953 research outputs found
Understanding Emotion Valence is a Joint Deep Learning Task
The valence analysis of speakers' utterances or written posts helps to
understand the activation and variations of the emotional state throughout the
conversation. More recently, the concept of Emotion Carriers (EC) has been
introduced to explain the emotion felt by the speaker and its manifestations.
In this work, we investigate the natural inter-dependency of valence and ECs
via a multi-task learning approach. We experiment with Pre-trained Language
Models (PLM) for single-task, two-step, and joint settings for the valence and
EC prediction tasks. We compare and evaluate the performance of generative
(GPT-2) and discriminative (BERT) architectures in each setting. We observed
that providing the ground truth label of one task improves the prediction
performance of the models in the other task. We further observed that the
discriminative model achieves the best trade-off of valence and EC prediction
tasks in the joint prediction setting. As a result, we attain a single model
that performs both tasks, thus, saving computation resources at training and
inference times
Narrative Language as an Expression of Individual and Group Identity
Scientific Narrative Psychology integrates quantitative methodologies into the study of identity. Its methodology, Narrative Categorical Analysis, and its toolkit, NarrCat, were both originally developed by the Hungarian Narrative Psychology Group. NarrCat is for machine-made transformation of sentences in self-narratives into psychologically relevant, statistically processable narrative categories. The main body of this flexible and comprehensive system is formed by Psycho-Thematic modules, such as Agency, Evaluation, Emotion, Cognition, Spatiality, and Temporality. The Relational Modules include Social References, Semantic Role Labeling (SRL), and Negation. Certain elements can be combined into Hypermodules, such as Psychological Perspective and Spatio-Temporal Perspective, which allow for even more complex, higher level exploration of composite psychological processes. Using up-to-date developments of corpus linguistics and Natural Language Processing (NLP), a unique feature of NarrCat is its capacity of SRL. The structure of NarrCat, as well as the empirical results in group identity research, is discussed
Whats New? Identifying the Unfolding of New Events in Narratives
Narratives include a rich source of events unfolding over time and context.
Automatic understanding of these events provides a summarised comprehension of
the narrative for further computation (such as reasoning). In this paper, we
study the Information Status (IS) of the events and propose a novel challenging
task: the automatic identification of new events in a narrative. We define an
event as a triplet of subject, predicate, and object. The event is categorized
as new with respect to the discourse context and whether it can be inferred
through commonsense reasoning. We annotated a publicly available corpus of
narratives with the new events at sentence level using human annotators. We
present the annotation protocol and study the quality of the annotation and the
difficulty of the task. We publish the annotated dataset, annotation materials,
and machine learning baseline models for the task of new event extraction for
narrative understanding
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