Source side pre-ordering using recurrent neural networks for English-Myanmar machine translation

Word reordering has remained one of the challenging problems for machine translation when translating between language pairs with different word orders e.g. English and Myanmar. Without reordering between these languages, a source sentence may be translated directly with similar word order and translation can not be meaningful. Myanmar is a subject-objectverb (SOV) language and an effective reordering is essential for translation. In this paper, we applied a pre-ordering approach using recurrent neural networks to pre-order words of the source Myanmar sentence into target English’s word order. This neural pre-ordering model is automatically derived from parallel word-aligned data with syntactic and lexical features based on dependency parse trees of the source sentences. This can generate arbitrary permutations that may be non-local on the sentence and can be combined into English-Myanmar machine translation. We exploited the model to reorder English sentences into Myanmar-like word order as a preprocessing stage for machine translation, obtaining improvements quality comparable to baseline rule-based pre-ordering approach on asian language treebank (ALT) corpus

A Study of Burkholderia pseudomallei in the Environment of Farms in Thanlyin and Hmawbi Townships, Myanmar.

Melioidosis is a tropical infection, first described in Myanmar but now rarely diagnosed there, which is widespread in Southeast Asia. The infection is predominantly acquired by people and animals through contact with soil or water. This study aimed to detect the causative organism, Burkholderia pseudomallei, in environmental samples from farms in Thanlyin and Hmawbi townships near Yangon, Myanmar. One hundred and twenty soil samples and 12 water samples were collected and processed using standard microbiological methods. Burkholderia species were isolated from 50 of the 120 (42%) soil samples but none of the water samples. Arabinose assimilation was tested to differentiate between B. pseudomallei and the nonpathogenic Burkholderia thailandensis, and seven of 50 isolates (14%) were negative. These were all confirmed as B. pseudomallei by a species-specific multiplex polymerase chain reaction (PCR). This is the first study to detect environmental B. pseudomallei in Myanmar and confirms that melioidosis is still endemic in the Yangon area

Reordering Model with Recurrent Neural Networks for Myanmar- English Statistical Machine Translation

Word reordering is a problematic issue forlanguage pairs with significantly different word orders,such as the translation between a subject-verb-object(SVO) language and a subject-object-verb (SOV)language. When translating between language pairswith high disparity in word order, reordering isextremely desirable for translation accuracy. In thispaper, the future research directions of reorderingmodels for Myanmar-English statistical machinetranslation (SMT) are also depicted. In this reorderingmodel, the word order on source-side is arranged intothe target side word order, before SMT system isapplied. We propose the use of recurrent neuralnetworks (RNNs) to model preordering for SMT

Concurrency Control Mechanism for Room Selling Using Short-Term Locks

A database is a structured collection of data items that can be accessed concurrently by several transactions. The concurrency control in distributed databases is an important problem. This paper is intended to build concurrency control system by using OSN (ordering by serialization number) method that uses time interval technique with short-term locks and that OSN scheduler is located on the server site. By using the OSN method, this proposed system can control the concurrent execution of transactions as well as the consistency of database can be maintained. And also it can avoid the deadlock by using short-term locks with time interval. In this paper, room selling for condominium is used as a case study