Machine Translation is a subfield of Multilinguality and Text Generation that deals with developing algorithms and models for automatically translating text or speech from one language to another. It involves analyzing the input text or speech, generating a representation of its meaning in the source language, and then producing a new text or speech in the target language that conveys the same meaning.

Text Generation is a subfield of Natural Language Processing (NLP) which attempts to teach machines to automatically generate texts that are indistinguishable from texts written by humans.

Multilinguality is a subfield of Natural Language Processing (NLP) that refers to the ability of machines to understand, process, and generate natural language text in multiple languages. Multilinguality is concerned with addressing the challenges that arise due to the linguistic and structural variations that exist across different languages. This includes variations in word order, syntax, grammar, or vocabulary.

Structured Data in NLP is a subfield of Multimodality that deals with the processing of structured data such as tables or graphs in conjunction with textual data. It involves the development of algorithms and models that can extract, represent, and use structured data in natural language form.

Multimodality is a subfield of Natural Language Processing (NLP) that refers to the capability of a system or method to process input of different types or “modalities”, such as natural language text, speech, audio, images, video, and programming languages in NLP applications. It involves developing algorithms and models that can process and analyze information from multiple modalities, and integrate them to form a unified representation of the input.

Natural Language Processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

When trained on very large parallel corpora, the phrase table component of a machine translation system grows to consume vast computational resources. In this paper, we introduce a novel pruning criterion that places phrase table pruning on a sound theoretical foundation. Systematic experiments on four language pairs under various data conditions show that our principled approach is superior to existing ad hoc pruning methods.

Publication:

A Systematic Comparison of Phrase Table Pruning Techniques

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