Machine Translation

In recent years, neural network-based machine translation (MT) approaches have steadily superseded the statistical MT (SMT) methods, and represents the current state-of-the-art in MT research. Neural MT (NMT) is a data-driven end-to-end learning ...

This paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are ...

Bilingual word embeddings (BWEs) play a very important role in many natural language processing (NLP) tasks, especially cross-lingual tasks such as machine translation (MT) and cross-language information retrieval. Most existing methods to train ...

An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of the back-translations of the target-side monolingual data. The standard back-translation method has ...

Learning bilingual word embeddings can be much easier if the parallel corpora are available with their words well aligned explicitly. However, in most cases, the parallel corpora only provide a set of pairs that are semantically equivalent to each ...

Self-attention-based encoder-decoder frameworks have drawn increasing attention in recent years. The self-attention mechanism generates contextual representations by attending to all tokens in the sentence. Despite improvements in performance, ...$$

Non-autoregressive machine translation aims to speed up the decoding procedure by discarding the autoregressive model and generating the target words independently. Because non-autoregressive machine translation fails to exploit target-side ...$$

We investigated multiple pivot approaches for the Japanese and Indonesian (Ja–Id) language pair in phrase-based statistical machine translation (SMT). We used four languages as pivots: viz., English, Malaysian, Filipino, and the Myanmar language. ...

The pervasive attention model is a sequence-to-sequence model that addresses the issue of source–target interaction in encoder–decoder models by jointly encoding the two sequences with a two-dimensional convolutional neural network. We investigate ...$$$$$$$$

Neural machine translation (NMT) has emerged as a preferred alternative to the previous mainstream statistical machine translation (SMT) approaches largely due to its ability to produce better translations. The NMT training is often characterized ...

Parallel corpora are central to translation studies and contrastive linguistics. However, training machine translation (MT) systems by barely using the semantic aspects of a parallel corpus leads to unsatisfactory results, as then the trained MT ...

Unsupervised Neural Machine Translation (UNMT) approaches have gained widespread popularity in recent times. Though these approaches show impressive translation performance using only monolingual corpora of the languages involved, these approaches ...