Recently, resources and tasks were proposed to go beyond state tracking in
dialogue systems. An example is the frame tracking task, which requires
recording multiple frames, one for each user goal set during the dialogue. This
allows a user, for instance, to compare items corresponding to different goals.
This paper proposes a model which takes as input the list of frames created so
far during the dialogue, the current user utterance as well as the dialogue
acts, slot types, and slot values associated with this utterance. The model
then outputs the frame being referenced by each triple of dialogue act, slot
type, and slot value. We show that on the recently published Frames dataset,
this model significantly outperforms a previously proposed rule-based baseline.
In addition, we propose an extensive analysis of the frame tracking task by
dividing it into sub-tasks and assessing their difficulty with respect to our
model.

本文提出一种模型，它可以通过记录多个框架（针对用户目标），来跟踪对话过程中的用户目标并输出相应框架。结果表明，该模型在 Frames 数据集上表现显着优于之前提出的基于规则的基准，此外，本文将该任务细分为子任务并评估其困难程度。

一种用于记忆增强对话系统的框架追踪模型

A Frame Tracking Model for Memory-Enhanced Dialogue Systems

This paper presents the Frames dataset (Frames is available at
this http URL), a corpus of 1369 human-human dialogues
with an average of 15 turns per dialogue. We developed this dataset to study
the role of memory in goal-oriented dialogue systems. Based on Frames, we
introduce a task called frame tracking, which extends state tracking to a
setting where several states are tracked simultaneously. We propose a baseline
model for this task. We show that Frames can also be used to study memory in
dialogue management and information presentation through natural language
generation.

本论文提出了 Frames 数据集，旨在研究目标导向型对话系统中记忆的作用并通过自然语言生成展示信息呈现。此外，还介绍了基于 Frames 数据集的任务 Frame Tracking，将状态跟踪扩展到同时跟踪多个状态，并提出了基线模型。