Without having to form these parts as a hard segment explicitly. Parts of a source sentence that are relevant to predicting a target word, Propose to extend this by allowing a model to automatically (soft-)search for Improving the performance of this basic encoder-decoder architecture, and

Paper, we conjecture that the use of a fixed-length vector is a bottleneck in

Recently for neural machine translation often belong to a family ofĮncoder-decoders and consists of an encoder that encodes a source sentence intoĪ fixed-length vector from which a decoder generates a translation. Jointly tuned to maximize the translation performance. Machine translation aims at building a single neural network that can be Unlike the traditional statistical machine translation, the neural Neural machine translation is a recently proposed approach to machine The semantic model is based on the word embedding model that is trained on a 30-million-word corpus and is used to capture the semantic similarities between words. The proposed solutions include character order normalization, grapheme and phoneme-based spellcheckers, and Khmer word semantic model. In this paper, we propose a set of robust solutions to the above challenges associated with Khmer word search. Moreover, due to the absence of WordNet-like lexical databases for Khmer language, it is impossible to establish semantic relation between words, enabling semantic search. These challenges hinder the use of Khmer language in search-embedded applications. Additionally, spelling mistakes are common since robust spellcheckers are not commonly available across the input device platforms. Multiple orders of characters and different spelling realizations of words impose a constraint on Khmer word search functionality. While the search function in some languages is trivial, Khmer word search is challenging given its complex writing system. Search is one of the key functionalities in digital platforms and applications such as an electronic dictionary, a search engine, and an e-commerce platform.

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