418 research outputs found
A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection
Hyperbole and metaphor are common in day-to-day communication (e.g., "I am in
deep trouble": how does trouble have depth?), which makes their detection
important, especially in a conversational AI setting. Existing approaches to
automatically detect metaphor and hyperbole have studied these language
phenomena independently, but their relationship has hardly, if ever, been
explored computationally. In this paper, we propose a multi-task deep learning
framework to detect hyperbole and metaphor simultaneously. We hypothesize that
metaphors help in hyperbole detection, and vice-versa. To test this hypothesis,
we annotate two hyperbole datasets- HYPO and HYPO-L- with metaphor labels.
Simultaneously, we annotate two metaphor datasets- TroFi and LCC- with
hyperbole labels. Experiments using these datasets give an improvement of the
state of the art of hyperbole detection by 12%. Additionally, our multi-task
learning (MTL) approach shows an improvement of up to 17% over single-task
learning (STL) for both hyperbole and metaphor detection, supporting our
hypothesis. To the best of our knowledge, ours is the first demonstration of
computational leveraging of linguistic intimacy between metaphor and hyperbole,
leading to showing the superiority of MTL over STL for hyperbole and metaphor
detection
Multilingual Multi-Figurative Language Detection
Figures of speech help people express abstract concepts and evoke stronger emotions than literal expressions, thereby making texts more creative and engaging. Due to its pervasive and fundamental character, figurative language understanding has been addressed in Natural Language Processing, but it's highly understudied in a multilingual setting and when considering more than one figure of speech at the same time. To bridge this gap, we introduce multilingual multi-figurative language modelling, and provide a benchmark for sentence-level figurative language detection, covering three common figures of speech and seven languages. Specifically, we develop a framework for figurative language detection based on template-based prompt learning. In so doing, we unify multiple detection tasks that are interrelated across multiple figures of speech and languages, without requiring task- or language-specific modules. Experimental results show that our framework outperforms several strong baselines and may serve as a blueprint for the joint modelling of other interrelated tasks.</p
Crowd-Sourcing A High-Quality Dataset for Metaphor Identification in Tweets
Metaphor is one of the most important elements of human communication, especially in informal settings such as social media. There have been a number of datasets created for metaphor identification, however, this task has proven difficult due to the nebulous nature of metaphoricity. In this paper, we present a crowd-sourcing approach for the creation of a dataset for metaphor identification, that is able to rapidly achieve large coverage over the different usages of metaphor in a given corpus while maintaining high accuracy. We validate this methodology by creating a set of 2,500 manually annotated tweets in English, for which we achieve inter-annotator agreement scores over 0.8, which is higher than other reported results that did not limit the task. This methodology is based on the use of an existing classifier for metaphor in order to assist in the identification and the selection of the examples for annotation, in a way that reduces the cognitive load for annotators and enables quick and accurate annotation. We selected a corpus of both general language tweets and political tweets relating to Brexit and we compare the resulting corpus on these two domains. As a result of this work, we have published the first dataset of tweets annotated for metaphors, which we believe will be invaluable for the development, training and evaluation of approaches for metaphor identification in tweets
Multilingual Multi-Figurative Language Detection
Figures of speech help people express abstract concepts and evoke stronger
emotions than literal expressions, thereby making texts more creative and
engaging. Due to its pervasive and fundamental character, figurative language
understanding has been addressed in Natural Language Processing, but it's
highly understudied in a multilingual setting and when considering more than
one figure of speech at the same time. To bridge this gap, we introduce
multilingual multi-figurative language modelling, and provide a benchmark for
sentence-level figurative language detection, covering three common figures of
speech and seven languages. Specifically, we develop a framework for figurative
language detection based on template-based prompt learning. In so doing, we
unify multiple detection tasks that are interrelated across multiple figures of
speech and languages, without requiring task- or language-specific modules.
Experimental results show that our framework outperforms several strong
baselines and may serve as a blueprint for the joint modelling of other
interrelated tasks.Comment: Accepted to ACL 2023 (Findings
Micro-Macro Analysis of Complex Networks
Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a \u201cclassic\u201d approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail (\u201cmicro\u201d) to a different scale level (\u201cmacro\u201d), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
Data augmentation is proven to be effective in many NLU tasks, especially for
those suffering from data scarcity. In this paper, we present a powerful and
easy to deploy text augmentation framework, Data Boost, which augments data
through reinforcement learning guided conditional generation. We evaluate Data
Boost on three diverse text classification tasks under five different
classifier architectures. The result shows that Data Boost can boost the
performance of classifiers especially in low-resource data scenarios. For
instance, Data Boost improves F1 for the three tasks by 8.7% on average when
given only 10% of the whole data for training. We also compare Data Boost with
six prior text augmentation methods. Through human evaluations (N=178), we
confirm that Data Boost augmentation has comparable quality as the original
data with respect to readability and class consistency.Comment: In proceedings of the 2020 Conference on Empirical Methods in Natural
Language Processing (EMNLP 2020). Onlin
Machines, journeys, prisons and yo-yos:Metaphors of pain, illness and medicine in consultations with chronic pain patients
Introduction: This paper examines pain, illness and medicine metaphors as used in consultations between chronic pain patients and anaesthesiologists, physiotherapists and psychologists in a Belgian pain clinic. As metaphors frame and highlight aspects of understanding and experiences of life events, including illness, they can provide insight in how health professionals and patients construct illness, pain and medicine in interaction. Materials and method: 16 intake consultations (collected in Belgium in April–May 2019) between 6 patients and 4 health professionals were qualitatively coded twice ATLAS. TI by a team of 3 coders, using an adjusted form of the Metaphor Identification Procedure. Each metaphor was labelled for source domain, target domain and speaker. Results: A number of metaphors that have been previously documented in past research were frequent in our data too, such as journey and machine metaphors, although sometimes also used differently, like war metaphors. Our data set also contained many few-used and sometimes more novel metaphors, such as ILLNESS IS A YO-YO. Many metaphors highlight particular aspects of living with and talking about chronic pain, such as its duration and persistent presence, a lack of agency and feelings of powerlessness, and a dualistic perspective on body and mind. Discussion and conclusion: The metaphors used by health professionals and patients give insight in the lived experience of having and treating chronic pain. In this way, they can contribute to our understanding of patients’ experiences and challenges, how they recur in clinical communication, and how they are related to wider discourses on health, illness and pain.</p
- …