Multi-task learning (MTL) has been widely applied in Natural Language Processing. A major
task and its associated auxiliary tasks share the same encoder; hence, an MTL encoder can …

Most existing deep multi-task learning models are based on parameter sharing, such as
hard sharing, hierarchical sharing, and soft sharing. How choosing a suitable sharing …

Deep transfer learning has become increasingly prevalent in various fields such as industry
and medical science in recent years. To ensure the successful implementation of target …

Multitask learning has shown promising performance in learning multiple related tasks
simultaneously, and variants of model architectures have been proposed, especially for …

One of the strongest indicators of a mental health crisis is how people interact with each
other or express them-selves. Hence, social media is an ideal source to extract user-level …

The idea of using multi-task learning approaches to address the joint extraction of entity and
relation is motivated by the relatedness between the entity recognition task and the relation …

The problem of learning and forecasting underlying trends in time series data arises in a
variety of applications, such as traffic management, energy optimization, etc. In literature, a …

In this paper, we focus on multi-task classification, where related classification tasks share
the same label space and are learned simultaneously. In particular, we tackle a new setting …

Graph Neural Networks (GNNs) are a framework for graph representation learning, where a
model learns to generate low dimensional node embeddings that encapsulate structural and …

The sewerage infrastructure is one of the most important and expensive infrastructures in
modern society. In order to efficiently manage the sewerage infrastructure, automated sewer …