9,799 research outputs found
Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus
Most Semantic Role Labeling (SRL) approaches are supervised methods which
require a significant amount of annotated corpus, and the annotation requires
linguistic expertise. In this paper, we propose a Multi-Task Active Learning
framework for Semantic Role Labeling with Entity Recognition (ER) as the
auxiliary task to alleviate the need for extensive data and use additional
information from ER to help SRL. We evaluate our approach on Indonesian
conversational dataset. Our experiments show that multi-task active learning
can outperform single-task active learning method and standard multi-task
learning. According to our results, active learning is more efficient by using
12% less of training data compared to passive learning in both single-task and
multi-task setting. We also introduce a new dataset for SRL in Indonesian
conversational domain to encourage further research in this area.Comment: ACL 2018 workshop on Deep Learning Approaches for Low-Resource NL
AppTechMiner: Mining Applications and Techniques from Scientific Articles
This paper presents AppTechMiner, a rule-based information extraction
framework that automatically constructs a knowledge base of all application
areas and problem solving techniques. Techniques include tools, methods,
datasets or evaluation metrics. We also categorize individual research articles
based on their application areas and the techniques proposed/improved in the
article. Our system achieves high average precision (~82%) and recall (~84%) in
knowledge base creation. It also performs well in application and technique
assignment to an individual article (average accuracy ~66%). In the end, we
further present two use cases presenting a trivial information retrieval system
and an extensive temporal analysis of the usage of techniques and application
areas. At present, we demonstrate the framework for the domain of computational
linguistics but this can be easily generalized to any other field of research.Comment: JCDL 2017, 6th International Workshop on Mining Scientific
Publications. arXiv admin note: substantial text overlap with
arXiv:1608.0638
Learning to see like children: proof of concept
In the last few years we have seen a growing interest in machine learning
approaches to computer vision and, especially, to semantic labeling. Nowadays
state of the art systems use deep learning on millions of labeled images with
very successful results on benchmarks, though it is unlikely to expect similar
results in unrestricted visual environments. Most learning schemes essentially
ignore the inherent sequential structure of videos: this might be a critical
issue, since any visual recognition process is remarkably more complex when
shuffling video frames. Based on this remark, we propose a re-foundation of the
communication protocol between visual agents and the environment, which is
referred to as learning to see like children. Like for human interaction,
visual concepts are acquired by the agents solely by processing their own
visual stream along with human supervisions on selected pixels. We give a proof
of concept that remarkable semantic labeling can emerge within this protocol by
using only a few supervised examples. This is made possible by exploiting a
constraint of motion coherent labeling that virtually offers tons of
supervisions. Additional visual constraints, including those associated with
object supervisions, are used within the context of learning from constraints.
The framework is extended in the direction of lifelong learning, so as our
visual agents live in their own visual environment without distinguishing
learning and test set. Learning takes place in deep architectures under a
progressive developmental scheme. In order to evaluate our Developmental Visual
Agents (DVAs), in addition to classic benchmarks, we open the doors of our lab,
allowing people to evaluate DVAs by crowd-sourcing. Such assessment mechanism
might result in a paradigm shift in methodologies and algorithms for computer
vision, encouraging truly novel solutions within the proposed framework
Building a Semantic Role Labelling System for Vietnamese
Semantic role labelling (SRL) is a task in natural language processing which
detects and classifies the semantic arguments associated with the predicates of
a sentence. It is an important step towards understanding the meaning of a
natural language. There exists SRL systems for well-studied languages like
English, Chinese or Japanese but there is not any such system for the
Vietnamese language. In this paper, we present the first SRL system for
Vietnamese with encouraging accuracy. We first demonstrate that a simple
application of SRL techniques developed for English could not give a good
accuracy for Vietnamese. We then introduce a new algorithm for extracting
candidate syntactic constituents, which is much more accurate than the common
node-mapping algorithm usually used in the identification step. Finally, in the
classification step, in addition to the common linguistic features, we propose
novel and useful features for use in SRL. Our SRL system achieves an
score of 73.53\% on the Vietnamese PropBank corpus. This system, including
software and corpus, is available as an open source project and we believe that
it is a good baseline for the development of future Vietnamese SRL systems.Comment: 8 pages, ICDIM 201
Natural Language Processing (almost) from Scratch
We propose a unified neural network architecture and learning algorithm that
can be applied to various natural language processing tasks including:
part-of-speech tagging, chunking, named entity recognition, and semantic role
labeling. This versatility is achieved by trying to avoid task-specific
engineering and therefore disregarding a lot of prior knowledge. Instead of
exploiting man-made input features carefully optimized for each task, our
system learns internal representations on the basis of vast amounts of mostly
unlabeled training data. This work is then used as a basis for building a
freely available tagging system with good performance and minimal computational
requirements
Multi-task Learning for Japanese Predicate Argument Structure Analysis
An event-noun is a noun that has an argument structure similar to a
predicate. Recent works, including those considered state-of-the-art, ignore
event-nouns or build a single model for solving both Japanese predicate
argument structure analysis (PASA) and event-noun argument structure analysis
(ENASA). However, because there are interactions between predicates and
event-nouns, it is not sufficient to target only predicates. To address this
problem, we present a multi-task learning method for PASA and ENASA. Our
multi-task models improved the performance of both tasks compared to a
single-task model by sharing knowledge from each task. Moreover, in PASA, our
models achieved state-of-the-art results in overall F1 scores on the NAIST Text
Corpus. In addition, this is the first work to employ neural networks in ENASA.Comment: 10 pages; NAACL 201
Representing Verbs as Argument Concepts
Verbs play an important role in the understanding of natural language text.
