Conversational question answering (ConvQA) over law knowledge bases (KBs)
involves answering multi-turn natural language questions about law and hope to
find answers in the law knowledge base. Despite many methods have been
proposed. Existing law knowledge base ConvQA model assume that the input
question is clear and can perfectly reflect user's intention. However, in real
world, the input questions are noisy and inexplict. This makes the model hard
to find the correct answer in the law knowledge bases. In this paper, we try to
use reinforcement learning to solve this problem. The reinforcement learning
agent can automatically learn how to find the answer based on the input
question and the conversation history, even when the input question is
inexplicit. We test the proposed method on several real world datasets and the
results show the effectivenss of the proposed model.

使用强化学习方法解决基于法律知识库的多轮问答中输入问题含糊不清的问题，测试结果证明了该方法的有效性。

基于知识图谱的对话问答强化学习

Reinforcement Learning for Conversational Question Answering over  Knowledge Graph

Many important questions (e.g. "How to eat healthier?") require conversation
to establish context and explore in depth. However, conversational question
answering (ConvQA) systems have long been stymied by scarce training data that
is expensive to collect. To address this problem, we propose a new technique
for synthetically generating diverse and high-quality dialog data: dialog
inpainting. Our approach takes the text of any document and transforms it into
a two-person dialog between the writer and an imagined reader: we treat
sentences from the article as utterances spoken by the writer, and then use a
dialog inpainter to predict what the imagined reader asked or said in between
each of the writer's utterances. By applying this approach to passages from
Wikipedia and the web, we produce WikiDialog and WebDialog, two datasets
totalling 19 million diverse information-seeking dialogs -- 1,000x larger than
the largest existing ConvQA dataset. Furthermore, human raters judge the answer
adequacy and conversationality of WikiDialog to be as good or better than
existing manually-collected datasets. Using our inpainted data to pre-train
ConvQA retrieval systems, we significantly advance state-of-the-art across
three benchmarks (QReCC, OR-QuAC, TREC CAsT) yielding up to 40% relative gains
on standard evaluation metrics.

提出一种通过生成 dialog 数据来解决 ConvQA 系统缺乏训练数据的问题的技术 --dialog inpainting，并将其应用于来自维基百科和互联网的篇章中，生成了两个数据集：WikiDialog 和 WebDialog，共 19m 条对话数据，相对于现有数据集增加了 1000 倍，进而预训练 ConvQA 检索系统，在三个基准测试中（QReCC，OR-QuAC，TREC CAsT）实现了显著提升，最高可达 40%。

对话修复：将文档转化为对话

Dialog Inpainting: Turning Documents into Dialogs

Conversational question answering (ConvQA) tackles sequential information
needs where contexts in follow-up questions are left implicit. Current ConvQA
systems operate over homogeneous sources of information: either a knowledge
base (KB), or a text corpus, or a collection of tables. This paper addresses
the novel issue of jointly tapping into all of these together, this way
boosting answer coverage and confidence. We present CONVINSE, an end-to-end
pipeline for ConvQA over heterogeneous sources, operating in three stages: i)
learning an explicit structured representation of an incoming question and its
conversational context, ii) harnessing this frame-like representation to
uniformly capture relevant evidences from KB, text, and tables, and iii)
running a fusion-in-decoder model to generate the answer. We construct and
release the first benchmark, ConvMix, for ConvQA over heterogeneous sources,
comprising 3000 real-user conversations with 16000 questions, along with entity
annotations, completed question utterances, and question paraphrases.
Experiments demonstrate the viability and advantages of our method, compared to
state-of-the-art baselines.

本研究提出了 CONVINSE，一种面向杂多数据源的对话问答系统，对来自知识库、文本和表格等多种异构数据源相应回答问题，并通过构建 ConvMix 基准测试平台，验证了该系统的优越性能。

异构数据源中的会话式问答

Conversational Question Answering on Heterogeneous Sources

Conversational question answering (ConvQA) is a simplified but concrete
setting of conversational search. One of its major challenges is to leverage
the conversation history to understand and answer the current question. In this
work, we propose a novel solution for ConvQA that involves three aspects.
First, we propose a positional history answer embedding method to encode
conversation history with position information using BERT in a natural way.
BERT is a powerful technique for text representation. Second, we design a
history attention mechanism (HAM) to conduct a "soft selection" for
conversation histories. This method attends to history turns with different
weights based on how helpful they are on answering the current question. Third,
in addition to handling conversation history, we take advantage of multi-task
learning (MTL) to do answer prediction along with another essential
conversation task (dialog act prediction) using a uniform model architecture.
MTL is able to learn more expressive and generic representations to improve the
performance of ConvQA. We demonstrate the effectiveness of our model with
extensive experimental evaluations on QuAC, a large-scale ConvQA dataset. We
show that position information plays an important role in conversation history
modeling. We also visualize the history attention and provide new insights into
conversation history understanding.

本研究提出一种新的解决方案，涉及三个方面（位置历史答案嵌入法、历史注意机制、多任务学习），用于通过卷积式问题应答来理解和回答当前问题，并通过多项实验评估证明该模型的有效性。