Information-seeking dialogue systems are widely used in e-commerce systems,
with answers that must be tailored to fit the specific settings of the online
system. Given the user query, the information-seeking dialogue systems first
retrieve a subset of response candidates, then further select the best response
from the candidate set through re-ranking. Current methods mainly retrieve
response candidates based solely on the current query, however, incorporating
similar questions could introduce more diverse content, potentially refining
the representation and improving the matching process. Hence, in this paper, we
proposed a Query-bag based Pseudo Relevance Feedback framework (QB-PRF), which
constructs a query-bag with related queries to serve as pseudo signals to guide
information-seeking conversations. Concretely, we first propose a Query-bag
Selection module (QBS), which utilizes contrastive learning to train the
selection of synonymous queries in an unsupervised manner by leveraging the
representations learned from pre-trained VAE. Secondly, we come up with a
Query-bag Fusion module (QBF) that fuses synonymous queries to enhance the
semantic representation of the original query through multidimensional
attention computation. We verify the effectiveness of the QB-PRF framework on
two competitive pretrained backbone models, including BERT and GPT-2.
Experimental results on two benchmark datasets show that our framework achieves
superior performance over strong baselines.

本论文提出了一种 Query-bag 基于伪相关反馈的框架（QB-PRF），通过构建与查询相关的查询包作为伪信号来指导信息检索对话，采用对比学习训练无监督方式中的同义查询选择模块（QBS），通过多维注意力计算融合同义查询的查询包融合模块（QBF），验证其在两个预训练骨干模型（BERT 和 GPT-2）上的优越性能。实验结果表明，QB-PRF 框架在两个基准数据集上的表现比强基线模型更好。

选择查询包作为信息寻求对话中的伪相关反馈

Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking  Conversations

Information-seeking dialogue systems, including knowledge identification and
response generation, aim to respond to users with fluent, coherent, and
informative responses based on users' needs, which. To tackle this challenge,
we utilize data augmentation methods and several training techniques with the
pre-trained language models to learn a general pattern of the task and thus
achieve promising performance. In DialDoc21 competition, our system achieved
74.95 F1 score and 60.74 Exact Match score in subtask 1, and 37.72 SacreBLEU
score in subtask 2. Empirical analysis is provided to explain the effectiveness
of our approaches.

使用数据增强方法和预训练语言模型训练技巧来学习任务的通用模式，以实现对用户需求的流畅，连贯和信息丰富的响应，该方法在 DialDoc21 竞赛中取得了良好的成效。

DialDoc21 中的 CAiRE：面向信息获取对话系统的数据增强

CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue  System

Understanding the structure of interaction processes helps us to improve
information-seeking dialogue systems. Analyzing an interaction process boils
down to discovering patterns in sequences of alternating utterances exchanged
between a user and an agent. Process mining techniques have been successfully
applied to analyze structured event logs, discovering the underlying process
models or evaluating whether the observed behavior is in conformance with the
known process. In this paper, we apply process mining techniques to discover
patterns in conversational transcripts and extract a new model of
information-seeking dialogues, QRFA, for Query, Request, Feedback, Answer. Our
results are grounded in an empirical evaluation across multiple conversational
datasets from different domains, which was never attempted before. We show that
the QRFA model better reflects conversation flows observed in real
information-seeking conversations than models proposed previously. Moreover,
QRFA allows us to identify malfunctioning in dialogue system transcripts as
deviations from the expected conversation flow described by the model via
conformance analysis.

本文采用过程挖掘技术应用于对话记录中，从多个不同领域的交互式数据集中提取新的 QRFA 信息寻求对话模型，该模型更好地反映了实际信息寻求对话中观察到的对话流程，并且通过符合性分析，使我们能够识别对话系统中的故障。