632 research outputs found
Arabic machine transliteration using an attention-based encoder-decoder model
Transliteration is the process of converting words from a given source language alphabet to a target language alphabet, in a way
that best preserves the phonetic and orthographic aspects of the transliterated words. Even though an important effort has been
made towards improving this process for many languages such as English, French and Chinese, little research work has been
accomplished with regard to the Arabic language. In this work, an attention-based encoder-decoder system is proposed for the
task of Machine Transliteration between the Arabic and English languages. Our experiments proved the efficiency of our proposal
approach in comparison to some previous research developed in this area
Arabic machine transliteration using an attention-based encoder-decoder model
Transliteration is the process of converting words from a given source language alphabet to a target language alphabet, in a way
that best preserves the phonetic and orthographic aspects of the transliterated words. Even though an important effort has been
made towards improving this process for many languages such as English, French and Chinese, little research work has been
accomplished with regard to the Arabic language. In this work, an attention-based encoder-decoder system is proposed for the
task of Machine Transliteration between the Arabic and English languages. Our experiments proved the efficiency of our proposal
approach in comparison to some previous research developed in this area
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation
Interlingua based Machine Translation (MT) aims to encode multiple languages
into a common linguistic representation and then decode sentences in multiple
target languages from this representation. In this work we explore this idea in
the context of neural encoder decoder architectures, albeit on a smaller scale
and without MT as the end goal. Specifically, we consider the case of three
languages or modalities X, Z and Y wherein we are interested in generating
sequences in Y starting from information available in X. However, there is no
parallel training data available between X and Y but, training data is
available between X & Z and Z & Y (as is often the case in many real world
applications). Z thus acts as a pivot/bridge. An obvious solution, which is
perhaps less elegant but works very well in practice is to train a two stage
model which first converts from X to Z and then from Z to Y. Instead we explore
an interlingua inspired solution which jointly learns to do the following (i)
encode X and Z to a common representation and (ii) decode Y from this common
representation. We evaluate our model on two tasks: (i) bridge transliteration
and (ii) bridge captioning. We report promising results in both these
applications and believe that this is a right step towards truly interlingua
inspired encoder decoder architectures.Comment: 10 page
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