For companies with customer service, mapping intents inside their
conversational data is crucial in building applications based on natural
language understanding (NLU). Nevertheless, there is no established automated
technique to gather the intents from noisy online chats or voice transcripts.
Simple clustering approaches are not suited to intent-sparse dialogues. To
solve this intent-landscape task, we propose an unsupervised pipeline that
extracts the intents and the taxonomy of intents from real-world dialogues. Our
pipeline mines intent-span candidates with an extractive Question-Answering
Electra model and leverages sentence embeddings to apply a low-level density
clustering followed by a top-level hierarchical clustering. Our results
demonstrate the generalization ability of an ELECTRA large model fine-tuned on
the SQuAD2 dataset to understand dialogues. With the right prompting question,
this model achieves a rate of linguistic validation on intent spans beyond 85%.
We furthermore reconstructed the intent schemes of five domains from the
MultiDoGo dataset with an average recall of 94.3%.

本研究提出了一种无监督的自动化 pipeline，基于问答模型和聚类方法，能够从真实的对话数据中抽取意图并建立意图分组分类体系，验证结果表明该方法能够解决自然语言理解中的意图识别问题，实现了超过 85% 的区分准确度。

利用可提取问答变换器构建实际谈话语料库的意图模式

Building the Intent Landscape of Real-World Conversational Corpora with Extractive Question-Answering Transformers

A law practitioner has to go through a lot of long legal case proceedings. To
understand the motivation behind the actions of different parties/individuals
in a legal case, it is essential that the parts of the document that express an
intent corresponding to the case be clearly understood. In this paper, we
introduce a dataset of 93 legal documents, belonging to the case categories of
either Murder, Land Dispute, Robbery, or Corruption, where phrases expressing
intent same as the category of the document are annotated. Also, we annotate
fine-grained intents for each such phrase to enable a deeper understanding of
the case for a reader. Finally, we analyze the performance of several
transformer-based models in automating the process of extracting intent phrases
(both at a coarse and a fine-grained level), and classifying a document into
one of the possible 4 categories, and observe that, our dataset is challenging,
especially in the case of fine-grained intent classification.

本文介绍了一个由 93 份涉及谋杀、土地纠纷、抢劫和腐败等案件类别的法律文书数据集，其中对于表达与文件类别相对应的意图的短语进行了注释，并为每个这样的短语注释了细粒度意图，最后分析了基于 Transformer 模型在自动提取意图短语（包括粗粒度和细粒度意图）及将文件分类为可能的 4 个类别方面的性能，并观察到，我们的数据集具有挑战性，尤其是在细粒度意图分类方面。

法律领域的细粒度意图分类

Fine-grained Intent Classification in the Legal Domain

Modern dialog managers face the challenge of having to fulfill human-level
conversational skills as part of common user expectations, including but not
limited to discourse with no clear objective. Along with these requirements,
agents are expected to extrapolate intent from the user's dialogue even when
subjected to non-canonical forms of speech. This depends on the agent's
comprehension of paraphrased forms of such utterances. Especially in
low-resource languages, the lack of data is a bottleneck that prevents
advancements of the comprehension performance for these types of agents. In
this regard, here we demonstrate the necessity of extracting the intent
argument of non-canonical directives in a natural language format, which may
yield more accurate parsing, and suggest guidelines for building a parallel
corpus for this purpose. Following the guidelines, we construct a Korean corpus
of 50K instances of question/command-intent pairs, including the labels for
classification of the utterance type. We also propose a method for mitigating
class imbalance, demonstrating the potential applications of the corpus
generation method and its multilingual extensibility.

本文探讨了现代对话管理器面临的挑战，提出了抽取非规范指令的意图参数的必要性，并提出了一种针对这种情况建立平行语料库的指南和方法，最终构建了一个包含 50K 个实例的韩语语料库，并展示了该语料库的潜在应用和多语言扩展性。