论文标题
多任务在语义搜索聊天机器人候选检索中查询响应互动
Query-Response Interactions by Multi-tasks in Semantic Search for Chatbot Candidate Retrieval
论文作者
论文摘要
在基于检索的聊天机器人中，对候选人检索的语义搜索是一个重要但被忽视的问题，该聊天机器人的目的是从大型池中有效地选择一堆候选人反应。现有的瓶颈是确保模型架构具有两个点：1）查询和响应之间的丰富相互作用，以产生与查询相关的响应； 2）将查询和响应分别投影到潜在空间的能力，可以在线推断期间有效地应用语义搜索。为了解决这个问题，我们提出了一种新的方法，称为基于多任务的语义搜索神经网络（MSSNN），用于候选检索，该方法通过多任务完成了查询响应相互作用。该方法采用SEQ2SEQ建模任务来学习一个良好的查询编码器，然后执行一个单词预测任务来构建响应嵌入，最后执行了一个简单的匹配模型来形成Dot-Prododuct Schorer。实验研究表明了所提出的方法的潜力。
Semantic search for candidate retrieval is an important yet neglected problem in retrieval-based Chatbots, which aims to select a bunch of candidate responses efficiently from a large pool. The existing bottleneck is to ensure the model architecture having two points: 1) rich interactions between a query and a response to produce query-relevant responses; 2) ability of separately projecting the query and the response into latent spaces to apply efficiently in semantic search during online inference. To tackle this problem, we propose a novel approach, called Multitask-based Semantic Search Neural Network (MSSNN) for candidate retrieval, which accomplishes query-response interactions through multi-tasks. The method employs a Seq2Seq modeling task to learn a good query encoder, and then performs a word prediction task to build response embeddings, finally conducts a simple matching model to form the dot-product scorer. Experimental studies have demonstrated the potential of the proposed approach.
