507,282 research outputs found
Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation
In this paper, we empirically evaluate the utility of transfer and multi-task
learning on a challenging semantic classification task: semantic interpretation
of noun--noun compounds. Through a comprehensive series of experiments and
in-depth error analysis, we show that transfer learning via parameter
initialization and multi-task learning via parameter sharing can help a neural
classification model generalize over a highly skewed distribution of relations.
Further, we demonstrate how dual annotation with two distinct sets of relations
over the same set of compounds can be exploited to improve the overall accuracy
of a neural classifier and its F1 scores on the less frequent, but more
difficult relations.Comment: EMNLP 2018: Conference on Empirical Methods in Natural Language
Processing (EMNLP
Learning Multiple Dense Prediction Tasks from Partially Annotated Data
Despite the recent advances in multi-task learning of dense prediction
problems, most methods rely on expensive labelled datasets. In this paper, we
present a label efficient approach and look at jointly learning of multiple
dense prediction tasks on partially annotated data (i.e. not all the task
labels are available for each image), which we call multi-task
partially-supervised learning. We propose a multi-task training procedure that
successfully leverages task relations to supervise its multi-task learning when
data is partially annotated. In particular, we learn to map each task pair to a
joint pairwise task-space which enables sharing information between them in a
computationally efficient way through another network conditioned on task
pairs, and avoids learning trivial cross-task relations by retaining high-level
information about the input image. We rigorously demonstrate that our proposed
method effectively exploits the images with unlabelled tasks and outperforms
existing semi-supervised learning approaches and related methods on three
standard benchmarks.Comment: CVPR2022, Multi-task Partially-supervised Learning, Code will be
available at https://github.com/VICO-UoE/MTPS
Multi-task Active Learning for Pre-trained Transformer-based Models
Multi-task learning, in which several tasks are jointly learned by a single
model, allows NLP models to share information from multiple annotations and may
facilitate better predictions when the tasks are inter-related. This technique,
however, requires annotating the same text with multiple annotation schemes
which may be costly and laborious. Active learning (AL) has been demonstrated
to optimize annotation processes by iteratively selecting unlabeled examples
whose annotation is most valuable for the NLP model. Yet, multi-task active
learning (MT-AL) has not been applied to state-of-the-art pre-trained
Transformer-based NLP models. This paper aims to close this gap. We explore
various multi-task selection criteria in three realistic multi-task scenarios,
reflecting different relations between the participating tasks, and demonstrate
the effectiveness of multi-task compared to single-task selection. Our results
suggest that MT-AL can be effectively used in order to minimize annotation
efforts for multi-task NLP models.Comment: Accepted for publication in Transactions of the Association for
Computational Linguistics (TACL), 2022. Pre-MIT Press publication versio
Relational Multi-Task Learning: Modeling Relations between Data and Tasks
A key assumption in multi-task learning is that at the inference time the
multi-task model only has access to a given data point but not to the data
point's labels from other tasks. This presents an opportunity to extend
multi-task learning to utilize data point's labels from other auxiliary tasks,
and this way improves performance on the new task. Here we introduce a novel
relational multi-task learning setting where we leverage data point labels from
auxiliary tasks to make more accurate predictions on the new task. We develop
MetaLink, where our key innovation is to build a knowledge graph that connects
data points and tasks and thus allows us to leverage labels from auxiliary
tasks. The knowledge graph consists of two types of nodes: (1) data nodes,
where node features are data embeddings computed by the neural network, and (2)
task nodes, with the last layer's weights for each task as node features. The
edges in this knowledge graph capture data-task relationships, and the edge
label captures the label of a data point on a particular task. Under MetaLink,
we reformulate the new task as a link label prediction problem between a data
node and a task node. The MetaLink framework provides flexibility to model
knowledge transfer from auxiliary task labels to the task of interest. We
evaluate MetaLink on 6 benchmark datasets in both biochemical and vision
domains. Experiments demonstrate that MetaLink can successfully utilize the
relations among different tasks, outperforming the state-of-the-art methods
under the proposed relational multi-task learning setting, with up to 27%
improvement in ROC AUC.Comment: ICLR 2022 Spotligh
Multi-task Learning by Leveraging the Semantic Information
One crucial objective of multi-task learning is to align distributions across
tasks so that the information between them can be transferred and shared.
However, existing approaches only focused on matching the marginal feature
distribution while ignoring the semantic information, which may hinder the
learning performance. To address this issue, we propose to leverage the label
information in multi-task learning by exploring the semantic conditional
relations among tasks. We first theoretically analyze the generalization bound
of multi-task learning based on the notion of Jensen-Shannon divergence, which
provides new insights into the value of label information in multi-task
learning. Our analysis also leads to a concrete algorithm that jointly matches
the semantic distribution and controls label distribution divergence. To
confirm the effectiveness of the proposed method, we first compare the
algorithm with several baselines on some benchmarks and then test the
algorithms under label space shift conditions. Empirical results demonstrate
that the proposed method could outperform most baselines and achieve
state-of-the-art performance, particularly showing the benefits under the label
shift conditions
Facial Expression Analysis via Transfer Learning
Automated analysis of facial expressions has remained an interesting and challenging research topic in the field of computer vision and pattern recognition due to vast applications such as human-machine interface design, social robotics, and developmental psychology. This dissertation focuses on developing and applying transfer learning algorithms - multiple kernel learning (MKL) and multi-task learning (MTL) - to resolve the problems of facial feature fusion and the exploitation of multiple facial action units (AUs) relations in designing robust facial expression recognition systems. MKL algorithms are employed to fuse multiple facial features with different kernel functions and tackle the domain adaption problem at the kernel level within support vector machines (SVM). lp-norm is adopted to enforce both sparse and nonsparse kernel combination in our methods. We further develop and apply MTL algorithms for simultaneous detection of multiple related AUs by exploiting their inter-relationships. Three variants of task structure models are designed and investigated to obtain fine depiction of AU relations. lp-norm MTMKL and TD-MTMKL (Task-Dependent MTMKL) are group-sensitive MTL methodsthat model the co-occurrence relations among AUs. On the other hand, our proposed hierarchical multi-task structural learning (HMTSL) includes a latent layer to learn a hierarchical structure to exploit all possible AU interrelations for AU detection. Extensive experiments on public face databases show that our proposed transfer learning methods have produced encouraging results compared to several state-of-the-art methods for facial expression recognition and AU detection
Learning Task Relatedness in Multi-Task Learning for Images in Context
Multimedia applications often require concurrent solutions to multiple tasks.
These tasks hold clues to each-others solutions, however as these relations can
be complex this remains a rarely utilized property. When task relations are
explicitly defined based on domain knowledge multi-task learning (MTL) offers
such concurrent solutions, while exploiting relatedness between multiple tasks
performed over the same dataset. In most cases however, this relatedness is not
explicitly defined and the domain expert knowledge that defines it is not
available. To address this issue, we introduce Selective Sharing, a method that
learns the inter-task relatedness from secondary latent features while the
model trains. Using this insight, we can automatically group tasks and allow
them to share knowledge in a mutually beneficial way. We support our method
with experiments on 5 datasets in classification, regression, and ranking tasks
and compare to strong baselines and state-of-the-art approaches showing a
consistent improvement in terms of accuracy and parameter counts. In addition,
we perform an activation region analysis showing how Selective Sharing affects
the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster
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