death. Our work explores the use of sentiment analysis to recognize euphemistic and dysphemistic language. First, we identify near-synonym phrases for three topics (firing, lying, and stealing) using a bootstrapping algorithm for semantic lexicon induction. Next, we classify phrases as euphemistic, dysphemistic, or neutral using lexical sentiment cues and contextual sentiment analysis. We introduce a new gold standard data set and present our experimental results for this task.
IlliniMet: Illinois System for Metaphor Detection with Contextual and Linguistic Information
Authors: Hongyu Gong, Kshitij Gupta, Akriti Jain, Suma Bhat
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

Metaphors are rhetorical use of words based on the conceptual mapping as opposed to their literal use. Metaphor detection, an important task in language understanding, aims to identify metaphors in word level from given sentences. We present IlliniMet, a system to automatically detect metaphorical words. Our model combines the strengths of the contextualized representation by the widely used RoBERTa model and the rich linguistic information from external resources such as WordNet. The proposed approach is shown to outperform strong baselines on a benchmark dataset. Our best model achieves F1 scores of 73.0{\%} on VUA ALLPOS, 77.1{\%} on VUA VERB, 70.3{\%} on TOEFL ALLPOS and 71.9{\%} on TOEFL VERB.
Adaptation of Word-Level Benchmark Datasets for Relation-Level Metaphor Identification
Authors: Omnia Zayed, John Philip McCrae, Paul Buitelaar
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Relation

Metaphor processing and understanding has attracted the attention of many researchers recently with an increasing number of computational approaches. A common factor among these approaches is utilising existing benchmark datasets for evaluation and comparisons. The availability, quality and size of the annotated data are among the main difficulties facing the growing research area of metaphor processing. The majority of current approaches pertaining to metaphor processing concentrate on word-level processing due to data availability. On the other hand, approaches that process metaphors on the relation-level ignore the context where the metaphoric expression. This is due to the nature and format of the available data. Word-level annotation is poorly grounded theoretically and is harder to use in downstream tasks such as metaphor interpretation. The conversion from word-level to relation-level annotation is non-trivial. In this work, we attempt to fill this research gap by adapting three benchmark datasets, namely the VU Amsterdam metaphor corpus, the TroFi dataset and the TSV dataset, to suit relation-level metaphor identification. We publish the adapted datasets to facilitate future research in relation-level metaphor processing.
Generating Ethnographic Models from Communities' Online Data
Authors: Tomek Strzalkowski, Anna Newheiser, Nathan Kemper, Ning Sa, Bharvee Acharya, Gregorios Katsios
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

In this paper we describe computational ethnography study to demonstrate how machine learning techniques can be utilized to exploit bias resident in language data produced by communities with online presence. Specifically, we leverage the use of figurative language (i.e., the choice of metaphors) in online text (e.g., news media, blogs) produced by distinct communities to obtain models of community worldviews that can be shown to be distinctly biased and thus different from other communities{'} models. We automatically construct metaphor-based community models for two distinct scenarios: gun rights and marriage equality. We then conduct a series of experiments to validate the hypothesis that the metaphors found in each community{'}s online language convey the bias in the community{'}s worldview.
Oxymorons: a preliminary corpus investigation
Authors: Marta La Pietra, Francesca Masini
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

This paper contains a preliminary corpus study of oxymorons, a figure of speech so far under-investigated in NLP-oriented research. The study resulted in a list of 376 oxymorons, identified by extracting a set of antonymous pairs (under various configurations) from corpora of written Italian and by manually checking the results. A complementary method is also envisaged for discovering contextual oxymorons, which are highly relevant for the detection of humor, irony and sarcasm.
Can Humor Prediction Datasets be used for Humor Generation? Humorous Headline Generation via Style Transfer
Authors: Orion Weller, Nancy Fulda, Kevin Seppi
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Headline Generation, Style Transfer

