An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification

End-to-end neural machine translation has overtaken statistical machine translation in terms of translation quality for some language pairs, specially those with large amounts of parallel data. Besides this palpable improvement, neural networks provide several new properties. A single system can be trained to translate between many languages at almost no additional cost other than training time. Furthermore, internal representations learned by the network serve as a new semantic representation of words -or sentences- which, unlike standard word embeddings, are learned in an essentially bilingual or even multilingual context. In view of these properties, the contribution of the present work is two-fold. First, we systematically study the NMT context vectors, i.e. output of the encoder, and their power as an interlingua representation of a sentence. We assess their quality and effectiveness by measuring similarities across translations, as well as semantically related and semantically unrelated sentence pairs. Second, as extrinsic evaluation of the first point, we identify parallel sentences in comparable corpora, obtaining an F1=98.2% on data from a shared task when using only NMT context vectors. Using context vectors jointly with similarity measures F1 reaches 98.9%.Comment: 11 pages, 4 figure

An Intelligent Framework for Natural Language Stems Processing

This work describes an intelligent framework that enables the derivation of stems from inflected words. Word stemming is one of the most important factors affecting the performance of many language applications including parsing, syntactic analysis, speech recognition, retrieval systems, medical systems, tutoring systems, biological systems,…, and translation systems. Computational stemming is essential for dealing with some natural language processing such as Arabic Language, since Arabic is a highly inflected language. Computational stemming is an urgent necessity for dealing with Arabic natural language processing. The framework is based on logic programming that creates a program to enabling the computer to reason logically. This framework provides information on semantics of words and resolves ambiguity. It determines the position of each addition or bound morpheme and identifies whether the inflected word is a subject, object, or something else. Position identification (expression) is vital for enhancing understandability mechanisms. The proposed framework adapts bi-directional approaches. It can deduce morphemes from inflected words or it can build inflected words from stems. The proposed framework handles multi-word expressions and identification of names. The framework is based on definiteclause grammar where rules are built according to Arabic patterns (templates) using programming language prolog as predicates in first-order logic. This framework is based on using predicates in firstorder logic with object-oriented programming convention which can address problems of complexity. This complexity of natural language processing comes from the huge amount of storage required. This storage reduces the efficiency of the software system. In order to deal with this complexity, the research uses Prolog as it is based on efficient and simple proof routines. It has dynamic memory allocation of automatic garbage collection. This facility, in addition to relieve th