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With the development of Natural Language Processing, Automaticquestion-answering system such as Waston, Siri, Alexa, has become one of themost important NLP applications. Nowadays, enterprises try to build automaticcustom service chatbots to save human resources and provide a 24-hour customerservice. Evaluation of chatbots currently relied greatly on human annotationwhich cost a plenty of time. Thus, has initiated a new Short Text Conversationsubtask called Dialogue Quality (DQ) and Nugget Detection (ND) which aim toautomatically evaluate dialogues generated by chatbots. In this paper, we solvethe DQ and ND subtasks by deep neural network. We proposed two models for bothDQ and ND subtasks which is constructed by hierarchical structure: embeddinglayer, utterance layer, context layer and memory layer, to hierarchical learndialogue representation from word level, sentence level, context level to longrange context level. Furthermore, we apply gating and attention mechanism atutterance layer and context layer to improve the performance. We also triedBERT to replace embedding layer and utterance layer as sentence representation.The result shows that BERT produced a better utterance representation thanmulti-stack CNN for both DQ and ND subtasks and outperform other modelsproposed by other researches. The evaluation measures are proposed by , thatis, NMD, RSNOD for DQ and JSD, RNSS for ND, which is not traditional evaluationmeasures such as accuracy, precision, recall and f1-score. Thus, we have done aseries of experiments by using traditional evaluation measures and analyze theperformance and error.

Short Text Conversation Based on Deep Neural Network and Analysis on Evaluation Measures