Continual learning (CL) aims to enable information systems to learn from a continuous data
stream across time. However, it is difficult for existing deep learning architectures to learn a …

Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Active learning strives to reduce annotation costs by choosing the most critical examples to
label. Typically, the active learning strategy is contingent on the classification model. For …

We consider the problem of making efficient use of heterogeneous training data in neural
machine translation (NMT). Specifically, given a training dataset with a sentence-level …

Graph neural networks (GNNs) have been attracting increasing popularity due to their
simplicity and effectiveness in a variety of fields. However, a large number of labeled data is …

Construction of human-curated annotated datasets for abstractive text summarization (ATS)
is very time-consuming and expensive because creating each instance requires a human …

Active learning is an efficient approach for mitigating data dependency when training neural
machine translation (NMT) models. In this paper, we explore new training frameworks by …

The competitive performance of neural machine translation (NMT) critically relies on large
amounts of training data. However, acquiring high-quality translation pairs requires expert …

Perceiving users' engagement accurately is important for technologies that need to respond
to learners in a natural and intelligent way. In this paper, we address the problem of …