137,349 research outputs found
Few-shot Text Classification with Dual Contrastive Consistency
In this paper, we explore how to utilize pre-trained language model to
perform few-shot text classification where only a few annotated examples are
given for each class. Since using traditional cross-entropy loss to fine-tune
language model under this scenario causes serious overfitting and leads to
sub-optimal generalization of model, we adopt supervised contrastive learning
on few labeled data and consistency-regularization on vast unlabeled data.
Moreover, we propose a novel contrastive consistency to further boost model
performance and refine sentence representation. After conducting extensive
experiments on four datasets, we demonstrate that our model (FTCC) can
outperform state-of-the-art methods and has better robustness.Comment: 8 pages, 2 figures, under revie
Towards Agile Text Classifiers for Everyone
Text-based safety classifiers are widely used for content moderation and
increasingly to tune generative language model behavior - a topic of growing
concern for the safety of digital assistants and chatbots. However, different
policies require different classifiers, and safety policies themselves improve
from iteration and adaptation. This paper introduces and evaluates methods for
agile text classification, whereby classifiers are trained using small,
targeted datasets that can be quickly developed for a particular policy.
Experimenting with 7 datasets from three safety-related domains, comprising 15
annotation schemes, led to our key finding: prompt-tuning large language
models, like PaLM 62B, with a labeled dataset of as few as 80 examples can
achieve state-of-the-art performance. We argue that this enables a paradigm
shift for text classification, especially for models supporting safer online
discourse. Instead of collecting millions of examples to attempt to create
universal safety classifiers over months or years, classifiers could be tuned
using small datasets, created by individuals or small organizations, tailored
for specific use cases, and iterated on and adapted in the time-span of a day.Comment: Findings of EMNLP 202
Music classification by transductive learning using bipartite heterogeneous networks
The popularization of music distribution in electronic format has increased the amount of music with incomplete metadata. The incompleteness of data can hamper some important tasks, such as music and artist recommendation. In this scenario, transductive classification can be used to classify the whole dataset considering just few labeled instances. Usually transductive classification is performed through label propagation, in which data are represented as networks and the examples propagate their labels through\ud
their connections. Similarity-based networks are usually applied to model data as network. However, this kind of representation requires the definition of parameters, which significantly affect the classification accuracy, and presentes a high cost due to the computation of similarities among all dataset instances. In contrast, bipartite heterogeneous networks have appeared as an alternative to similarity-based networks in text mining applications. In these networks, the words are connected to the documents which they occur. Thus, there is no parameter or additional costs to generate such networks. In this paper, we propose the use of the bipartite network representation to perform transductive classification of music, using a bag-of-frames approach to describe music signals. We demonstrate that the proposed approach outperforms other music classification approaches when few labeled instances are available.Sao Paulo Research Foundation (FAPESP) (grants 2011/12823-6, 2012/50714-7, 2013/26151-5, and 2014/08996-0
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Quick interaction between a human teacher and a learning machine presents
numerous benefits and challenges when working with web-scale data. The human
teacher guides the machine towards accomplishing the task of interest. The
learning machine leverages big data to find examples that maximize the training
value of its interaction with the teacher. When the teacher is restricted to
labeling examples selected by the machine, this problem is an instance of
active learning. When the teacher can provide additional information to the
machine (e.g., suggestions on what examples or predictive features should be
used) as the learning task progresses, then the problem becomes one of
interactive learning.
To accommodate the two-way communication channel needed for efficient
interactive learning, the teacher and the machine need an environment that
supports an interaction language. The machine can access, process, and
summarize more examples than the teacher can see in a lifetime. Based on the
machine's output, the teacher can revise the definition of the task or make it
more precise. Both the teacher and the machine continuously learn and benefit
from the interaction.
We have built a platform to (1) produce valuable and deployable models and
(2) support research on both the machine learning and user interface challenges
of the interactive learning problem. The platform relies on a dedicated,
low-latency, distributed, in-memory architecture that allows us to construct
web-scale learning machines with quick interaction speed. The purpose of this
paper is to describe this architecture and demonstrate how it supports our
research efforts. Preliminary results are presented as illustrations of the
architecture but are not the primary focus of the paper
Complex Event Recognition from Images with Few Training Examples
We propose to leverage concept-level representations for complex event
recognition in photographs given limited training examples. We introduce a
novel framework to discover event concept attributes from the web and use that
to extract semantic features from images and classify them into social event
categories with few training examples. Discovered concepts include a variety of
objects, scenes, actions and event sub-types, leading to a discriminative and
compact representation for event images. Web images are obtained for each
discovered event concept and we use (pretrained) CNN features to train concept
classifiers. Extensive experiments on challenging event datasets demonstrate
that our proposed method outperforms several baselines using deep CNN features
directly in classifying images into events with limited training examples. We
also demonstrate that our method achieves the best overall accuracy on a
dataset with unseen event categories using a single training example.Comment: Accepted to Winter Applications of Computer Vision (WACV'17
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector,
have been attracting increasing attention due to their simplicity, scalability,
and effectiveness. However, comparing to sophisticated deep learning
architectures such as convolutional neural networks, these methods usually
yield inferior results when applied to particular machine learning tasks. One
possible reason is that these text embedding methods learn the representation
of text in a fully unsupervised way, without leveraging the labeled information
available for the task. Although the low dimensional representations learned
are applicable to many different tasks, they are not particularly tuned for any
task. In this paper, we fill this gap by proposing a semi-supervised
representation learning method for text data, which we call the
\textit{predictive text embedding} (PTE). Predictive text embedding utilizes
both labeled and unlabeled data to learn the embedding of text. The labeled
information and different levels of word co-occurrence information are first
represented as a large-scale heterogeneous text network, which is then embedded
into a low dimensional space through a principled and efficient algorithm. This
low dimensional embedding not only preserves the semantic closeness of words
and documents, but also has a strong predictive power for the particular task.
Compared to recent supervised approaches based on convolutional neural
networks, predictive text embedding is comparable or more effective, much more
efficient, and has fewer parameters to tune.Comment: KDD 201
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