ECBTNet: English-Foreign Chinese intelligent translation via multi-subspace attention and hyperbolic tangent LSTM

The translation and sharing of languages around the world has become a necessary precondition for the movement of people. Teaching Chinese as a foreign language (TCFL) undertakes international function of spreading national culture. How to translate Chinese as a foreign language into English has become an important task. Machine translation has moved beyond the realm of theory to practical use as a result of advancements in computing. Deep learning is a prominent and relatively young subfield of machine learning that has shown promising results in a variety of fields. This paper aims to develop a TCFL-oriented English-Chinese neural machine translation model. First, this paper proposes a hyperbolic tangent long short-term memory network (HTLSTM). This will integrate future information and historical information to extract more sufficient contextual semantic information. Secondly, this paper proposes a multi-subspace attention mechanism. This integrates multiple attention calculation functions in the multi-subspace attention mechanism (MSATT). Thirdly, this paper combines HTLSTM with MSATT to construct an English-Chinese bilingual neural translation model called ECBTNet. The multi-subspace attention maps hidden state of hyperbolic tangent long-term short-term memory network to multiple subspaces. This then uses multiple attention calculation functions in the multi-attention mechanism when calculating the attention score. By applying different attention calculation functions in different subspaces to extract omni-directional context information features, accurate attention calculation results can be obtained. Finally, a systematic experiment is carried out, and the experimental data verify the feasibility of applying ECBTNet to the field of English-Chinese translation in TCFL.