Background Based Conversations (BBCs) have been introduced to help
conversational systems avoid generating overly generic responses. In a BBC, the
conversation is grounded in a knowledge source. A key challenge in BBCs is
Knowledge Selection (KS): given a conversational context, try to find the
appropriate background knowledge (a text fragment containing related facts or
comments, etc.) based on which to generate the next response. Previous work
addresses KS by employing attention and/or pointer mechanisms. These mechanisms
use a local perspective, i.e., they select a token at a time based solely on
the current decoding state. We argue for the adoption of a global perspective,
i.e., pre-selecting some text fragments from the background knowledge that
could help determine the topic of the next response. We enhance KS in BBCs by
introducing a Global-to-Local Knowledge Selection (GLKS) mechanism. Given a
conversational context and background knowledge, we first learn a topic
transition vector to encode the most likely text fragments to be used in the
next response, which is then used to guide the local KS at each decoding
timestamp. In order to effectively learn the topic transition vector, we
propose a distantly supervised learning schema. Experimental results show that
the GLKS model significantly outperforms state-of-the-art methods in terms of
both automatic and human evaluation. More importantly, GLKS achieves this
without requiring any extra annotations, which demonstrates its high degree of
scalability.

该研究提出了一种全局到本地的知识选取机制，用于提高基于背景的对话系统中知识的选择效率，并且该机制通过远程监督学习模式进行训练，实验结果表明，它在自动评估和人工评估方面均明显优于现有的方法，且无须额外的注释，具有很高的可扩展性。

全球思考，本地行动：基于背景对话的远程监督全局到局部知识选择

Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local  Knowledge Selection for Background Based Conversation

Existing conversational systems tend to generate generic responses. Recently,
Background Based Conversations (BBCs) have been introduced to address this
issue. Here, the generated responses are grounded in some background
information. The proposed methods for BBCs are able to generate more
informative responses, they either cannot generate natural responses or have
difficulty in locating the right background information. In this paper, we
propose a Reference-aware Network (RefNet) to address the two issues. Unlike
existing methods that generate responses token by token, RefNet incorporates a
novel reference decoder that provides an alternative way to learn to directly
cite a semantic unit (e.g., a span containing complete semantic information)
from the background. Experimental results show that RefNet significantly
outperforms state-of-the-art methods in terms of both automatic and human
evaluations, indicating that RefNet can generate more appropriate and
human-like responses.

本文介绍了一种基于语义引用的方法，使用 Reference-aware Network (RefNet) 从背景信息中直接引用包含完整语义信息的语义单元，帮助生成更加自然和人性化的响应，实验结果表明 RefNet 在自动和人工评估方面都表现出比现有技术更优秀的性能。

RefNet: 基于背景的对话参考感知网络

RefNet: A Reference-aware Network for Background Based Conversation

Background Based Conversations (BBCs) have been developed to make dialogue
systems generate more informative and natural responses by leveraging
background knowledge. Existing methods for BBCs can be grouped into two
categories: extraction-based methods and generation-based methods. The former
extract spans frombackground material as responses that are not necessarily
natural. The latter generate responses thatare natural but not necessarily
effective in leveraging background knowledge. In this paper, we focus on
generation-based methods and propose a model, namely Context-aware Knowledge
Pre-selection (CaKe), which introduces a pre-selection process that uses
dynamic bi-directional attention to improve knowledge selection by using the
utterance history context as prior information to select the most relevant
background material. Experimental results show that our model is superior to
current state-of-the-art baselines, indicating that it benefits from the
pre-selection process, thus improving in-formativeness and fluency.

本研究提出了一种基于上下文感知的知识预选择模型，使用动态双向注意力机制来提高知识的选择，进而生成更具信息量和流畅的自然回复。实验结果表明，该模型在准确度和流畅度上都优于当前最先进的方法。