As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

This paper presents the results of the shared tasks from the 8th workshop on Asian
translation (WAT2021). For the WAT2021, 28 teams participated in the shared tasks and 24 …

Large language models (LLMs) demonstrate remarkable machine translation (MT) abilities
via prompting, even though they were not explicitly trained for this task. However, even given …

Abstract Progress with supervised Open Information Extraction (OpenIE) has been primarily
limited to English due to the scarcity of training data in other languages. In this paper, we …

Despite its original goal to jointly learn to align and translate, prior researches suggest that
Transformer captures poor word alignments through its attention mechanism. In this paper …

Neural machine translation (NMT) has obtained significant performance improvement over
the recent years. However, NMT models still face various challenges including fragility and …

Despite significant progress, state-of-the-art abstractive summarization methods are still
prone to hallucinate content inconsistent with the source document. In this paper, we …

Gender inclusivity in language technologies has become a prominent research topic. In this
study, we explore gender-neutral translation (GNT) as a form of gender inclusivity and a goal …