Multilingual Denoising Pre-training, NLP Leave a comment

Issue #88 – Multilingual Denoising Pre-training for Neural Machine Translation

02 Jul20 Issue #88 – Multilingual Denoising Pre-training for Neural Machine Translation Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Introduction Pre-training has been used in many natural language processing (NLP) tasks with significant improvements in performance. In neural machine translation (NMT), pre-training is mostly applied to building blocks of the whole system, e.g. encoder or decoder. In a previous post (#70), we compared several approaches using pre-training with masked language models. In this post, we take a closer […]

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Estimation Metric, NLP Leave a comment

Issue #87 – YiSi – A Unified Semantic MT Quality Evaluation and Estimation Metric

25 Jun20 Issue #87 – YiSi – A Unified Semantic MT Quality Evaluation and Estimation Metric Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction Automatic evaluation is an issue that has long troubled machine translation (MT): how do we evaluate how good the MT output is? Traditionally, BLEU has been the “go to”, as it is simple to use across language pairs. However, it is overly simplistic, evaluating string matches to a single reference translation. More sophisticated metrics […]

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Levenshtein Transformer, NLP Leave a comment

Issue #86 – Neural MT with Levenshtein Transformer

18 Jun20 Issue #86 – Neural MT with Levenshtein Transformer Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction The standard Transformer model is autoregressive, meaning that the prediction of each target word is based on the predictions for the previous words. The output is generated from left to right, with no chance to revise a past decision and without considering future predictions of the words on the right of the current word. In a recent post (#82), […]

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Neural MT, NLP Leave a comment

Issue #85 – Applying Terminology Constraints in Neural MT

11 Jun20 Issue #85 – Applying Terminology Constraints in Neural MT Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Introduction Maintaining consistency of terminology translation in Neural Machine Translation (NMT) is a more challenging task than in Statistical MT (SMT). In this post, we review a method proposed by Dinu et al. (2019) to train NMT to use custom terminology. Translation with Terminology Constraints Applying terminology constraints to translation may appear to be an easy task. It is a […]

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Estimators of Quality, NLP Leave a comment

Issue #84 – Are Neural Machine Translation Systems Good Estimators of Quality?

04 Jun20 Issue #84 – Are Neural Machine Translation Systems Good Estimators of Quality? Author: Prof. Lucia Specia, Professor of Natural Language Processing, Imperial College London (also to ADAPT/Dublin City University and University of Sheffield) This week, we are delighted to have a guest post from Prof. Lucia Specia of Imperial College London, and laterally the University of Sheffield and our own alma mater, Dublin City University. Prof. Specia is one of the world’s preeminent experts on the topic of […]

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Constrained Decoding, NLP Leave a comment

Issue #82 – Constrained Decoding using Levenshtein Transformer

14 May20 Issue #82 – Constrained Decoding using Levenshtein Transformer Author: Raj Patel, Machine Translation Scientist @ Iconic Introduction In constrained decoding, we force in-domain terminology to appear in the final translation. We have previously discussed constrained decoding in earlier blog posts (#7, #9, #79). In this blog post, we will discuss a simple and effective algorithm for incorporating lexical constraints in Neural Machine Translation (NMT) proposed by Susanto et al. (2020) and try to understand how it is better than […]

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Human-Machine Parity, NLP Leave a comment

Issue #81 – Evaluating Human-Machine Parity in Language Translation: part 2

07 May20 Issue #81 – Evaluating Human-Machine Parity in Language Translation: part 2 Author: Dr. Sheila Castilho, Post-Doctoral Researcher @ ADAPT Research Centre This is the second in a 2-part post addressing machine translation quality evaluation – an overarching topic regardless of the underlying algorithms. Following our own summary last week, this week we are delighted to have one of the paper’s authors, Dr. Sheila Castilho, give her take on the paper, their motivations for writing it, and where we […]

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Mixed Multi-Head Self-Attention for Neural MT, NLP Leave a comment

Issue #73 – Mixed Multi-Head Self-Attention for Neural MT

12 Mar20 Issue #73 – Mixed Multi-Head Self-Attention for Neural MT Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic Self-attention is a key component of the Transformer, a state-of-the-art neural machine translation architecture. In the Transformer, self-attention is divided into multiple heads to allow the system to independently attend to information from different representation subspaces. Recently it has been shown that some redundancy occurs in the multiple heads. In this post, we take a look at approaches which ensure […]

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BERT in Neural MT, NLP Leave a comment

Issue #68 – Incorporating BERT in Neural MT

07 Feb20 Issue #68 – Incorporating BERT in Neural MT Author: Raj Patel, Machine Translation Scientist @ Iconic BERT (Bidirectional Encoder Representations from Transformers) has shown impressive results in various Natural Language Processing (NLP) tasks. However, how to effectively apply BERT in Neural MT has not been fully explored. In general, BERT is used as fine-tuning for downstream NLP tasks. For Neural MT, a pre-trained BERT model is used to initialise the encoder in an encoder-decoder architecture. In this post we […]

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Low-Resource Languages, NLP Leave a comment

Issue #66 – Neural Machine Translation Strategies for Low-Resource Languages

23 Jan20 Issue #66 – Neural Machine Translation Strategies for Low-Resource Languages This week we are pleased to welcome the newest member to our scientific team, Dr. Chao-Hong Liu. In this, his first post with us, he’ll give his views on two specific MT strategies, namely, pivot MT and zero-shot MT. While we have covered these topics in previous ‘Neural MT Weekly’ blog posts (Issue #54, Issue #40), these are topics that Chao-Hong has recently worked on prior to joining […]

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