As an essential component of dialogue systems, response selection aims to pick out the optimal response among candidates to continue the dialogue. In existing studies, this task is usually regarded as a binary classification problem, where every candidate is ranked respectively for appropriateness. To improve its performance, we reformulate this task as a multiple-choice problem that allows the best selection to be made in one-shot inference. This new view inspires us to propose an architecture called Panoramic-encoder (Our work will be open-source for reproducibility and future research.) with a novel Candidates Attention Mechanism (CAM), which allows context-wise attention between responses and leads to fine-grained comparisons. Furthermore, we investigate and incorporate several techniques that have been proven effective for improving response selection. Experiments on three benchmarks show that our method pushes the state-of-the-art while achieving approximately 3X faster inference speed.
翻译:作为对话系统的一个基本组成部分,应答选择的目的是在候选人中选择继续对话的最佳回应。在现有研究中,这项任务通常被视为一个二进制分类问题,每个候选人被分别排列适当的等级。为了改进业绩,我们将此任务改写为一个多选制问题,使最佳选择能够以一分推论方式作出。这一新观点激励我们提出一个称为“全景编码(我们的工作将是可复制和今后研究的公开来源)”的架构,并有一个全新的候选人注意机制(CAM),允许在答复之间按背景来关注,并导致细化的比较。此外,我们调查并纳入一些已被证明对改进反应选择有效的方法。在三个基准上进行的实验表明,我们的方法在加快了大约3X的推断速度的同时,推动了目前的状况。