908 research outputs found
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues
Conversations emerge as the primary media for exchanging ideas and
conceptions. From the listener's perspective, identifying various affective
qualities, such as sarcasm, humour, and emotions, is paramount for
comprehending the true connotation of the emitted utterance. However, one of
the major hurdles faced in learning these affect dimensions is the presence of
figurative language, viz. irony, metaphor, or sarcasm. We hypothesize that any
detection system constituting the exhaustive and explicit presentation of the
emitted utterance would improve the overall comprehension of the dialogue. To
this end, we explore the task of Sarcasm Explanation in Dialogues, which aims
to unfold the hidden irony behind sarcastic utterances. We propose MOSES, a
deep neural network, which takes a multimodal (sarcastic) dialogue instance as
an input and generates a natural language sentence as its explanation.
Subsequently, we leverage the generated explanation for various natural
language understanding tasks in a conversational dialogue setup, such as
sarcasm detection, humour identification, and emotion recognition. Our
evaluation shows that MOSES outperforms the state-of-the-art system for SED by
an average of ~2% on different evaluation metrics, such as ROUGE, BLEU, and
METEOR. Further, we observe that leveraging the generated explanation advances
three downstream tasks for affect classification - an average improvement of
~14% F1-score in the sarcasm detection task and ~2% in the humour
identification and emotion recognition task. We also perform extensive analyses
to assess the quality of the results.Comment: Accepted at AAAI 2023. 11 Pages; 14 Tables; 3 Figure
Computational Sarcasm Analysis on Social Media: A Systematic Review
Sarcasm can be defined as saying or writing the opposite of what one truly
wants to express, usually to insult, irritate, or amuse someone. Because of the
obscure nature of sarcasm in textual data, detecting it is difficult and of
great interest to the sentiment analysis research community. Though the
research in sarcasm detection spans more than a decade, some significant
advancements have been made recently, including employing unsupervised
pre-trained transformers in multimodal environments and integrating context to
identify sarcasm. In this study, we aim to provide a brief overview of recent
advancements and trends in computational sarcasm research for the English
language. We describe relevant datasets, methodologies, trends, issues,
challenges, and tasks relating to sarcasm that are beyond detection. Our study
provides well-summarized tables of sarcasm datasets, sarcastic features and
their extraction methods, and performance analysis of various approaches which
can help researchers in related domains understand current state-of-the-art
practices in sarcasm detection.Comment: 50 pages, 3 tables, Submitted to 'Data Mining and Knowledge
Discovery' for possible publicatio
Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation
Multimodal Sarcasm Explanation (MuSE) is a new yet challenging task, which
aims to generate a natural language sentence for a multimodal social post (an
image as well as its caption) to explain why it contains sarcasm. Although the
existing pioneer study has achieved great success with the BART backbone, it
overlooks the gap between the visual feature space and the decoder semantic
space, the object-level metadata of the image, as well as the potential
external knowledge. To solve these limitations, in this work, we propose a
novel mulTi-source sEmantic grAph-based Multimodal sarcasm explanation scheme,
named TEAM. In particular, TEAM extracts the object-level semantic meta-data
instead of the traditional global visual features from the input image.
Meanwhile, TEAM resorts to ConceptNet to obtain the external related knowledge
concepts for the input text and the extracted object meta-data. Thereafter,
TEAM introduces a multi-source semantic graph that comprehensively characterize
the multi-source (i.e., caption, object meta-data, external knowledge) semantic
relations to facilitate the sarcasm reasoning. Extensive experiments on a
public released dataset MORE verify the superiority of our model over
cutting-edge methods.Comment: Accepted by ACL 2023 main conferenc
TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models
Pre-trained large language models have recently achieved ground-breaking
performance in a wide variety of language understanding tasks. However, the
same model can not be applied to multimodal behavior understanding tasks (e.g.,
video sentiment/humor detection) unless non-verbal features (e.g., acoustic and
visual) can be integrated with language. Jointly modeling multiple modalities
significantly increases the model complexity, and makes the training process
data-hungry. While an enormous amount of text data is available via the web,
collecting large-scale multimodal behavioral video datasets is extremely
expensive, both in terms of time and money. In this paper, we investigate
whether large language models alone can successfully incorporate non-verbal
information when they are presented in textual form. We present a way to
convert the acoustic and visual information into corresponding textual
descriptions and concatenate them with the spoken text. We feed this augmented
input to a pre-trained BERT model and fine-tune it on three downstream
multimodal tasks: sentiment, humor, and sarcasm detection. Our approach,
TextMI, significantly reduces model complexity, adds interpretability to the
model's decision, and can be applied for a diverse set of tasks while achieving
superior (multimodal sarcasm detection) or near SOTA (multimodal sentiment
analysis and multimodal humor detection) performance. We propose TextMI as a
general, competitive baseline for multimodal behavioral analysis tasks,
particularly in a low-resource setting
A Multimodal Approach to Sarcasm Detection on Social Media