Understanding and identifying humor has been increasingly popular, as seen by the number of datasets created to study humor. However, one area of humor research, humor generation, has remained a difficult task, with machine generated jokes failing to match human-created humor. As many humor prediction datasets claim to aid in generative tasks, we examine whether these claims are true. We focus our experiments on the most popular dataset, included in the 2020 SemEval{'}s Task 7, and teach our model to take normal text and {``}translate{''} it into humorous text. We evaluate our model compared to humorous human generated headlines, finding that our model is preferred equally in A/B testing with the human edited versions, a strong success for humor generation, and is preferred over an intelligent random baseline 72{\%} of the time. We also show that our model is assumed to be human written comparable with that of the human edited headlines and is significantly better than random, indicating that this dataset does indeed provide potential for future humor generation systems.
Evaluating a Bi-LSTM Model for Metaphor Detection in TOEFL Essays
Authors: Kevin Kuo, Marine Carpuat
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

This paper describes systems submitted to the Metaphor Shared Task at the Second Workshop on Figurative Language Processing. In this submission, we replicate the evaluation of the Bi-LSTM model introduced by Gao et al.(2018) on the VUA corpus in a new setting: TOEFL essays written by non-native English speakers. Our results show that Bi-LSTM models outperform feature-rich linear models on this challenging task, which is consistent with prior findings on the VUA dataset. However, the Bi-LSTM models lag behind the best performing systems in the shared task.
Neural Metaphor Detection with a Residual biLSTM-CRF Model
Authors: Andr{\'e}s Torres Rivera, Antoni Oliver, Salvador Climent, Marta Coll-Florit
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Word Embeddings

In this paper we present a novel resource-inexpensive architecture for metaphor detection based on a residual bidirectional long short-term memory and conditional random fields. Current approaches on this task rely on deep neural networks to identify metaphorical words, using additional linguistic features or word embeddings. We evaluate our proposed approach using different model configurations that combine embeddings, part of speech tags, and semantically disambiguated synonym sets. This evaluation process was performed using the training and testing partitions of the VU Amsterdam Metaphor Corpus. We use this method of evaluation as reference to compare the results with other current neural approaches for this task that implement similar neural architectures and features, and that were evaluated using this corpus. Results show that our system achieves competitive results with a simpler architecture compared to previous approaches.
Augmenting Neural Metaphor Detection with Concreteness
Authors: Ghadi Alnafesah, Harish Tayyar Madabushi, Mark Lee
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sentence

The idea that a shift in concreteness within a sentence indicates the presence of a metaphor has been around for a while. However, recent methods of detecting metaphor that have relied on deep neural models have ignored concreteness and related psycholinguistic information. We hypothesis that this information is not available to these models and that their addition will boost the performance of these models in detecting metaphor. We test this hypothesis on the Metaphor Detection Shared Task 2020 and find that the addition of concreteness information does in fact boost deep neural models. We also run tests on data from a previous shared task and show similar results.
Supervised Disambiguation of German Verbal Idioms with a BiLSTM Architecture
Authors: Rafael Ehren, Timm Lichte, Laura Kallmeyer, Jakub Waszczuk
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

Supervised disambiguation of verbal idioms (VID) poses special demands on the quality and quantity of the annotated data used for learning and evaluation. In this paper, we present a new VID corpus for German and perform a series of VID disambiguation experiments on it. Our best classifier, based on a neural architecture, yields an error reduction across VIDs of 57{\%} in terms of accuracy compared to a simple majority baseline.
Metaphor Detection using Context and Concreteness
Authors: Rowan Hall Maudslay, Tiago Pimentel, Ryan Cotterell, Simone Teufel
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

We report the results of our system on the Metaphor Detection Shared Task at the Second Workshop on Figurative Language Processing 2020. Our model is an ensemble, utilising contextualised and static distributional semantic representations, along with word-type concreteness ratings. Using these features, it predicts word metaphoricity with a deep multi-layer perceptron. We are able to best the state-of-the-art from the 2018 Shared Task by an average of 8.0{\%} F1, and finish fourth in both sub-tasks in which we participate.
Being neighbourly: Neural metaphor identification in discourse
Authors: Verna Dankers, Karan Malhotra, Gaurav Kudva, Volodymyr Medentsiy, Ekaterina Shutova
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: POS, Sentence

Existing approaches to metaphor processing typically rely on local features, such as immediate lexico-syntactic contexts or information within a given sentence. However, a large body of corpus-linguistic research suggests that situational information and broader discourse properties influence metaphor production and comprehension. In this paper, we present the first neural metaphor processing architecture that models a broader discourse through the use of attention mechanisms. Our models advance the state of the art on the all POS track of the 2018 VU Amsterdam metaphor identification task. The inclusion of discourse-level information yields further significant improvements.
Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task
Authors: Xianyang Chen, Chee Wee (Ben) Leong, Michael Flor, Beata Beigman Klebanov
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Multi-Task Learning

