Abstract Automatic Text Summarization (ATS) is an important area in Natural Language
Processing (NLP) with the goal of shortening a long text into a more compact version by …

In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity
and provide state-of-the-art performance in a wide variety of tasks, such as machine …

In recent years, the volume of textual data has rapidly increased, which has generated a
valuable resource for extracting and analysing information. To retrieve useful knowledge …

Recent studies have shown that reinforcement learning (RL) is an effective approach for
improving the performance of neural machine translation (NMT) system. However, due to its …

The encoder-decoder framework has achieved promising progress for many sequence
generation tasks, including machine translation, text summarization, dialog system, image …

Beam search is the go-to method for decoding auto-regressive machine translation models.
While it yields consistent improvements in terms of BLEU, it is only concerned with finding …

Existing approaches to neural machine translation (NMT) generate the target language
sequence token-by-token from left to right. However, this kind of unidirectional decoding …

The advances in the Internet of Things (IoT) and increased availability of the road sensors
allow for fine-grained traffic forecasting, which is of particular importance toward building an …

As a promising area in artificial intelligence, a new learning paradigm, called Small Sample
Learning (SSL), has been attracting prominent research attention in the recent years. In this …