# Chinese Neural Question Generation: Augmenting Knowledge into Multiple Neural Encoders
###### Abstract  
Neural question generation (NQG) is the task of automatically generating a question from a given passage and answering it with sequence-to-sequence neural models. Passage compression has been proposed to address the challenge of generating questions from a long passage text by only extracting relevant sentences containing the answer. However, it may not work well if the discarded irrelevant sentences contain the contextual information for the target question. Therefore, this study investigated how to incorporate knowledge triples into the sequence-to-sequence neural model to reduce such contextual information loss and proposed a multi-encoder neural model for Chinese question generation. This approach has been extensively evaluated in a large Chinese question and answer dataset. The study results showed that our approach outperformed the state-of-the-art NQG models by 5.938 points on the BLEU score and 7.120 points on the ROUGE-L score on the average since the proposed model is answer focused, which is helpful to produce an interrogative word matching the answer type. In addition, augmenting the information from the knowledge graph improves the BLEU score by 10.884 points. Finally, we discuss the challenges remaining for Chinese NQG. natural language processing; question generation; deep learning 20221212103  
of processing and ultimately the output. A different architecture, such as the one proposed here, would make many of these errors avoidable and their multiplying effect eliminated or substantially reduced. In recent years, with the availability of \"big data\", such as large question banks (SQuAD; [8]), and effective machine learning algorithms, e.g., deep neural networks [9], data driven approaches based on new data processing architectures have attracted a great deal of interest from researchers working on natural language processing tasks, such as neural text summarization [10] and neural machine translation [11], mainly because these approaches do not require human defined rules and have good generalization power. Some researchers attempted to adopt this approach in question generation [12; 13; 14]. [PERSON] et al. [13] presented a recurrent neural network to generate factoid English questions from structured data, which is not common in text-to-text question generation tasks, where the source is an unstructured text, such as a sentence or paragraph. [PERSON] et al. [12] proposed a preliminary study on neural English question generation from text by using an encoder-decoder neural network model that takes an answer and its source text as input and outputs a relevant question. [PERSON] et al. [14] further improved the model with rich features about the answer (e.g., answer position and lexical features) to generate answer focused questions, and incorporate a copy mechanism, dealing with unknown words, that allows the copy of the source text when generating questions. This model has reached one of the best results on the Stanford Question Answering Dataset (SQuAD), which contains 100,100+ question answer pairs based on the Wikipedia articles. However, the error analysis results showed that most ill generated questions (37.04% errors) had incorrect question words or interrogative words although the answer position feature has been incorporated. Another problem is that these erroneous questions (20.37% errors) include wrong parts of the source text, which are irrelevant to the answer. A major cause for these problems is the relatively large size of the source text, which may cause the current neural encoder-decoder model to not pay attention to the answer and relevant context words. To address the complexity of the long passage text, a traditional approach includes a passage compression step in question generation by extracting only relevant sentences containing the answer [15]. Thus, the question is generated based on the relevant sentences.