12,778 research outputs found
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
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
Emotion Embeddings \unicode{x2014} Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets
Human emotion is expressed in many communication modalities and media formats
and so their computational study is equally diversified into natural language
processing, audio signal analysis, computer vision, etc. Similarly, the large
variety of representation formats used in previous research to describe
emotions (polarity scales, basic emotion categories, dimensional approaches,
appraisal theory, etc.) have led to an ever proliferating diversity of
datasets, predictive models, and software tools for emotion analysis. Because
of these two distinct types of heterogeneity, at the expressional and
representational level, there is a dire need to unify previous work on
increasingly diverging data and label types. This article presents such a
unifying computational model. We propose a training procedure that learns a
shared latent representation for emotions, so-called emotion embeddings,
independent of different natural languages, communication modalities, media or
representation label formats, and even disparate model architectures.
Experiments on a wide range of heterogeneous affective datasets indicate that
this approach yields the desired interoperability for the sake of reusability,
interpretability and flexibility, without penalizing prediction quality. Code
and data are archived under https://doi.org/10.5281/zenodo.7405327 .Comment: 18 pages, 6 figure
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The role of HG in the analysis of temporal iteration and interaural correlation
PersoNER: Persian named-entity recognition
© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network
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