4 research outputs found
Dialogue Act Sequence Labeling using Hierarchical encoder with CRF
Dialogue Act recognition associate dialogue acts (i.e., semantic labels) to
utterances in a conversation. The problem of associating semantic labels to
utterances can be treated as a sequence labeling problem. In this work, we
build a hierarchical recurrent neural network using bidirectional LSTM as a
base unit and the conditional random field (CRF) as the top layer to classify
each utterance into its corresponding dialogue act. The hierarchical network
learns representations at multiple levels, i.e., word level, utterance level,
and conversation level. The conversation level representations are input to the
CRF layer, which takes into account not only all previous utterances but also
their dialogue acts, thus modeling the dependency among both, labels and
utterances, an important consideration of natural dialogue. We validate our
approach on two different benchmark data sets, Switchboard and Meeting Recorder
Dialogue Act, and show performance improvement over the state-of-the-art
methods by and absolute points, respectively. It is worth
noting that the inter-annotator agreement on Switchboard data set is ,
and our method is able to achieve the accuracy of about despite being
trained on the noisy data
"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets
For an unknown (new) classification dataset, choosing an appropriate deep
learning architecture is often a recursive, time-taking, and laborious process.
In this research, we propose a novel technique to recommend a suitable
architecture from a repository of known models. Further, we predict the
performance accuracy of the recommended architecture on the given unknown
dataset, without the need for training the model. We propose a model encoder
approach to learn a fixed length representation of deep learning architectures
along with its hyperparameters, in an unsupervised fashion. We manually curate
a repository of image datasets with corresponding known deep learning models
and show that the predicted accuracy is a good estimator of the actual
accuracy. We discuss the implications of the proposed approach for three
benchmark images datasets and also the challenges in using the approach for
text modality. To further increase the reproducibility of the proposed
approach, the entire implementation is made publicly available along with the
trained models.Comment: NeurIPS 2019, New in ML Grou
Fine Grained Classification of Personal Data Entities
Entity Type Classification can be defined as the task of assigning category
labels to entity mentions in documents. While neural networks have recently
improved the classification of general entity mentions, pattern matching and
other systems continue to be used for classifying personal data entities (e.g.
classifying an organization as a media company or a government institution for
GDPR, and HIPAA compliance). We propose a neural model to expand the class of
personal data entities that can be classified at a fine grained level, using
the output of existing pattern matching systems as additional contextual
features. We introduce new resources, a personal data entities hierarchy with
134 types, and two datasets from the Wikipedia pages of elected representatives
and Enron emails. We hope these resource will aid research in the area of
personal data discovery, and to that effect, we provide baseline results on
these datasets, and compare our method with state of the art models on
OntoNotes dataset
A Neural Architecture for Person Ontology population
A person ontology comprising concepts, attributes and relationships of people
has a number of applications in data protection, didentification, population of
knowledge graphs for business intelligence and fraud prevention. While
artificial neural networks have led to improvements in Entity Recognition,
Entity Classification, and Relation Extraction, creating an ontology largely
remains a manual process, because it requires a fixed set of semantic relations
between concepts. In this work, we present a system for automatically
populating a person ontology graph from unstructured data using neural models
for Entity Classification and Relation Extraction. We introduce a new dataset
for these tasks and discuss our results.Comment: 6 pages, 10 figures. arXiv admin note: substantial text overlap with
arXiv:1811.0936