Existing approaches to Dialogue State Tracking (DST) rely on turn level
dialogue state annotations, which are expensive to acquire in large scale. In
call centers, for tasks like managing bookings or subscriptions, the user goal
can be associated with actions (e.g.~API calls) issued by customer service
agents. These action logs are available in large volumes and can be utilized
for learning dialogue states. However, unlike turn-level annotations, such
logged actions are only available sparsely across the dialogue, providing only
a form of weak supervision for DST models. To efficiently learn DST with sparse
labels, we extend a state-of-the-art encoder-decoder model. The model learns a
slot-aware representation of dialogue history, which focuses on relevant turns
to guide the decoder. We present results on two public multi-domain DST
datasets (MultiWOZ and Schema Guided Dialogue) in both settings i.e. training
with turn-level and with sparse supervision. The proposed approach improves
over baseline in both settings. More importantly, our model trained with sparse
supervision is competitive in performance to fully supervised baselines, while
being more data and cost efficient.

本论文提出了一种基于 encoder-decoder 模型的 Dialogue State Tracking 方法，在利用数据量较小的情况下学习对话状态，结果表明该方法与基于完全监督的基线相当，但成本更低。

稀疏监督引导下的注意力对话状态追踪

Attention Guided Dialogue State Tracking with Sparse Supervision

Dialogue state tracking (DST) aims at estimating the current dialogue state
given all the preceding conversation. For multi-domain DST, the data sparsity
problem is a major obstacle due to increased numbers of state candidates and
dialogue lengths. To encode the dialogue context efficiently, we utilize the
previous dialogue state (predicted) and the current dialogue utterance as the
input for DST. To consider relations among different domain-slots, the schema
graph involving prior knowledge is exploited. In this paper, a novel context
and schema fusion network is proposed to encode the dialogue context and schema
graph by using internal and external attention mechanisms. Experiment results
show that our approach can obtain new state-of-the-art performance of the
open-vocabulary DST on both MultiWOZ 2.0 and MultiWOZ 2.1 benchmarks.

本文提出了一种基于内部和外部注意机制的上下文和架构融合网络来编码对话上下文和架构图，能够在 MultiWOZ 2.0 和 MultiWOZ 2.1 基准测试中获得最新的最佳性能的开放词汇 DST。

多域对话状态跟踪的高效上下文和模式融合网络

Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue  State Tracking

Task oriented dialog agents provide a natural language interface for users to
complete their goal. Dialog State Tracking (DST), which is often a core
component of these systems, tracks the system's understanding of the user's
goal throughout the conversation. To enable accurate multi-domain DST, the
model needs to encode dependencies between past utterances and slot semantics
and understand the dialog context, including long-range cross-domain
references. We introduce a novel architecture for this task to encode the
conversation history and slot semantics more robustly by using attention
mechanisms at multiple granularities. In particular, we use cross-attention to
model relationships between the context and slots at different semantic levels
and self-attention to resolve cross-domain coreferences. In addition, our
proposed architecture does not rely on knowing the domain ontologies beforehand
and can also be used in a zero-shot setting for new domains or unseen slot
values. Our model improves the joint goal accuracy by 5% (absolute) in the
full-data setting and by up to 2% (absolute) in the zero-shot setting over the
present state-of-the-art on the MultiWoZ 2.1 dataset.

本文提出了一种新的架构，利用多粒度的注意力机制来更稳健地编码对话历史和槽的语义，用于实现准确的跨域对话状态跟踪，并在 MultiWoZ 2.1 数据集上将联合目标准确率提高了 5%（绝对值），并在零 - shot 设置下比现有最先进技术提高至多 2%（绝对值）。