21 research outputs found
On Effectively Learning of Knowledge in Continual Pre-training
Pre-trained language models (PLMs) like BERT have made significant progress
in various downstream NLP tasks. However, by asking models to do cloze-style
tests, recent work finds that PLMs are short in acquiring knowledge from
unstructured text. To understand the internal behaviour of PLMs in retrieving
knowledge, we first define knowledge-baring (K-B) tokens and knowledge-free
(K-F) tokens for unstructured text and ask professional annotators to label
some samples manually. Then, we find that PLMs are more likely to give wrong
predictions on K-B tokens and attend less attention to those tokens inside the
self-attention module. Based on these observations, we develop two solutions to
help the model learn more knowledge from unstructured text in a fully
self-supervised manner. Experiments on knowledge-intensive tasks show the
effectiveness of the proposed methods. To our best knowledge, we are the first
to explore fully self-supervised learning of knowledge in continual
pre-training
ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization
Due to its simplicity and outstanding ability to generalize, stochastic
gradient descent (SGD) is still the most widely used optimization method
despite its slow convergence. Meanwhile, adaptive methods have attracted rising
attention of optimization and machine learning communities, both for the
leverage of life-long information and for the profound and fundamental
mathematical theory. Taking the best of both worlds is the most exciting and
challenging question in the field of optimization for machine learning. Along
this line, we revisited existing adaptive gradient methods from a novel
perspective, refreshing understanding of second moments. Our new perspective
empowers us to attach the properties of second moments to the first moment
iteration, and to propose a novel first moment optimizer,
\emph{Angle-Calibrated Moment method} (\method). Our theoretical results show
that \method is able to achieve the same convergence rate as mainstream
adaptive methods. Furthermore, extensive experiments on CV and NLP tasks
demonstrate that \method has a comparable convergence to SOTA Adam-type
optimizers, and gains a better generalization performance in most cases.Comment: 25 pages, 4 figure
Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization
Pretrained language models have achieved remarkable success in natural
language understanding. However, fine-tuning pretrained models on limited
training data tends to overfit and thus diminish performance. This paper
presents Bi-Drop, a fine-tuning strategy that selectively updates model
parameters using gradients from various sub-nets dynamically generated by
dropout. The sub-net estimation of Bi-Drop is performed in an in-batch manner,
so it overcomes the problem of hysteresis in sub-net updating, which is
possessed by previous methods that perform asynchronous sub-net estimation.
Also, Bi-Drop needs only one mini-batch to estimate the sub-net so it achieves
higher utility of training data. Experiments on the GLUE benchmark demonstrate
that Bi-Drop consistently outperforms previous fine-tuning methods.
Furthermore, empirical results also show that Bi-Drop exhibits excellent
generalization ability and robustness for domain transfer, data imbalance, and
low-resource scenarios.Comment: EMNLP 2023 Findings. Camera-ready version. Co-first authors with
equal contribution
A multiple criteria service composition selection algorithm supporting time-sensitive rules
Constructing composite services by using of services offered by third parties is an attractive and inexpensive way for service brokers and aggregators to enhance differentiation from their competitors. When multiple services provide the same or similar functionalities, selecting those that satisfy users' non-functional requirements is crucial. In many cases, non-functional properties of services are heavily dependent on the activity of the network delivering those services whilst the network activity follows certain time-sensitive rules. We present a service selection algorithm that takes into account time-sensitive variations of non-functional propensities of services to identify a service combination offering the highest quality within a specified time interval