This paper describes the ETS entry to the 2020 Metaphor Detection shared task. Our contribution consists of a sequence of experiments using BERT, starting with a baseline, strengthening it by spell-correcting the TOEFL corpus, followed by a multi-task learning setting, where one of the tasks is the token-level metaphor classification as per the shared task, while the other is meant to provide additional training that we hypothesized to be relevant to the main task. In one case, out-of-domain data manually annotated for metaphor is used for the auxiliary task; in the other case, in-domain data automatically annotated for idioms is used for the auxiliary task. Both multi-task experiments yield promising results.
Metaphor Detection using Ensembles of Bidirectional Recurrent Neural Networks
Authors: Jennifer Brooks, Abdou Youssef
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

In this paper we present our results from the Second Shared Task on Metaphor Detection, hosted by the Second Workshop on Figurative Language Processing. We use an ensemble of RNN models with bidirectional LSTMs and bidirectional attention mechanisms. Some of the models were trained on all parts of speech. Each of the other models was trained on one of four categories for parts of speech: {``}nouns{''}, {``}verbs{''}, {``}adverbs/adjectives{''}, or {``}other{''}. The models were combined into voting pools and the voting pools were combined using the logical {``}OR{''} operator.
Metaphor Detection Using Contextual Word Embeddings From Transformers
Authors: Jerry Liu, Nathan O{'}Hara, Alex Rubin, er, Rachel Draelos, Cynthia Rudin
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, Sentiment Analysis, Translation, Word Embeddings

The detection of metaphors can provide valuable information about a given text and is crucial to sentiment analysis and machine translation. In this paper, we outline the techniques for word-level metaphor detection used in our submission to the Second Shared Task on Metaphor Detection. We propose using both BERT and XLNet language models to create contextualized embeddings and a bi-directional LSTM to identify whether a given word is a metaphor. Our best model achieved F1-scores of 68.0{\%} on VUA AllPOS, 73.0{\%} on VUA Verbs, 66.9{\%} on TOEFL AllPOS, and 69.7{\%} on TOEFL Verbs, placing 7th, 6th, 5th, and 5th respectively. In addition, we outline another potential approach with a KNN-LSTM ensemble model that we did not have enough time to implement given the deadline for the competition. We show that a KNN classifier provides a similar F1-score on a validation set as the LSTM and yields different information on metaphors.
Testing the role of metadata in metaphor identification
Authors: Egon Stemle, Alex Onysko, er
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

This paper describes the adaptation and application of a neural network system for the automatic detection of metaphors. The LSTM BiRNN system participated in the shared task of metaphor identification that was part of the Second Workshop of Figurative Language Processing (FigLang2020) held at the Annual Conference of the Association for Computational Linguistics (ACL2020). The particular focus of our approach is on the potential influence that the metadata given in the ETS Corpus of Non-Native Written English might have on the automatic detection of metaphors in this dataset. The article first discusses the annotated ETS learner data, highlighting some of its peculiarities and inherent biases of metaphor use. A series of evaluations follow in order to test whether specific metadata influence the system performance in the task of automatic metaphor identification. The system is available under the APLv2 open-source license.
Sarcasm Detection Using an Ensemble Approach
Authors: Jens Lemmens, Ben Burtenshaw, Ehsan Lotfi, Ilia Markov, Walter Daelemans
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sarcasm Detection

We present an ensemble approach for the detection of sarcasm in Reddit and Twitter responses in the context of The Second Workshop on Figurative Language Processing held in conjunction with ACL 2020. The ensemble is trained on the predicted sarcasm probabilities of four component models and on additional features, such as the sentiment of the comment, its length, and source (Reddit or Twitter) in order to learn which of the component models is the most reliable for which input. The component models consist of an LSTM with hashtag and emoji representations; a CNN-LSTM with casing, stop word, punctuation, and sentiment representations; an MLP based on Infersent embeddings; and an SVM trained on stylometric and emotion-based features. All component models use the two conversational turns preceding the response as context, except for the SVM, which only uses features extracted from the response. The ensemble itself consists of an adaboost classifier with the decision tree algorithm as base estimator and yields F1-scores of 67{\%} and 74{\%} on the Reddit and Twitter test data, respectively.
A Transformer Approach to Contextual Sarcasm Detection in Twitter
Authors: Hunter Gregory, Steven Li, Pouya Mohammadi, Natalie Tarn, Rachel Draelos, Cynthia Rudin
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sarcasm Detection