In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly more difficult than categorizing sentiment. Because, in sarcasm, the intended meaning of the expression by the user is opposite to the literal meaning. Because of its complex nature, it is often difficult even for human to detect sarcasm without proper context. However, people on social media succeed in detecting sarcasm despite interacting with strangers across the world. That motivates us to investigate the human process of detecting sarcasm on social media where abundant context information is often unavailable and the group of users communicating with each other are rarely well-acquainted. We have conducted a qualitative study to examine the patterns of users conveying sarcasm on social media. Whereas most sarcasm detection systems deal in word-by-word basis to accomplish their goal, we focused on the holistic sentiment conveyed by the post. We argue that utilization of word-level information will limit the systems performance to the domain of the dataset used to train the system and might not perform well for non-English language. As an endeavor to make our system less dependent on text data, we proposed a multimodal approach for sarcasm detection. We showed the applicability of images and reaction emoticons as other sources of hints about the sentiment of the post. Our research showed the superior results from a multimodal approach when compared to a unimodal approach. Multimodal sarcasm detection systems, as the one presented in this research, with the inclusion of more modes or sources of data might lead to a better sarcasm detection model
Argumentation by figurative language in verbal communication: a pragmatic perspective
This thesis has two goals. The first is to explain, within a pragmatic perspective,
how figurative language (i.e. metaphor and irony) performs argumentation. Based on
the argumentation theory (AT) of Perelman and Olbrecht-Tyteca (1958), argumentation
is defined as the process of justifying something in an organized or logical way, which
is composed of one or more claims and shows one or more grounds for maintaining
them.
The second goal is to examine the hearer’s interpretation of figurative utterances in
argumentation. The theoretical foundation of this discussion is based on experientialist
epistemology (i.e. experientialism) and cognitive pragmatics in the form of Relevance
Theory (RT).
In pursuit of those goals, I present four main innovations: First, I argue the status
of metaphor should be viewed as ‘what is implicated’, rather than ‘what is said’. Second,
I propose explanation of some exceptional cases of irony, which the standard RT
approach does not treat, which relies on the notion of ‘incongruity’. Third, I propose
integration of AT concepts within RT. Thus, this approach contributes to pursuing more
economical explanation of communication as argumentation, by a single principle of
relevance, but incorporating argumentative concepts such as doxa, topoi and polyphony.
Finally, I apply this integrated approach to analysing real cases of commercial
advertisement by metaphor or irony, or both. This includes explaining connection and
overlapping, two ways in which metaphor and irony can work together
Computational sarcasm detection and understanding in online communication
The presence of sarcasm in online communication has motivated an increasing number of computational investigations of sarcasm across the scientific community. In this thesis, we build upon these investigations. Pointing out their limitations, we bring four contributions that span two research directions: sarcasm detection and sarcasm understanding.
Sarcasm detection is the task of building computational models optimised for recognising sarcasm in a given text.
These models are often built in a supervised learning paradigm, relying on datasets of texts labelled for sarcasm.
We bring two contributions in this direction.
First, we question the effectiveness of previous methods used to label texts for sarcasm. We argue that the labels they produce might not coincide with the sarcastic intention of the authors of the texts that they are labelling.
In response, we suggest a new method, and we use it to build iSarcasm, a novel dataset of sarcastic and non-sarcastic tweets.
We show that previous models achieve considerably lower performance on iSarcasm than on previous datasets, while human annotators achieve a considerably higher performance, compared to models, pointing out the need for more effective models.
Therefore, as a second contribution, we organise a competition that invites the community to create such models.
Sarcasm understanding is the task of explicating the phenomena that are subsumed under the umbrella of sarcasm through computational investigation.
We bring two contributions in this direction.
First, we conduct an alaysis into the socio-demographic ecology of sarcastic exchanges between human interlocutors. We find that the effectiveness of such exchanges is influenced by the socio-demographic similarity between the interlocutors, with factors such as English language nativeness, age, and gender, being particualry influential. We suggest that future social analysis tools should account for these factors.
Second, we challenge the motivation of a recent endeavour of the community; mainly, that of augmenting dialogue systems with the ability to generate sarcastic responses. Through a series of social experiments, we provide guidelines for dialogue systems concerning the appropriateness of generating sarcastic responses, and the formulation of such responses.
Through our work, we aim to encourage the community to consider computational investigations of sarcasm interdisciplinarily, at the intersection of natural language processing and computational social science
Automated Moderation: Detecting Irony in a Norwegian Facebook Comment Section using a Longformer Transformer Model with a Context Encoded Dataset
Irony is a complex phenomenon of human communication and due to its contextual nature has been notoriously difficult for machine learning algorithms to detect. With an established practical definition of irony based in the environment of Facebook comment sections. Used together with a Norwegian language pre-trained BERT model converted to a long version that supports longer text inputs, and a Norwegian Facebook comment dataset with contextual article and reply comment text included. It was found that the long BERT model trained on the context included inputs dataset outperformed the short BERT models trained on datasets of the same and more comments, but without the contextual information encoded.Master's Thesis in Information ScienceINFO390MASV-INF
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