Multimodal intent recognition poses significant challenges, requiring the
incorporation of non-verbal modalities from real-world contexts to enhance the
comprehension of human intentions. Existing benchmark datasets are limited in
scale and suffer from difficulties in handling out-of-scope samples that arise
in multi-turn conversational interactions. We introduce MIntRec2.0, a
large-scale benchmark dataset for multimodal intent recognition in multi-party
conversations. It contains 1,245 dialogues with 15,040 samples, each annotated
within a new intent taxonomy of 30 fine-grained classes. Besides 9,304 in-scope
samples, it also includes 5,736 out-of-scope samples appearing in multi-turn
contexts, which naturally occur in real-world scenarios. Furthermore, we
provide comprehensive information on the speakers in each utterance, enriching
its utility for multi-party conversational research. We establish a general
framework supporting the organization of single-turn and multi-turn dialogue
data, modality feature extraction, multimodal fusion, as well as in-scope
classification and out-of-scope detection. Evaluation benchmarks are built
using classic multimodal fusion methods, ChatGPT, and human evaluators. While
existing methods incorporating nonverbal information yield improvements,
effectively leveraging context information and detecting out-of-scope samples
remains a substantial challenge. Notably, large language models exhibit a
significant performance gap compared to humans, highlighting the limitations of
machine learning methods in the cognitive intent understanding task. We believe
that MIntRec2.0 will serve as a valuable resource, providing a pioneering
foundation for research in human-machine conversational interactions, and
significantly facilitating related applications. The full dataset and codes are
available at this https URL

MIntRec2.0 是一个大规模的多模态意图识别基准数据集，包含 1,245 个对话，15,040 个样本，使用 30 个细粒度类别的新意图分类法进行注释，还包括 5,736 个出现在多轮对话上下文中的超范围样本。

MIntRec2.0：一个用于对话中多模态意图识别和超范围检测的大规模基准数据集

MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent  Recognition and Out-of-scope Detection in Conversations

Legal case retrieval is a special Information Retrieval~(IR) task focusing on
legal case documents. Depending on the downstream tasks of the retrieved case
documents, users' information needs in legal case retrieval could be
significantly different from those in Web search and traditional ad-hoc
retrieval tasks. While there are several studies that retrieve legal cases
based on text similarity, the underlying search intents of legal retrieval
users, as shown in this paper, are more complicated than that yet mostly
unexplored. To this end, we present a novel hierarchical intent taxonomy of
legal case retrieval. It consists of five intent types categorized by three
criteria, i.e., search for Particular Case(s), Characterization, Penalty,
Procedure, and Interest. The taxonomy was constructed transparently and
evaluated extensively through interviews, editorial user studies, and query log
analysis. Through a laboratory user study, we reveal significant differences in
user behavior and satisfaction under different search intents in legal case
retrieval. Furthermore, we apply the proposed taxonomy to various downstream
legal retrieval tasks, e.g., result ranking and satisfaction prediction, and
demonstrate its effectiveness. Our work provides important insights into the
understanding of user intents in legal case retrieval and potentially leads to
better retrieval techniques in the legal domain, such as intent-aware ranking
strategies and evaluation methodologies.

通过研究案例检索中用户意图，本文提出了一个新颖的层次意图分类法，将法律案例检索任务划分为五种不同的意图类型。通过实验研究，揭示了不同意图下用户行为和满意度的显著差异，并将所提出的分类法应用于案例排序和满意度预测等任务中，展示了其有效性。该研究对于理解法律案例检索中用户意图具有重要的洞察力，并有望促进法律领域中更好的检索技术发展。

法律案例检索的意图分类

An Intent Taxonomy of Legal Case Retrieval

Over the years customers' expectation of getting information instantaneously
has given rise to the increased usage of channels like virtual assistants.
Typically, customers try to get their questions answered by low-touch channels
like search and virtual assistant first, before getting in touch with a live
chat agent or the phone representative. Higher usage of these low-touch systems
is a win-win for both customers and the organization since it enables
organizations to attain a low cost of service while customers get served
without delay. In this paper, we propose a two-part framework where the first
part describes methods to combine the information from different interaction
channels like call, search, and chat. We do this by summarizing (using a
stacked Bi-LSTM network) the high-touch interaction channel data such as call
and chat into short searchquery like customer intents and then creating an
organically grown intent taxonomy from interaction data (using Hierarchical
Agglomerative Clustering). The second part of the framework focuses on
extracting customer questions by analyzing interaction data sources. It
calculates similarity scores using TF-IDF and BERT(Devlin et al., 2019). It
also maps these identified questions to the output of the first part of the
framework using syntactic and semantic similarity.

本文提出了一个通过摘要网络和层次聚类技术来提取客户提问的框架，同时对客户的问题进行语法和语义相似度分析，并采用 TF-IDF 和 BERT 计算相似性得分。