32 research outputs found
Active Discriminative Text Representation Learning
We propose a new active learning (AL) method for text classification with
convolutional neural networks (CNNs). In AL, one selects the instances to be
manually labeled with the aim of maximizing model performance with minimal
effort. Neural models capitalize on word embeddings as representations
(features), tuning these to the task at hand. We argue that AL strategies for
multi-layered neural models should focus on selecting instances that most
affect the embedding space (i.e., induce discriminative word representations).
This is in contrast to traditional AL approaches (e.g., entropy-based
uncertainty sampling), which specify higher level objectives. We propose a
simple approach for sentence classification that selects instances containing
words whose embeddings are likely to be updated with the greatest magnitude,
thereby rapidly learning discriminative, task-specific embeddings. We extend
this approach to document classification by jointly considering: (1) the
expected changes to the constituent word representations; and (2) the model's
current overall uncertainty regarding the instance. The relative emphasis
placed on these criteria is governed by a stochastic process that favors
selecting instances likely to improve representations at the outset of
learning, and then shifts toward general uncertainty sampling as AL progresses.
Empirical results show that our method outperforms baseline AL approaches on
both sentence and document classification tasks. We also show that, as
expected, the method quickly learns discriminative word embeddings. To the best
of our knowledge, this is the first work on AL addressing neural models for
text classification.Comment: This paper got accepted by AAAI 201
Deep Active Learning for Named Entity Recognition
Deep learning has yielded state-of-the-art performance on many natural
language processing tasks including named entity recognition (NER). However,
this typically requires large amounts of labeled data. In this work, we
demonstrate that the amount of labeled training data can be drastically reduced
when deep learning is combined with active learning. While active learning is
sample-efficient, it can be computationally expensive since it requires
iterative retraining. To speed this up, we introduce a lightweight architecture
for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and
word encoders and a long short term memory (LSTM) tag decoder. The model
achieves nearly state-of-the-art performance on standard datasets for the task
while being computationally much more efficient than best performing models. We
carry out incremental active learning, during the training process, and are
able to nearly match state-of-the-art performance with just 25\% of the
original training data
How useful is Active Learning for Image-based Plant Phenotyping?
Deep learning models have been successfully deployed for a diverse array of
image-based plant phenotyping applications including disease detection and
classification. However, successful deployment of supervised deep learning
models requires large amount of labeled data, which is a significant challenge
in plant science (and most biological) domains due to the inherent complexity.
Specifically, data annotation is costly, laborious, time consuming and needs
domain expertise for phenotyping tasks, especially for diseases. To overcome
this challenge, active learning algorithms have been proposed that reduce the
amount of labeling needed by deep learning models to achieve good predictive
performance. Active learning methods adaptively select samples to annotate
using an acquisition function to achieve maximum (classification) performance
under a fixed labeling budget. We report the performance of four different
active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy,
(3) Least Confidence, and (4) Coreset, with conventional random sampling-based
annotation for two different image-based classification datasets. The first
image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to
eight different soybean stresses and a healthy class, and the second consists
of nine different weed species from the field. For a fixed labeling budget, we
observed that the classification performance of deep learning models with
active learning-based acquisition strategies is better than random
sampling-based acquisition for both datasets. The integration of active
learning strategies for data annotation can help mitigate labelling challenges
in the plant sciences applications particularly where deep domain knowledge is
required
Char-RNN and Active Learning for Hashtag Segmentation
We explore the abilities of character recurrent neural network (char-RNN) for
hashtag segmentation. Our approach to the task is the following: we generate
synthetic training dataset according to frequent n-grams that satisfy
predefined morpho-syntactic patterns to avoid any manual annotation. The active
learning strategy limits the training dataset and selects informative training
subset. The approach does not require any language-specific settings and is
compared for two languages, which differ in inflection degree.Comment: to appear in Cicling201