National University of Defense Technology

Other

Tao Chen

Fei Cai

Honghui Chen

Zhen Shu

Aimin Luo

Taihua Shao

Junxian Liu

Wanyu Chen

Xueshan Luo

Jingjing Yan

Danyang Jiang

Mengmeng Zhang

A method, a device and electronic device for clarification question generation are provided in one or more embodiments of this disclosure. The method includes: extracting entity information from a fuzzy context input by a user; inputting the fuzzy context into a template generating module of a pre-built CQG neural network model so as to obtain a clarification question template; inputting the entity information into an entity rendering module of the CQG neural network model so as to obtain at least one entity phrase; and generating a clarification question for a fuzzy question based on the clarification question template and the at least one entity phrase for presenting to the user.

Method, system, electronic device and storage medium for clarification question generation

The invention discloses a device and text representation method applied to sentence embedding, which has determining a parent word and a child words set corresponding to the parent word, obtaining an hidden interaction state of parent word based on the hidden interaction state of all child words; obtaining parent word sequence corresponding to the parent word and obtaining hidden state sequence corresponding to the hidden state of parent word; obtaining the interaction representation sequence of each parent word and other parent word based on the hidden state sequence, generating sentence embeddings. The invention proposes to realize sentence embeddings through a two-level interaction representation. The two-level interaction representation is local interaction representation and a global interaction representation respectively, and combines the two-level representation to generate a hybrid interaction representation, which can improve the accuracy and efficiency of sentence embeddings and be significantly better than the Tree-LSTM model in terms of accuracy.

Device and text representation method applied to sentence embedding

Disclosed are an answer selection method for a question answering system. With the method and system, a sentence distributed representation of a question and an answer is generated by adopting a hierarchical length-adaptive neural network structure with a purpose of extracting high-quality sentence features by using different neural feature extractors according to a length of an input sentence. First, one word distributed representation is generated for each word in the input sentence by connecting one fixed word embedding and one finely-adjusted word embedding. Next, a BiLSTM-based feature extractor is used for a short sentence and a Transformer-based feature extractor is used for a long sentence. Finally, a sentence vector representation for measuring a correlation between a question and a candidate answer is generated by using an attention pooling layer considering an interaction between a question sentence and an answer sentence.

Answer selection method for question answering system and the system

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Changsha, China

Tao Chen