Identifying intents from dialogue utterances forms an integral component of
task-oriented dialogue systems. Intent-related tasks are typically formulated
either as a classification task, where the utterances are classified into
predefined categories or as a clustering task when new and previously unknown
intent categories need to be discovered from these utterances. Further, the
intent classification may be modeled in a multiclass (MC) or multilabel (ML)
setup. While typically these tasks are modeled as separate tasks, we propose
IntenDD, a unified approach leveraging a shared utterance encoding backbone.
IntenDD uses an entirely unsupervised contrastive learning strategy for
representation learning, where pseudo-labels for the unlabeled utterances are
generated based on their lexical features. Additionally, we introduce a
two-step post-processing setup for the classification tasks using modified
adsorption. Here, first, the residuals in the training data are propagated
followed by smoothing the labels both modeled in a transductive setting.
Through extensive evaluations on various benchmark datasets, we find that our
approach consistently outperforms competitive baselines across all three tasks.
On average, IntenDD reports percentage improvements of 2.32%, 1.26%, and 1.52%
in their respective metrics for few-shot MC, few-shot ML, and the intent
discovery tasks respectively.

使用对比学习策略和修改的吸附法模型，IntenDD 方法通过共享的话语编码骨干来实现任务导向的对话系统中对意图的识别，并在多个任务中实现了显著的性能提升。

IntenDD: 一种统一对比学习方法用于意图检测和发现

IntenDD: A Unified Contrastive Learning Approach for Intent Detection  and Discovery

The rapidly growing market demand for automatic dialogue agents capable of
goal-oriented behavior has caused many tech-industry leaders to invest
considerable efforts into task-oriented dialog systems. The success of these
systems is highly dependent on the accuracy of their intent identification --
the process of deducing the goal or meaning of the user's request and mapping
it to one of the known intents for further processing. Gaining insights into
unrecognized utterances -- user requests the systems fail to attribute to a
known intent -- is therefore a key process in continuous improvement of
goal-oriented dialog systems.
We present an end-to-end pipeline for processing unrecognized user
utterances, deployed in a real-world, commercial task-oriented dialog system,
including a specifically-tailored clustering algorithm, a novel approach to
cluster representative extraction, and cluster naming. We evaluated the
proposed components, demonstrating their benefits in the analysis of
unrecognized user requests.

本研究提出了一种用于处理商业任务导向型对话系统中未识别用户语句的端到端管道，包括特定定制聚类算法、新颖的聚类代表提取方法和聚类命名。通过评估这些组件，证明了它们在分析未识别用户请求方面的益处。

任务导向对话系统中未被识别的用户话语的洞察

Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems

Open intent classification is a challenging task in dialogue systems. On the
one hand, it should ensure the quality of known intent identification. On the
other hand, it needs to detect the open (unknown) intent without prior
knowledge. Current models are limited in finding the appropriate decision
boundary to balance the performances of both known intents and the open intent.
In this paper, we propose a post-processing method to learn the adaptive
decision boundary (ADB) for open intent classification. We first utilize the
labeled known intent samples to pre-train the model. Then, we automatically
learn the adaptive spherical decision boundary for each known class with the
aid of well-trained features. Specifically, we propose a new loss function to
balance both the empirical risk and the open space risk. Our method does not
need open intent samples and is free from modifying the model architecture.
Moreover, our approach is surprisingly insensitive with less labeled data and
fewer known intents. Extensive experiments on three benchmark datasets show
that our method yields significant improvements compared with the
state-of-the-art methods. The codes are released at
this https URL

本文提出了一种后处理方法，通过利用已标记的已知意图样本对模型进行预训练，自动学习每个已知类别的自适应球形决策边界，从而均衡已知意图和未知意图之间的性能差异。实验证明，该方法在不需要未知意图样本和修改模型架构的情况下，依然能在三个基准数据集上显著提高分类精度。

自适应决策边界的深度开放意图分类

Deep Open Intent Classification with Adaptive Decision Boundary

One of the first steps in the utterance interpretation pipeline of many
task-oriented conversational AI systems is to identify user intents and the
corresponding slots. Since data collection for machine learning models for this
task is time-consuming, it is desirable to make use of existing data in a
high-resource language to train models in low-resource languages. However,
development of such models has largely been hindered by the lack of
multilingual training data. In this paper, we present a new data set of 57k
annotated utterances in English (43k), Spanish (8.6k) and Thai (5k) across the
domains weather, alarm, and reminder. We use this data set to evaluate three
different cross-lingual transfer methods: (1) translating the training data,
(2) using cross-lingual pre-trained embeddings, and (3) a novel method of using
a multilingual machine translation encoder as contextual word representations.
We find that given several hundred training examples in the the target
language, the latter two methods outperform translating the training data.
Further, in very low-resource settings, multilingual contextual word
representations give better results than using cross-lingual static embeddings.
We also compare the cross-lingual methods to using monolingual resources in the
form of contextual ELMo representations and find that given just small amounts
of target language data, this method outperforms all cross-lingual methods,
which highlights the need for more sophisticated cross-lingual methods.

本文讨论了在任务导向的对话系统中如何识别用户意图和相应的插槽，并通过多种跨语言转移方法进行数据集训练比较