36,203 research outputs found
Co-training for Demographic Classification Using Deep Learning from Label Proportions
Deep learning algorithms have recently produced state-of-the-art accuracy in
many classification tasks, but this success is typically dependent on access to
many annotated training examples. For domains without such data, an attractive
alternative is to train models with light, or distant supervision. In this
paper, we introduce a deep neural network for the Learning from Label
Proportion (LLP) setting, in which the training data consist of bags of
unlabeled instances with associated label distributions for each bag. We
introduce a new regularization layer, Batch Averager, that can be appended to
the last layer of any deep neural network to convert it from supervised
learning to LLP. This layer can be implemented readily with existing deep
learning packages. To further support domains in which the data consist of two
conditionally independent feature views (e.g. image and text), we propose a
co-training algorithm that iteratively generates pseudo bags and refits the
deep LLP model to improve classification accuracy. We demonstrate our models on
demographic attribute classification (gender and race/ethnicity), which has
many applications in social media analysis, public health, and marketing. We
conduct experiments to predict demographics of Twitter users based on their
tweets and profile image, without requiring any user-level annotations for
training. We find that the deep LLP approach outperforms baselines for both
text and image features separately. Additionally, we find that co-training
algorithm improves image and text classification by 4% and 8% absolute F1,
respectively. Finally, an ensemble of text and image classifiers further
improves the absolute F1 measure by 4% on average
Mining the Demographics of Political Sentiment from Twitter Using Learning from Label Proportions
Opinion mining and demographic attribute inference have many applications in
social science. In this paper, we propose models to infer daily joint
probabilities of multiple latent attributes from Twitter data, such as
political sentiment and demographic attributes. Since it is costly and
time-consuming to annotate data for traditional supervised classification, we
instead propose scalable Learning from Label Proportions (LLP) models for
demographic and opinion inference using U.S. Census, national and state
political polls, and Cook partisan voting index as population level data. In
LLP classification settings, the training data is divided into a set of
unlabeled bags, where only the label distribution in of each bag is known,
removing the requirement of instance-level annotations. Our proposed LLP model,
Weighted Label Regularization (WLR), provides a scalable generalization of
prior work on label regularization to support weights for samples inside bags,
which is applicable in this setting where bags are arranged hierarchically
(e.g., county-level bags are nested inside of state-level bags). We apply our
model to Twitter data collected in the year leading up to the 2016 U.S.
presidential election, producing estimates of the relationships among political
sentiment and demographics over time and place. We find that our approach
closely tracks traditional polling data stratified by demographic category,
resulting in error reductions of 28-44% over baseline approaches. We also
provide descriptive evaluations showing how the model may be used to estimate
interactions among many variables and to identify linguistic temporal
variation, capabilities which are typically not feasible using traditional
polling methods
Neural Machine Translation into Language Varieties
Both research and commercial machine translation have so far neglected the
importance of properly handling the spelling, lexical and grammar divergences
occurring among language varieties. Notable cases are standard national
varieties such as Brazilian and European Portuguese, and Canadian and European
French, which popular online machine translation services are not keeping
distinct. We show that an evident side effect of modeling such varieties as
unique classes is the generation of inconsistent translations. In this work, we
investigate the problem of training neural machine translation from English to
specific pairs of language varieties, assuming both labeled and unlabeled
parallel texts, and low-resource conditions. We report experiments from English
to two pairs of dialects, EuropeanBrazilian Portuguese and European-Canadian
French, and two pairs of standardized varieties, Croatian-Serbian and
Indonesian-Malay. We show significant BLEU score improvements over baseline
systems when translation into similar languages is learned as a multilingual
task with shared representations.Comment: Published at EMNLP 2018: third conference on machine translation (WMT
2018
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
Due to the importance of zero-shot learning, i.e. classifying images where
there is a lack of labeled training data, the number of proposed approaches has
recently increased steadily. We argue that it is time to take a step back and
to analyze the status quo of the area. The purpose of this paper is three-fold.
First, given the fact that there is no agreed upon zero-shot learning
benchmark, we first define a new benchmark by unifying both the evaluation
protocols and data splits of publicly available datasets used for this task.
This is an important contribution as published results are often not comparable
and sometimes even flawed due to, e.g. pre-training on zero-shot test classes.
Moreover, we propose a new zero-shot learning dataset, the Animals with
Attributes 2 (AWA2) dataset which we make publicly available both in terms of
image features and the images themselves. Second, we compare and analyze a
significant number of the state-of-the-art methods in depth, both in the
classic zero-shot setting but also in the more realistic generalized zero-shot
setting. Finally, we discuss in detail the limitations of the current status of
the area which can be taken as a basis for advancing it.Comment: Accepted by TPAMI in July, 2018. We introduce Proposed Split Version
2.0 (Please download it from our project webpage). arXiv admin note:
substantial text overlap with arXiv:1703.0439
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