Multi-Turn Response Selection for Chatbots With Hierarchical Aggregation Network of Multi-Representation

Abstract

Matching an appropriate response with its multi-turn context is a crucial challenge in retrievalbased chatbots. Current studies construct multiple representations of context and response to facilitate response selection, but they use these representations in isolation and ignore the relationships among representations. To address these problems, we propose a hierarchical aggregation network of multirepresentation (HAMR) to leverage abundant representations sufficiently and enhance valuable information. First, we employ bidirectional recurrent neural networks (BiRNN) to extract syntactic and semantic representations of sentences and use a self-aggregation mechanism to combine these representations. Second, we design a matching aggregation mechanism for fusing different matching information between each utterance in context and response, which is generated by an attention mechanism. By considering the candidate response as the real part of the context, we try to integrate all of them in chronological order and then accumulate the vectors to calculate the final matching degree. An extensive empirical study on two multi-turn response selection data sets indicates that our proposed model achieves a new state-of-the-art result.

A critical review of state‐of‐the‐art chatbot designs and applications

“…The framework considers the relationships between previous utterances and candidate responses and selects the optimal responses for the context. Mao et al (2019) also designed hierarchical aggregation network of multi‐representation based on bidirectional RNNs to match the information in context to alternative responses. The MILABOT developed by Serban et al (2018) adopted deep RL to select the most appropriate responses after considering the context.…”

A Question Detection Algorithm for Text Analysis

“…Differently, [6] utilizes user model and communication resources in developing a deep reinforcement learning network for a large financial corporation to enhance its customers experience. The main disadvantages of using AI-based systems [1,7,9] are long training and validation processes and requiring high performance computing cluster. In addition, for languages like Vietnamese, there are many ways to construct a question and a question can be understood based on conversational context, thus, identifying if a sentence is a question is always a challenging task.…”

“…In recent years, the emergence of chatbot [1,3,5,7,15] and voicebot [11,14] systems has created a strong demand for text analysis for promptly and accurately responding to customers' inquiries. The analysis helps the bot systems to better understand a given context represented by either text or voice.…”

Multi-Sensor Context-Aware Based Chatbot Model: An Application of Humanoid Companion Robot

“…In Ref. [ 6 ], bidirectional recurrent neural network (BiRNN) technology was used as the basis for a chatbot, and a GRU [ 7 ] was used as the core of the technology. The system architecture was exceedingly large and required substantial computing resources, but was verified favorably in experiments.…”