Sequence-Semantic-Embedding - Tools and recipes to train deep learning models and build services for NLP tasks such as text classification, semantic search ranking and recall fetching, cross-lingual information retrieval, and question answering etc
SSE(Sequence Semantic Embedding) is an encoder framework toolkit for natural language processing related tasks. It's implemented in TensorFlow by leveraging TF's convenient deep learning blocks like DNN/CNN/LSTM etc. Depending on each specific task, similar semantic meanings can have different definitions. For example, in the category classification task, similar semantic meanings means that for each correct pair of (listing-title, category), the SSE of listing-title is close to the SSE of corresponding category. While in the information retrieval task, similar semantic meaning means for each relevant pair of (query, document), the SSE of query is close to the SSE of relevant document. While in the question answering task, the SSE of question is close to the SSE of correct answers.