This paper studies the problem of abstracting the subject and object arguments
of a verb into a set of noun concepts, known as the "argument concepts". This
set of concepts, whose size is parameterized, represents the fine-grained
semantics of a verb. For example, the object of "enjoy" can be abstracted into
time, hobby and event, etc. We present a novel framework to automatically infer
human readable and machine computable action concepts with high accuracy.Comment: 7 pages, 2 figures, AAAI 201
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned
from a labeled source dataset to solve similar tasks in a new unlabeled domain.
Prior UDA methods typically require to access the source data when learning to
adapt the model, making them risky and inefficient for decentralized private
data. This work tackles a practical setting where only a trained source model
is available and investigates how we can effectively utilize such a model
without source data to solve UDA problems. We propose a simple yet generic
representation learning framework, named \emph{Source HypOthesis Transfer}
(SHOT). SHOT freezes the classifier module (hypothesis) of the source model and
learns the target-specific feature extraction module by exploiting both
information maximization and self-supervised pseudo-labeling to implicitly
align representations from the target domains to the source hypothesis. To
verify its versatility, we evaluate SHOT in a variety of adaptation cases
including closed-set, partial-set, and open-set domain adaptation. Experiments
indicate that SHOT yields state-of-the-art results among multiple domain
adaptation benchmarks.Comment: ICML2020. Fix the typos for Digits. Code is available at
https://github.com/tim-learn/SHO
Vietnamese Semantic Role Labelling
In this paper, we study semantic role labelling (SRL), a subtask of semantic
parsing of natural language sentences and its application for the Vietnamese
language. We present our effort in building Vietnamese PropBank, the first
Vietnamese SRL corpus and a software system for labelling semantic roles of
Vietnamese texts. In particular, we present a novel constituent extraction
algorithm in the argument candidate identification step which is more suitable
and more accurate than the common node-mapping method. In the machine learning
part, our system integrates distributed word features produced by two recent
unsupervised learning models in two learned statistical classifiers and makes
use of integer linear programming inference procedure to improve the accuracy.
The system is evaluated in a series of experiments and achieves a good result,
an score of 74.77%. Our system, including corpus and software, is
available as an open source project for free research and we believe that it is
a good baseline for the development of future Vietnamese SRL systems.Comment: Accepted to the VNU Journal of Scienc
Variational Adversarial Active Learning
Active learning aims to develop label-efficient algorithms by sampling the
most representative queries to be labeled by an oracle. We describe a
pool-based semi-supervised active learning algorithm that implicitly learns
this sampling mechanism in an adversarial manner. Unlike conventional active
learning algorithms, our approach is task agnostic, i.e., it does not depend on
the performance of the task for which we are trying to acquire labeled data.
Our method learns a latent space using a variational autoencoder (VAE) and an
adversarial network trained to discriminate between unlabeled and labeled data.
The mini-max game between the VAE and the adversarial network is played such
that while the VAE tries to trick the adversarial network into predicting that
all data points are from the labeled pool, the adversarial network learns how
to discriminate between dissimilarities in the latent space. We extensively
evaluate our method on various image classification and semantic segmentation
benchmark datasets and establish a new state of the art on
, , ,
, and . Our results demonstrate that our
adversarial approach learns an effective low dimensional latent space in
large-scale settings and provides for a computationally efficient sampling
method. Our code is available at https://github.com/sinhasam/vaal.Comment: First two authors contributed equally, listed alphabetically.
Accepted as Oral at ICCV 201
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