452,953 research outputs found
DIALOG-22 RuATD Generated Text Detection
Text Generation Models (TGMs) succeed in creating text that matches human
language style reasonably well. Detectors that can distinguish between
TGM-generated text and human-written ones play an important role in preventing
abuse of TGM.
In this paper, we describe our pipeline for the two DIALOG-22 RuATD tasks:
detecting generated text (binary task) and classification of which model was
used to generate text (multiclass task). We achieved 1st place on the binary
classification task with an accuracy score of 0.82995 on the private test set
and 4th place on the multiclass classification task with an accuracy score of
0.62856 on the private test set. We proposed an ensemble method of different
pre-trained models based on the attention mechanism.Comment: 6 page
Adversarial Multi-task Learning for Text Classification
Neural network models have shown their promising opportunities for multi-task
learning, which focus on learning the shared layers to extract the common and
task-invariant features. However, in most existing approaches, the extracted
shared features are prone to be contaminated by task-specific features or the
noise brought by other tasks. In this paper, we propose an adversarial
multi-task learning framework, alleviating the shared and private latent
feature spaces from interfering with each other. We conduct extensive
experiments on 16 different text classification tasks, which demonstrates the
benefits of our approach. Besides, we show that the shared knowledge learned by
our proposed model can be regarded as off-the-shelf knowledge and easily
transferred to new tasks. The datasets of all 16 tasks are publicly available
at \url{http://nlp.fudan.edu.cn/data/}Comment: Accepted by ACL201
CrypTen: Secure Multi-Party Computation Meets Machine Learning
Secure multi-party computation (MPC) allows parties to perform computations
on data while keeping that data private. This capability has great potential
for machine-learning applications: it facilitates training of machine-learning
models on private data sets owned by different parties, evaluation of one
party's private model using another party's private data, etc. Although a range
of studies implement machine-learning models via secure MPC, such
implementations are not yet mainstream. Adoption of secure MPC is hampered by
the absence of flexible software frameworks that "speak the language" of
machine-learning researchers and engineers. To foster adoption of secure MPC in
machine learning, we present CrypTen: a software framework that exposes popular
secure MPC primitives via abstractions that are common in modern
machine-learning frameworks, such as tensor computations, automatic
differentiation, and modular neural networks. This paper describes the design
of CrypTen and measure its performance on state-of-the-art models for text
classification, speech recognition, and image classification. Our benchmarks
show that CrypTen's GPU support and high-performance communication between (an
arbitrary number of) parties allows it to perform efficient private evaluation
of modern machine-learning models under a semi-honest threat model. For
example, two parties using CrypTen can securely predict phonemes in speech
recordings using Wav2Letter faster than real-time. We hope that CrypTen will
spur adoption of secure MPC in the machine-learning community
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