November 15, 2018 Neural MT, NLP Leave a comment

Issue #17 – Speeding up Neural MT

15 Nov18 Issue #17 – Speeding up Neural MT Author: Raj Nath Patel, Machine Translation Scientist @ Iconic For all the benefits Neural MT has brought in terms of translation quality, producing output quickly and efficiently is still a challenge for developers. All things being equal, Neural MT is slower than its statistical counterpart. This is particularly the case when running translation on standard processors (CPUs) as opposed to faster, more powerful (but also more expensive) graphics processors (GPUs), which is […]

November 8, 2018 Neural MT, NLP Leave a comment

Issue #16 – Revisiting synthetic training data for Neural MT

08 Nov18 Issue #16 – Revisiting synthetic training data for Neural MT Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic In a previous guest post in this series, Prof. Andy Way explained how to create training data for Neural MT through back-translation. This technique involves translating monolingual data in the target language into the source language to obtain a parallel corpus of “synthetic” source and “authentic” target data – so called back-translation. Andy reported interesting findings whereby, with a few million […]

November 1, 2018 cache-based memory network, NLP Leave a comment

Issue #15 – Document-Level Neural MT

01 Nov18 Issue #15 – Document-Level Neural MT Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic In this week’s post, we take a look at document-level neural machine translation. Most, if not all existing approaches to machine translation operate on the sentence level. That is to say, when translating a document, it is actually split up into individual sentences or segments, and they are processed independently of each other. With document-level Neural MT, as the name suggests, we are going beyond […]

October 25, 2018 Neural MT, NLP Leave a comment

Issue #14 – Neural MT: A Case Study

25 Oct18 Issue #14 – Neural MT: A Case Study Author: Dr. John Tinsley, CEO @ Iconic As a machine translation provider, one of the questions we’ve been asking ourselves most frequently over the last 18 months has been, “When should we switch an existing production deployment to Neural MT?”. While all new projects are built using Neural MT, there is a certain element of – “if it ain’t broke, don’t fix it” – that can creep in when it comes to […]

October 11, 2018 Contrastive Evaluation of Machine Translation, NLP Leave a comment

Issue #13 – Evaluation of Neural MT Architectures

11 Oct18 Issue #13 – Evaluation of Neural MT Architectures Author: Raj Nath Patel, Machine Translation Scientist @ Iconic What are the different approaches to Neural MT? Since its relatively recent advent, the underlying technology has been based on one of three main architectures: Recurrent Neural Networks (RNN) Convolutional Neural Networks (CNN) Self-Attention Networks (Transformer) For various language pairs, non-recurrent architectures (CNN and Transformer) have outperformed RNNs but there has not been any solid explanations as to why. In this post, we’ll evaluate […]

October 4, 2018 character-based Neural MT, NLP Leave a comment

Issue #12 – Character-based Neural MT

04 Oct18 Issue #12 – Character-based Neural MT Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic Most flavours of Machine Translation naturally use the word as the basis for learning models. Early work on Neural MT that followed this approach had to limit the vocabulary scope for practical reasons.. This created problems when dealing with out-of-vocabulary words. One approach that was explored to solve this problem was character-based Neural MT. With the emergence of subword approaches, which almost solves the […]

September 27, 2018 Back Translation, NLP Leave a comment

Issue #11 – Unsupervised Neural MT

27 Sep18 Issue #11 – Unsupervised Neural MT Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic In this week’s article, we will explore unsupervised machine translation. In other words, training a machine translation engine without using any parallel data! As you might imagine, the potential implications of not needing any data to train a Neural MT engine could be huge. In general, most of the approaches in this direction still use some bilingual signal, for example using parallel data […]

September 20, 2018 dissemination, NLP Leave a comment

Issue #10 – Evaluating Neural MT post-editing

20 Sep18 Issue #10 – Evaluating Neural MT post-editing Author: Dr. Joss Moorkens, Assistant Professor, Dublin City University This week, we have a guest post from Prof. Joss Moorkens of Dublin City University. Joss is renowned for his work in the area of translation technology and, particularly, the evaluation of MT output for certain use cases. Building on the “human parity” topic from Issue #8 of this series, Joss describes his recent work on evaluation of Neural MT post-editing for […]

September 13, 2018 Domain Adaptation for Neural MT, NLP Leave a comment

Issue #9 – Domain Adaptation for Neural MT

13 Sep18 Issue #9 – Domain Adaptation for Neural MT Author: Raj Nath Patel, Machine Translation Scientist @ Iconic While Neural MT has raised the bar in terms of the quality of general purpose machine translation, it is still limited when it comes to more intricate or technical use cases. That is where domain adaptation — the process of developing and adapting MT for specific industries, content types, and use cases — has a big part to play. In this […]

September 5, 2018 human translation, NLP Leave a comment

Issue #8 – Is Neural MT on par with human translation?

05 Sep18 Issue #8 – Is Neural MT on par with human translation? Author: Dr. John Tinsley, CEO @ Iconic The next few articles of the Neural MT Weekly will deal with the topic of quality and evaluation of machine translation. Since the advent of Neural MT, developments have moved fast, and we have seen quality expectation levels rise, in line with a number of striking proclamations about performance. Early claims of “bridging the gap between human and machine translation” […]

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