After more than 70 years of evolution, great achievements have been made in machine
translation. Especially in recent years, translation quality has been greatly improved with the …

Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

Many NLP applications can be framed as a graph-to-sequence learning problem. Previous
work proposing neural architectures on this setting obtained promising results compared to …

Most of the existing Neural Machine Translation (NMT) models focus on the conversion of
sequential data and do not directly use syntactic information. We propose a novel end-to …

In this paper, we propose an ordinal hyperplane ranking algorithm called OHRank, which
estimates human ages via facial images. The design of the algorithm is based on the relative …

Most neural machine translation (NMT) models are based on the sequential encoder-
decoder framework, which makes no use of syntactic information. In this paper, we improve …

Even though a linguistics-free sequence to sequence model in neural machine translation
(NMT) has certain capability of implicitly learning syntactic information of source sentences …

We describe a span-level supervised attention loss that improves compositional
generalization in semantic parsers. Our approach builds on existing losses that encourage …

Efficient decoding has been a fundamental problem in machine translation, especially with
an integrated language model which is essential for achieving good translation quality. We …

Syntactic reordering approaches are an effective method for handling word-order
differences between source and target languages in statistical machine translation (SMT) …