Understanding tone in Twitter posts will be increasingly important as more and more communication moves online. One of the most difficult, yet important tones to detect is sarcasm. In the past, LSTM and transformer architecture models have been used to tackle this problem. We attempt to expand upon this research, implementing LSTM, GRU, and transformer models, and exploring new methods to classify sarcasm in Twitter posts. Among these, the most successful were transformer models, most notably BERT. While we attempted a few other models described in this paper, our most successful model was an ensemble of transformer models including BERT, RoBERTa, XLNet, RoBERTa-large, and ALBERT. This research was performed in conjunction with the sarcasm detection shared task section in the Second Workshop on Figurative Language Processing, co-located with ACL 2020.
Syntactic Parsing in Humans and Machines
Authors: Paola Merlo
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

To process the syntactic structures of a language in ways that are compatible with human expectations, we need computational representations of lexical and syntactic properties that form the basis of human knowledge of words and sentences. Recent neural-network-based and distributed semantics techniques have developed systems of considerable practical success and impressive performance. As has been advocated by many, however, such systems still lack human-like properties. In particular, linguistic, psycholinguistic and neuroscientific investigations have shown that human processing of sentences is sensitive to structure and unbounded relations. In the spirit of better understanding the structure building and long-distance properties of neural networks, I will present an overview of recent results on agreement and island effects in syntax in several languages. While certain sets of results in the literature indicate that neural language models exhibit long-distance agreement abilities, other finer-grained investigation of how these effects are calculated indicates that that the similarity spaces they define do not correlate with human experimental results on intervention similarity in long-distance dependencies. This opens the way to reflections on how to better match the syntactic properties of natural languages in the representations of neural models.
End-to-End Negation Resolution as Graph Parsing
Authors: Robin Kurtz, Stephan Oepen, Marco Kuhlmann
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Negation

We present a neural end-to-end architecture for negation resolution based on a formulation of the task as a graph parsing problem. Our approach allows for the straightforward inclusion of many types of graph-structured features without the need for representation-specific heuristics. In our experiments, we specifically gauge the usefulness of syntactic information for negation resolution. Despite the conceptual simplicity of our architecture, we achieve state-of-the-art results on the Conan Doyle benchmark dataset, including a new top result for our best model.
Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era
Authors: Agnieszka Falenska, Anders Bj{\"o}rkelund, Jonas Kuhn
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Diversity, Multi-Task Learning

Graph-based and transition-based dependency parsers used to have different strengths and weaknesses. Therefore, combining the outputs of parsers from both paradigms used to be the standard approach to improve or analyze their performance. However, with the recent adoption of deep contextualized word representations, the chief weakness of graph-based models, i.e., their limited scope of features, has been mitigated. Through two popular combination techniques {--} blending and stacking {--} we demonstrate that the remaining diversity in the parsing models is reduced below the level of models trained with different random seeds. Thus, an integration no longer leads to increased accuracy. When both parsers depend on BiLSTMs, the graph-based architecture has a consistent advantage. This advantage stems from globally-trained BiLSTM representations, which capture more distant look-ahead syntactic relations. Such representations can be exploited through multi-task learning, which improves the transition-based parser, especially on treebanks with a high ratio of right-headed dependencies.
Semi-supervised Parsing with a Variational Autoencoding Parser
Authors: Xiao Zhang, Dan Goldwasser
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Dependency Parsing

We propose an end-to-end variational autoencoding parsing (VAP) model for semi-supervised graph-based projective dependency parsing. It encodes the input using continuous latent variables in a sequential manner by deep neural networks (DNN) that can utilize the contextual information, and reconstruct the input using a generative model. The VAP model admits a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We conducted experiments on the WSJ data sets, showing the proposed model can use the unlabeled data to increase the performance on a limited amount of labeled data, on a par with a recently proposed semi-supervised parser with faster inference.