5,929 research outputs found
Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks
When constructing models that learn from noisy labels produced by multiple
annotators, it is important to accurately estimate the reliability of
annotators. Annotators may provide labels of inconsistent quality due to their
varying expertise and reliability in a domain. Previous studies have mostly
focused on estimating each annotator's overall reliability on the entire
annotation task. However, in practice, the reliability of an annotator may
depend on each specific instance. Only a limited number of studies have
investigated modelling per-instance reliability and these only considered
binary labels. In this paper, we propose an unsupervised model which can handle
both binary and multi-class labels. It can automatically estimate the
per-instance reliability of each annotator and the correct label for each
instance. We specify our model as a probabilistic model which incorporates
neural networks to model the dependency between latent variables and instances.
For evaluation, the proposed method is applied to both synthetic and real data,
including two labelling tasks: text classification and textual entailment.
Experimental results demonstrate our novel method can not only accurately
estimate the reliability of annotators across different instances, but also
achieve superior performance in predicting the correct labels and detecting the
least reliable annotators compared to state-of-the-art baselines.Comment: 9 pages, 1 figures, 10 tables, 2019 Annual Conference of the North
American Chapter of the Association for Computational Linguistics (NAACL2019
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Modeling Empathy and Distress in Reaction to News Stories
Computational detection and understanding of empathy is an important factor
in advancing human-computer interaction. Yet to date, text-based empathy
prediction has the following major limitations: It underestimates the
psychological complexity of the phenomenon, adheres to a weak notion of ground
truth where empathic states are ascribed by third parties, and lacks a shared
corpus. In contrast, this contribution presents the first publicly available
gold standard for empathy prediction. It is constructed using a novel
annotation methodology which reliably captures empathy assessments by the
writer of a statement using multi-item scales. This is also the first
computational work distinguishing between multiple forms of empathy, empathic
concern, and personal distress, as recognized throughout psychology. Finally,
we present experimental results for three different predictive models, of which
a CNN performs the best.Comment: To appear at EMNLP 201
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