Interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in the sub-fields of AI such as machine learning, computer vision, and natural language processing. Much of the growth in these fields has been made possible with deep learning, a sub-area of machine learning that uses artificial neural networks. This has created significant interest in the integration of vision and language. In this survey, we focus on ten prominent tasks that integrate language and vision by discussing their problem formulation, methods, existing datasets, evaluation measures, and compare the results obtained with corresponding state-of-the-art methods. Our efforts go beyond earlier surveys which are either task-specific or concentrate only on one type of visual content, i.e., image or video. Furthermore, we also provide some potential future directions in this field of research with an anticipation that this survey stimulates innovative thoughts and ideas to address the existing challenges and build new applications.

What is Amazon Web Services? Amazon Web Services or AWS is world's broadly adopted cloud platform . AWS provides with a number of useful cloud computing services that are very much reliable, scalable and cost efficient as they say. AWS provides services like storage, networking, remote computing, servers, email, mobile development and security . So now coming to Amazon machine learning, frankly means leveraging ML algorithms on cloud platforms like AWS .

The Vauquois triangle is a classical hierarchical model for visualizing various machine translation approaches. Before we dive into the Vauquois triangle, let's look at what Machine Translation is. Machine translation is the process of using computer software to translate a text or speech in one natural language to another. The definition may look simple, but the process is extremely difficult. Languages differ in so many ways, grammatically, syntactically (sentence structure), semantically (meanings), etc.

Day by day the number of machine learning models is increasing at a pace. With this increasing rate, it is hard for beginners to choose an effective model to perform Natural Language Understanding (NLU) and Natural Language Generation (NLG) mechanisms. Researchers across the globe are working around the clock to achieve more progress in artificial intelligence to build agile and intuitive sequence-to-sequence learning models. And in recent times transformers are one such model which gained more prominence in the field of machine learning to perform speech-to-text activities. The wide availability of other sequence-to-sequence learning models like RNNs, LSTMs, and GRU always raises a challenge for beginners when they think about transformers.

In this paper we present our open-source neural machine translation (NMT) toolkit called "Yet Another Neural Machine Translation Toolkit" abbreviated as YANMTT which is built on top of the Transformers library. Despite the growing importance of sequence to sequence pre-training there surprisingly few, if not none, well established toolkits that allow users to easily do pre-training. Toolkits such as Fairseq which do allow pre-training, have very large codebases and thus they are not beginner friendly. With regards to transfer learning via fine-tuning most toolkits do not explicitly allow the user to have control over what parts of the pre-trained models can be transferred. YANMTT aims to address these issues via the minimum amount of code to pre-train large scale NMT models, selectively transfer pre-trained parameters and fine-tune them, perform translation as well as extract representations and attentions for visualization and analyses. Apart from these core features our toolkit also provides other advanced functionalities such as but not limited to document/multi-source NMT, simultaneous NMT and model compression via distillation which we believe are relevant to the purpose behind our toolkit.

Human evaluation has always been expensive while researchers struggle to trust the automatic metrics. To address this, we propose to customise traditional metrics by taking advantages of the pre-trained language models (PLMs) and the limited available human labelled scores. We first re-introduce the hLEPOR metric factors, followed by the Python portable version we developed which achieved the automatic tuning of the weighting parameters in hLEPOR metric. Then we present the customised hLEPOR (cushLEPOR) which uses LABSE distilled knowledge model to improve the metric agreement with human judgements by automatically optimised factor weights regarding the exact MT language pairs that cushLEPOR is deployed to. We also optimise cushLEPOR towards human evaluation data based on MQM and pSQM framework on English-German and Chinese-English language pairs. The experimental investigations show cushLEPOR boosts hLEPOR performances towards better agreements to PLMs like LABSE with much lower cost, and better agreements to human evaluations including MQM and pSQM scores, and yields much better performances than BLEU (data available at \url{https://github.com/poethan/cushLEPOR}).

Machine learning based compilation has been explored for over a decade [1]. Early work focused on learning profitability heuristics while more recently, deep learning models have been used to build code-to-code models, for translating or decompiling code. However, to the best of our knowledge, there has been no prior work on using machine learning to entirely automate compilation i.e given a high level source code program generate the equivalent assembler code. In this paper, we investigate whether it is possible to learn an end-to-end machine compiler using neural machine translation. In particular, we focus on the translation of small C functions to x86 assembler We use an existing function-level C corpus, Anghabench [2], to build a parallel C-x86 assembler corpus.

NMT is a popular and widely used translation service that incorporates an end-to-end approach for automatic translation which overcomes the weaknesses of RBMT and SMT methods. NMT uses the most recent deep learning methods to produce better translation output than other traditional Machine Translation solutions. It is the most recent type of machine translation that employs a neural network that is closely related to the neurons of the human brain, allowing it to categorize data into various groups and layers. NMT is a language translation approach that tries to incorporate the context of the sentences or paragraphs rather than individual words. The NMT system is made up of current multilingual databases and automated learning mechanisms that contribute to continuous improvement.

Advancements in natural language processing (NLP) enable computers to understand what humans say and help people communicate through tools like machine translation, voice-controlled assistants and chatbots.

Etsy is seeking a Senior Machine Learning Engineer to join our International Search engineering organization in the International Customer Experience initiative. We believe that code is craft and the work we do is part of a larger creative culture connecting 81M buyers and over 4M sellers to millions of items in the diverse marketplace. We believe that small, empowered, self-motivated teams can do big, impactful things. Do you have a passion for natural language processing, machine learning and working in a multi-functional engineering team? If so, this is the role for you!