| Title | Long Text Filtering in English Translation based on LSTM Semantic Association |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wu, Juan |
| Conference Name | 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) |
| Keywords | composability, Correlation, Deep Learning, feature extraction, Filtering, Human Behavior, international trade, Long and Short Term Memory, Metrics, pubcrawl, Scalability, semantic analysis, Semantics, text analytics, text categorization, Text Filtering, Text Translation |
| Abstract | Translation studies is one of the fastest growing interdisciplinary research fields in the world today. Business English is an urgent research direction in the field of translation studies. To some extent, the quality of business English translation directly determines the success or failure of international trade and the economic benefits. On the basis of sequence information encoding and decoding model of LSTM, this paper proposes a strategy combining attention mechanism with bidirectional LSTM model to handle the question of feature extraction of text information. The proposed method reduces the semantic complexity and improves the overall correlation accuracy. The experimental results show its advantages. |
| DOI | 10.1109/I-SMAC52330.2021.9640672 |
| Citation Key | wu_long_2021 |
Long Text Filtering in English Translation based on LSTM Semantic Association