The translation quality of machine translation systems depends on the parallel corpus used
for training, particularly on the quantity and quality of the corpus. However, building a high …

Back-translation is a critical component of Unsupervised Neural Machine Translation
(UNMT), which generates pseudo parallel data from target monolingual data. A UNMT …

Conventional knowledge distillation (KD) approaches are commonly employed to compress
neural machine translation (NMT) models. However, they only obtain one lightweight …

Numerous recent work on unsupervised machine translation (UMT) implies that competent
unsupervised translations of low-resource and unrelated languages, such as Nepali or …

Unsupervised text-graph alignment (UTGA) is a fundamental task that bidirectionally
generates texts and graphs without parallel data. Most available models of UTGA suffer from …

Conserving biodiversity has become more important for tropical countries, where agricultural
production is featured by a large number of small farms scattered in wide areas conducting …

Automatic source code summarization system aims to generate a valuable natural language
description for a program, which can facilitate software development and maintenance, code …

Neural Machine Translation (NMT) for low-resource languages is still a challenging task in
front of NLP researchers. In this work, we deploy a standard data augmentation …

Attention networks often make decisions relying solely on a few pieces of tokens, even if
those reliances are not truly indicative of the underlying meaning or intention of the full …

The success of Neural Machine Translation (NMT) largely depends on the availability of
large bitext training corpora. Due to the lack of such large corpora in low-resource language …