论文标题
用于NOTEC端到端响应选择的顺序神经网络
Sequential Neural Networks for Noetic End-to-End Response Selection
论文作者
论文摘要
Notic端到端响应选择挑战是第七对话系统技术挑战（DSTC7）中的一条曲目（DSTC7），旨在推动针对现实世界目标的对话系统的话语分类状态，为此，参与者需要从一组候选人中选择正确的下一语言，以供多态上下文。本文介绍了我们的系统在这一挑战下在两个数据集中排名最高的系统，一个集中精力，小（建议），另一个更多样化，更大（Ubuntu）。以前的最先进的模型使用基于层次结构的（话语级别和令牌级）神经网络，以明确地对不同转弯的话语之间的交互作用进行对上下文建模进行建模。在本文中，我们仅基于链序列上的多转响应选择的顺序匹配模型。我们的结果表明，过去尚未完全利用顺序匹配方法的潜力进行多转反应选择。除了在挑战中排名前1位外，所提出的模型还优于所有以前的模型，包括基于最新的层次结构模型，在两个大规模的公共多转变响应选择基准数据集上。
The noetic end-to-end response selection challenge as one track in the 7th Dialog System Technology Challenges (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context. This paper presents our systems that are ranked top 1 on both datasets under this challenge, one focused and small (Advising) and the other more diverse and large (Ubuntu). Previous state-of-the-art models use hierarchy-based (utterance-level and token-level) neural networks to explicitly model the interactions among different turns' utterances for context modeling. In this paper, we investigate a sequential matching model based only on chain sequence for multi-turn response selection. Our results demonstrate that the potentials of sequential matching approaches have not yet been fully exploited in the past for multi-turn response selection. In addition to ranking top 1 in the challenge, the proposed model outperforms all previous models, including state-of-the-art hierarchy-based models, on two large-scale public multi-turn response selection benchmark datasets.
