dzmitry bahdanau, kyunghyun cho, and yoshua bengio. 2014. neural machine translation by jointly learning to align and
Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance.
The models proposed as of late for brain machine interpretation frequently have a place with a group of encoder-decoders and comprises of an encoder that encodes a source sentence into a fixed-length vector from which a decoder creates an interpretation.
In this paper, we guess that the utilization of a fixed-length vector is a bottleneck in working on the exhibition of this essential encoder-decoder engineering, and propose to broaden this by permitting a model to naturally (delicate )look for parts of a source sentence that are pertinent to anticipating an objective word, without shaping these parts as a hard section unequivocally.
With this new methodology, we accomplish an interpretation execution equivalent to the current cutting edge state put together framework with respect to the undertaking of English-to-French interpretation. Moreover, subjective examination uncovers that the (delicate )arrangements found by the model concur well with our instinct.
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The British were in enormous debt, and they believed that they had the right to implement taxes on colonists as they were part of the British Crown.
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After winning the battle in the French and Indian War, the British were in enormous debt. To pay the debt British introduced different types of taxes in the colonies as they were part of the British Empire. Taxes included the Stamp Act and Townsend Acts. Colonies being part of the British Empire gave the Parliament to exercise strict laws and rules to gain wealth. These acts passed for the benefit of the empire rather than understanding the colonists.
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The structures that shape society B).