Conversational AI assistants are becoming popular and question-answering is
an important part of any conversational assistant. Using relevant utterances as
features in question-answering has shown to improve both the precision and
recall for retrieving the right answer by a conversational assistant. Hence,
utterance generation has become an important problem with the goal of
generating relevant utterances (sentences or phrases) from a knowledge base
article that consists of a title and a description. However, generating good
utterances usually requires a lot of manual effort, creating the need for an
automated utterance generation. In this paper, we propose an utterance
generation system which 1) uses extractive summarization to extract important
sentences from the description, 2) uses multiple paraphrasing techniques to
generate a diverse set of paraphrases of the title and summary sentences, and
3) selects good candidate paraphrases with the help of a novel candidate
selection algorithm.

本文提出了一种使用提取式摘要、多种换句技术和新颖的候选句子选择算法生成相关对话语句的系统来解决对话代理机器人中问答环节重要性的问题。

自动话语生成

Automated Utterance Generation

Knowledge-grounded dialogue is a task of generating an informative response
based on both discourse context and external knowledge. As we focus on better
modeling the knowledge selection in the multi-turn knowledge-grounded dialogue,
we propose a sequential latent variable model as the first approach to this
matter. The model named sequential knowledge transformer (SKT) can keep track
of the prior and posterior distribution over knowledge; as a result, it can not
only reduce the ambiguity caused from the diversity in knowledge selection of
conversation but also better leverage the response information for proper
choice of knowledge. Our experimental results show that the proposed model
improves the knowledge selection accuracy and subsequently the performance of
utterance generation. We achieve the new state-of-the-art performance on Wizard
of Wikipedia (Dinan et al., 2019) as one of the most large-scale and
challenging benchmarks. We further validate the effectiveness of our model over
existing conversation methods in another knowledge-based dialogue Holl-E
dataset (Moghe et al., 2018).

论文提出了一种名为 `SKT` 的序列潜变量模型，该模型可提高多轮对话的知识选择准确性，并相应地改进了话语生成的绩效；实验结果表明，该模型在大规模对话评估中取得了最先进的表现。