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Articles

  1. State-of-the-art results at record speed
  2. Machine Translation
  3. Natural Language Processing Summary
  4. Introduction to Natural Language Processing (NLP): What is NLP?
  5. Machine Translation over fifty years - Persée

Since the early s, a new artificial intelligence technology, deep neural networks aka deep learning , has allowed the technology of speech recognition to reach a quality level that allowed the Microsoft Translator team to combine speech recognition with its core text translation technology to launch a new speech translation technology. SMT uses advanced statistical analysis to estimate the best possible translations for a word given the context of a few words.

SMT has been used since the mids by all major translation service providers, including Microsoft. The advent of Neural Machine Translation NMT caused a radical shift in translation technology, resulting in much higher quality translations. It is incorporated across product localization, support, and online communication teams e. Microsoft Translator can be used in web or client applications on any hardware platform and with any operating system to perform language translation and other language-related operations such as language detection, text to speech, or dictionary.

Leveraging industry standard REST technology, the developer sends source text or audio for speech translation to the service with a parameter indicating the target language, and the service sends back the translated text for the client or web app to use. The Microsoft Translator service is an Azure service hosted in Microsoft data centers and benefits from the security, scalability, reliability, and nonstop availability that other Microsoft cloud services also receive.

Microsoft Translator speech translation technology was launched late starting with Skype Translator, and is available as an open API for customers since early Speech translation is now available through Microsoft Speech, an end-to-end set of fully customizable services for speech recognition, speech translation, and speech synthesis text-to-speech.

State-of-the-art results at record speed

Rather than writing hand-crafted rules to translate between languages, modern translation systems approach translation as a problem of learning the transformation of text between languages from existing human translations and leveraging recent advances in applied statistics and machine learning. Statistical modeling techniques and efficient algorithms help the computer address the problem of decipherment detecting the correspondences between source and target language in the training data and decoding finding the best translation of a new input sentence.

Microsoft Translator unites the power of statistical methods with linguistic information to produce models that generalize better and lead to more comprehensible translations. Because of this approach, which does not rely on dictionaries or grammatical rules, it provides the best translations of phrases where it can use the context around a given word versus trying to perform single word translations. Continuous improvements to translation are important. However, performance improvements have plateaued with SMT technology since the mids. Neural network translations fundamentally differ in how they are performed compared to the traditional SMT ones.

Machine Translation

The following animation depicts the various steps neural network translations go through to translate a sentence. Because of this approach, the translation will take into context the full sentence, versus only a few words sliding window that SMT technology uses and will produce more fluid and human-translated looking translations. Based on the neural-network training, each word is coded along a dimensions vector a representing its unique characteristics within a particular language pair e.

English and Chinese. Based on the language pairs used for training, the neural network will self-define what these dimensions should be. They could encode simple concepts like gender feminine, masculine, neutral , politeness level slang, casual, written, formal, etc.


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  • Natural Language Processing!

In can achieve this because the system learned that English and French invert the order of these words in sentences. Thanks to this approach, the final output is, in most cases, more fluent and closer to a human translation than an SMT-based translation could have ever been. Microsoft Translator is also capable of translating speech.

Natural Language Processing Summary

This model is trained on human-to-human interactions rather than human-to-machine commands, producing speech recognition that is optimized for normal conversations. It introduces important topics in text processing that are relevant in translation of languages that are not based on the Latin script.


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Text format preservation in translation is discussed in this section, especially for HTML documents. Here the author lists a variety of features and tools that are suitable for TWB however he did not discuss their availability and realistic use, especially for non Latin-based languages. Moreover the author did not give enough information about workflow in translation. A section on Translation Memory TM includes an interesting discussion also useful for information extraction about similarity measures between sentences to be translated and sentences stored in the TM.

Finally bilingual and subsentential alignment is discussed and substantial methods for sentence, word and terminology alignment are presented. Part 3, entitled "Machine Translation", is the major part and contains four chapters. The first one presents standard computational linguistics techniques for the analysis and the generation of natural language sentences, with an outline for their implementation.

The three other chapters present the main MT techniques.

Chapter 5, entitled "Computational linguistics techniques", includes a first section on computational morphology and the two-level model. A second section on syntactic analysis gives pointers to some approaches and presents short descriptions of syntax aspects: sign structure, agreement, complement structures, unbounded dependency constructions, relative clauses, etc. A section on parsing presents a chart-parsing algorithm to derive syntactic and semantic analyses according to a grammar. And a last section on generation presents general aspects of natural language generation NLG and a short description of the Semantic Head-Driven generation approach used in MT.

Chapter 6, entitled "Transfer machine translation", presents classic translation problems and the way they are handled in transfer-based MT systems. This chapter describes three different types of transfer systems. A first section describes the syntactic transfer MT and includes a fine classification of translation divergence argued with numerous examples from English and Spanish.

A second section describes the semantic transfer MT based on the QLF semantic representation Quasi-Logical Form , which is a representation of language meaning based on predicate logic. A third section describes the lexicalist MT by highlighting its non-recursive character compared to the syntactic and semantic transfer methodologies. This chapter ends with a comparative discussion about these three major MT transfer strategies. Chapter 7, entitled "Interlingua machine translation", describes two approaches to multilingual MT that are based on a language-neutral representation of sentence meaning: interlingua- based MT also known as pivot language-based MT and knowledge-based MT KBMT.

Jackendoff is described in this section. I found that the analysis process into LCS is described in detail while the generation process from LCS is underrepresented.

A knowledge representation framework is presented in which the domain model is described as an ontology. Analysis and generation processes are also described in this section. Finally, translation divergences are briefly discussed for both approaches and a comparative discussion ends this section.

Introduction to Natural Language Processing (NLP): What is NLP?

Moreover, although NLG has been described in section 5. Chapter 8, entitled "Other Approaches to MT", describes four other MT approaches that illustrate alternative solutions to various problems in translation. They rely on large amounts of bilingual corpora to achieve translation. The two other approaches described here are classified as rule-based approaches: Minimal Recursion Semantics MRS and constraint-based approach.

Part 4, entitled "Common issues", is the last part and it discusses two common issues in MT: disambiguation and evaluation.


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Chapter 9, entitled "Disambiguation", discusses disambiguation in the analysis and transfer stages of MT. Categorial part-of-speech , structural, lexical and transfer ambiguities are considered. A first section presents the POS tagging of words together with some tagging models such as the stochastic and the rule-based tagging E. A brief discussion about designing taggers is also introduced. A second section discusses the disambiguation of syntactic analysis. Methods for processing structural ambiguities are presented such as psycholinguistic heuristic preferences and probabilistic context-free grammars PCFGs.

A third section underlines some techniques for word sense disambiguation that are particularly useful for interlingua MT: selectional or sortal restrictions, frames and semantic distance and corpus-based approaches.

Machine Translation over fifty years - Persée

A last section presents some techniques for transfer disambiguation. Chapter 10, entitled "Evaluation", is the last chapter of the book. It discusses strategies for assessing the translation quality of a system as well as its cost-effectiveness. The author presents a variety of concepts and a number of strategies for evaluating translation software.