Machine translation has many applications such as news translation, email translation, official letter translation etc. Commercial translators, e.g. Google Translation lags in regional vocabulary and are unable to learn the bilingual text in the source and target languages within the input. In this paper, a regional vocabulary-based application-oriented Neural Machine Translation (NMT) model is proposed over the data set of emails used at the University for communication over a period of three years. A state-of-the-art Sequence-to-Sequence Neural Network for ML -> EN and EN -> ML translations is compared with Google Translate using Gated Recurrent Unit Recurrent Neural Network machine translation model with attention decoder. The low BLEU score of Google Translation in comparison to our model indicates that the application based regional models are better. The low BLEU score of EN -> ML of our model and Google Translation indicates that the Malay Language has complex language features corresponding to English.

Natural language processing, or NLP, is a type of artificial intelligence (AI) that specializes in analyzing human language. Have you ever used Apple's Siri and wondered how it understands (most of) what you're saying? This is an example of NLP in practice. NLP is becoming an essential part of our lives, and together with machine learning and deep learning, produces results that are far superior to what could be achieved just a few years ago. In this article we'll take a closer look at NLP, see how it's applied and learn how it works.

Machine translation is an automated way to translate text or speech from one language to another. It can take volumes of data and provide translations into a large number of supported languages. Although not intended to fully replace human translators, it can provide value when immediate translations are needed for a wide variety of languages. If you're looking to translate content on the web, you have several options. Many popular browsers offer translation capabilities, which are either built in (e.g.

Grammatical tense and mood are important linguistic phenomena to consider in natural language processing (NLP) research. We consider the correspondence between English and German tense and mood in translation. Human translators do not find this correspondence easy, and as we will show through careful analysis, there are no simplistic ways to map tense and mood from one language to another. Our observations about the challenges of human translation of tense and mood have important implications for multilingual NLP. Of particular importance is the challenge of modeling tense and mood in rule-based, phrase-based statistical and neural machine translation.

In this work, we present TGLS, a novel framework to unsupervised Text Generation by Learning from Search. We start by applying a strong search algorithm (in particular, simulated annealing) towards a heuristically defined objective that (roughly) estimates the quality of sentences. Then, a conditional generative model learns from the search results, and meanwhile smooth out the noise of search. The alternation between search and learning can be repeated for performance bootstrapping. We demonstrate the effectiveness of TGLS on two real-world natural language generation tasks, paraphrase generation and text formalization. Our model significantly outperforms unsupervised baseline methods in both tasks. Especially, it achieves comparable performance with the state-of-the-art supervised methods in paraphrase generation.

Neural network models using predictive coding are interesting from the viewpoint of computational modelling of human language acquisition, where the objective is to understand how linguistic units could be learned from speech without any labels. Even though several promising predictive coding -based learning algorithms have been proposed in the literature, it is currently unclear how well they generalise to different languages and training dataset sizes. In addition, despite that such models have shown to be effective phonemic feature learners, it is unclear whether minimisation of the predictive loss functions of these models also leads to optimal phoneme-like representations. The present study investigates the behaviour of two predictive coding models, Autoregressive Predictive Coding and Contrastive Predictive Coding, in a phoneme discrimination task (ABX task) for two languages with different dataset sizes. Our experiments show a strong correlation between the autoregressive loss and the phoneme discrimination scores with the two datasets. However, to our surprise, the CPC model shows rapid convergence already after one pass over the training data, and, on average, its representations outperform those of APC on both languages.

The 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), the premiere annual conference on AI and language, takes place July 5-10. As is the case with most events currently, ACL will be virtual this year due to COVID-19. At IBM Research AI, we’re excited to share with you — wherever you might be in the world — all the work we’ll have at ACL 2020 designed to advance AI for the enterprise. The ability of AI to master language has been one of IBM Research AI’s key areas of focus for years. The field of Natural Language Processing (NLP) is constantly evolving in efforts to better outfit AI with the ability to communicate similarly to how us humans can. It’s an incredibly challenging area of research. An AI must identify, decipher, and navigate through natural language barriers — tasks like slang, idioms, acronyms, different languages and extracting meaning from multi-format documents, to name a few. To tackle these challenges, IBM released earlier this year a new, four-part mastering language taxonomy…

To better understand the landscape of available tools for machine learning production, I decided to look up every AI/ML tool I could find. After filtering out applications companies (e.g. companies that use ML to provide business analytics), tools that aren't being actively developed, and tools that nobody uses, I got 202 tools. Please let me know if there are tools you think I should include but aren't on the list yet! The landscape is under-developed IV. I categorize the tools based on which step of the workflow that it supports. I don't include Project setup since it requires project management tools, not ML tools.

The Commonwealth Bank of Australia spent around $750 million and 5 years of work to convert its platform from COBOL to Java. Migrating an existing codebase to a modern or more efficient language like Java or C requires expertise in both the source and target languages, and is often costly. Usually, a transcompiler is deployed that converts source code from a high-level programming language (such as C or Python) to another. Transcompilers are primarily used for interoperability, and to port codebases written in an obsolete or deprecated language (e.g. They typically rely on handcrafted rewrite rules, applied to the source code abstract syntax tree.

The study of question answering has received increasing attention in recent years. This work focuses on providing an answer that compatible with both user intent and conditioning information corresponding to the question, such as delivery status and stock information in e-commerce. However, these conditions may be wrong or incomplete in real-world applications. Although existing question answering systems have considered the external information, such as categorical attributes and triples in knowledge base, they all assume that the external information is correct and complete. To alleviate the effect of defective condition values, this paper proposes condition aware and revise Transformer (CAR-Transformer). CAR-Transformer (1) revises each condition value based on the whole conversation and original conditions values, and (2) it encodes the revised conditions and utilizes the conditions embedding to select an answer. Experimental results on a real-world customer service dataset demonstrate that the CAR-Transformer can still select an appropriate reply when conditions corresponding to the question exist wrong or missing values, and substantially outperforms baseline models on automatic and human evaluations. The proposed CAR-Transformer can be extended to other NLP tasks which need to consider conditioning information.