1,984 research outputs found
Joint Modelling of Emotion and Abusive Language Detection
The rise of online communication platforms has been accompanied by some
undesirable effects, such as the proliferation of aggressive and abusive
behaviour online. Aiming to tackle this problem, the natural language
processing (NLP) community has experimented with a range of techniques for
abuse detection. While achieving substantial success, these methods have so far
only focused on modelling the linguistic properties of the comments and the
online communities of users, disregarding the emotional state of the users and
how this might affect their language. The latter is, however, inextricably
linked to abusive behaviour. In this paper, we present the first joint model of
emotion and abusive language detection, experimenting in a multi-task learning
framework that allows one task to inform the other. Our results demonstrate
that incorporating affective features leads to significant improvements in
abuse detection performance across datasets.Comment: Proceedings of the 58th Annual Meeting of the Association for
Computational Linguistics, 202
From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue
Emotion recognition in conversations (ERC) is a crucial task for building
human-like conversational agents. While substantial efforts have been devoted
to ERC for chit-chat dialogues, the task-oriented counterpart is largely left
unattended. Directly applying chit-chat ERC models to task-oriented dialogues
(ToDs) results in suboptimal performance as these models overlook key features
such as the correlation between emotions and task completion in ToDs. In this
paper, we propose a framework that turns a chit-chat ERC model into a
task-oriented one, addressing three critical aspects: data, features and
objective. First, we devise two ways of augmenting rare emotions to improve ERC
performance. Second, we use dialogue states as auxiliary features to
incorporate key information from the goal of the user. Lastly, we leverage a
multi-aspect emotion definition in ToDs to devise a multi-task learning
objective and a novel emotion-distance weighted loss function. Our framework
yields significant improvements for a range of chit-chat ERC models on EmoWOZ,
a large-scale dataset for user emotion in ToDs. We further investigate the
generalisability of the best resulting model to predict user satisfaction in
different ToD datasets. A comparison with supervised baselines shows a strong
zero-shot capability, highlighting the potential usage of our framework in
wider scenarios.Comment: Accepted by SIGDIAL 202
Bridging Emotion Role Labeling and Appraisal-based Emotion Analysis
The term emotion analysis in text subsumes various natural language
processing tasks which have in common the goal to enable computers to
understand emotions. Most popular is emotion classification in which one or
multiple emotions are assigned to a predefined textual unit. While such setting
is appropriate to identify the reader's or author's emotion, emotion role
labeling adds the perspective of mentioned entities and extracts text spans
that correspond to the emotion cause. The underlying emotion theories agree on
one important point; that an emotion is caused by some internal or external
event and comprises several subcomponents, including the subjective feeling and
a cognitive evaluation. We therefore argue that emotions and events are related
in two ways. (1) Emotions are events; and this perspective is the fundament in
NLP for emotion role labeling. (2) Emotions are caused by events; a perspective
that is made explicit with research how to incorporate psychological appraisal
theories in NLP models to interpret events. These two research directions, role
labeling and (event-focused) emotion classification, have by and large been
tackled separately. We contributed to both directions with the projects SEAT
(Structured Multi-Domain Emotion Analysis from Text) and CEAT (Computational
Event Evaluation based on Appraisal Theories for Emotion Analysis), both funded
by the German Research Foundation. In this paper, we consolidate the findings
and point out open research questions.Comment: under review for https://bigpictureworkshop.com
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