In task-oriented dialogue scenarios, cross-domain zero-shot slot filling
plays a vital role in leveraging source domain knowledge to learn a model with
high generalization ability in unknown target domain where annotated data is
unavailable. However, the existing state-of-the-art zero-shot slot filling
methods have limited generalization ability in target domain, they only show
effective knowledge transfer on seen slots and perform poorly on unseen slots.
To alleviate this issue, we present a novel Hierarchical Contrastive Learning
Framework (HiCL) for zero-shot slot filling. Specifically, we propose a coarse-
to fine-grained contrastive learning based on Gaussian-distributed embedding to
learn the generalized deep semantic relations between utterance-tokens, by
optimizing inter- and intra-token distribution distance. This encourages HiCL
to generalize to the slot types unseen at training phase. Furthermore, we
present a new iterative label set semantics inference method to unbiasedly and
separately evaluate the performance of unseen slot types which entangled with
their counterparts (i.e., seen slot types) in the previous zero-shot slot
filling evaluation methods. The extensive empirical experiments on four
datasets demonstrate that the proposed method achieves comparable or even
better performance than the current state-of-the-art zero-shot slot filling
approaches.

在任务导向的对话场景中，跨领域零样本槽填充在利用源领域知识学习高泛化能力模型的同时，在未标注数据的未知目标领域中起到至关重要的作用。然而，现有最先进的零样本槽填充方法在目标领域中的泛化能力有限，只对已见槽显示有效的知识转移，在未见槽上表现不佳。为了缓解这一问题，我们提出了一种新颖的分层对比学习框架（HiCL）用于零样本槽填充。具体而言，我们提出了一种基于高斯分布嵌入的粗细粒度对比学习方法，以学习语句标记之间的广义深层语义关系，通过优化之间和之内的标记分布距离。这促使 HiCL 在训练阶段泛化到未见的槽类型上。此外，我们提出了一种新的迭代标签集语义推断方法，可以不偏不倚地分别评估在先前的零样本槽填充评估方法中与它们的对应槽类型相纠缠的未见槽类型的性能。对四个数据集的广泛经验性实验证明，所提出的方法的性能与当前最先进的零样本槽填充方法相当甚至更好。

HierarchicalContrast：一个用于跨领域零样本槽位填充的粗到精对比学习框架

HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework  for Cross-Domain Zero-Shot Slot Filling

Slot filling is identifying contiguous spans of words in an utterance that
correspond to certain parameters (i.e., slots) of a user request/query. Slot
filling is one of the most important challenges in modern task-oriented dialog
systems. Supervised learning approaches have proven effective at tackling this
challenge, but they need a significant amount of labeled training data in a
given domain. However, new domains (i.e., unseen in training) may emerge after
deployment. Thus, it is imperative that these models seamlessly adapt and fill
slots from both seen and unseen domains -- unseen domains contain unseen slot
types with no training data, and even seen slots in unseen domains are
typically presented in different contexts. This setting is commonly referred to
as zero-shot slot filling. Little work has focused on this setting, with
limited experimental evaluation. Existing models that mainly rely on
context-independent embedding-based similarity measures fail to detect slot
values in unseen domains or do so only partially. We propose a new zero-shot
slot filling neural model, LEONA, which works in three steps. Step one acquires
domain-oblivious, context-aware representations of the utterance word by
exploiting (a) linguistic features; (b) named entity recognition cues; (c)
contextual embeddings from pre-trained language models. Step two fine-tunes
these rich representations and produces slot-independent tags for each word.
Step three exploits generalizable context-aware utterance-slot similarity
features at the word level, uses slot-independent tags, and contextualizes them
to produce slot-specific predictions for each word. Our thorough evaluation on
four diverse public datasets demonstrates that our approach consistently
outperforms the SOTA models by 17.52%, 22.15%, 17.42%, and 17.95% on average
for unseen domains on SNIPS, ATIS, MultiWOZ, and SGD datasets, respectively.

提出了一种新的零射击槽填充神经模型 LEONA，通过利用语言特征、命名实体识别提示、预训练语言模型的上下文嵌入，从而获得领域无关的上下文感知表示，通过三步骤 fine-tunes 产生独立于槽的标签，并使用可推广的上下文感知语句 - 槽相似特征将其上下文化以产生每个单词的针对特定槽的预测，总体而言，在 SNIPS、ATIS、MultiWOZ 和 SGD 数据集上，从未见过的领域平均表现优于 SOTA 模型 17.52％、22.15％、17.42％ 和 17.95％

基于语言丰富和上下文感知的零样本槽位填充

Linguistically-Enriched and Context-Aware Zero-shot Slot Filling

Task-oriented dialog systems increasingly rely on deep learning-based slot
filling models, usually needing extensive labeled training data for target
domains. Often, however, little to no target domain training data may be
available, or the training and target domain schemas may be misaligned, as is
common for web forms on similar websites. Prior zero-shot slot filling models
use slot descriptions to learn concepts, but are not robust to misaligned
schemas. We propose utilizing both the slot description and a small number of
examples of slot values, which may be easily available, to learn semantic
representations of slots which are transferable across domains and robust to
misaligned schemas. Our approach outperforms state-of-the-art models on two
multi-domain datasets, especially in the low-data setting.

在面对数据量缺乏的多领域任务型对话系统中，本文提出一种结合了槽位值示例及其描述信息的学习方法，从而得到可在不同领域中迁移的槽位语义表示，并在两个多领域数据集上实现了比最先进方法更好的效果表现。