III: Small: Exploiting the Massive User Generated Utterances for Intent Mining under Scarce Annotations

III：小：利用大量用户生成的话语进行稀缺注释下的意图挖掘

• 批准号: 1909323
• 负责人: Philip Yu
• 金额: $ 50万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909323&HistoricalAwards=false
• 关键词: III; Small; Exploiting; Massive; User

With the advance of artificial intelligence and machine learning technology, users interact with computational devices through spoken language to search information or accomplish tasks, as is evident by voice-based personal assistance products in smart home, automobile, education, healthcare, retail, and telecommunications environments. This project studies user intent mining that aims to understand the underlying goals or purposes from user-generated utterances. For example, by asking the personal assistance system "should I bring an umbrella tomorrow?", a user reveals the intention of getting weather information. Intent mining has been an elusive goal for information search due to diverse, implicit expressions in questions, and it is even harder for task accomplishment in conversational systems. For example, by giving a voice command "book a restaurant near me", the system shall learn to follow up with date or dietary preferences questions and refine the task goal, i.e., the intent, according to the user response. This project explores new computational techniques to understand user-generated utterances while addressing the scarcity of annotation data available for intent mining. The research findings and insights are expected to lead to better natural language understanding, dialogue management with reduced requirements on human annotation efforts. The proposed research will be applicable to the design of new question/conservation understanding systems that improve service, user satisfaction with reduced annotation cost. The research projects will engage graduate and undergraduate students to participate in. Research findings will be incorporated into course curriculum. The proposed project provides major advancements to the foundation of intent mining from user-generated utterances, by formulating four fundamental intent mining tasks that cover the discovery, annotation, unsupervised learning and sequential modeling phase in mining user intentions. The research tasks are proposed with a specific and consistent focus on dealing with the labeling scarcity issue as it is time-consuming and labor-intensive to obtain a large scale labeled data where user intents are accurately defined and correctly annotated from diverse and noise utterances. The project will include developments of principles, models and algorithms for intent discovery, joint intent and slot annotation, unsupervised intent learning and intent evolvement modeling. Abundant learning schemas such as zero-shot learning, reinforcement learning, generative modeling, and multi-modal learning will be introduced for the ever-intensive scenario where there is not enough annotation data for current learning rationales to succeed out-of-the-box. The research team plans to share results, including datasets and software, with the research community to facilitate future studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着人工智能和机器学习技术的发展，用户通过口头语言与计算设备进行互动，以搜索信息或完成任务，这是Smart Home，汽车，教育，医疗保健，零售，零售和电信环境中基于语音的个人援助产品可以明显看出的。该项目研究用户意图挖掘，旨在了解用户生成的话语中的基本目标或目的。例如，通过询问个人援助系统“我明天应该带伞吗？”，用户揭示了获取天气信息的目的。意图挖掘是由于问题中多样的，隐含的表达方式而导致信息搜索的难以捉摸的目标，并且在对话系统中完成任务更加困难。例如，通过给出语音命令“在我附近的餐厅”，该系统应学会跟进日期或饮食偏好问题，并根据用户的回应来完善任务目标，即意图。该项目探索了新的计算技术，以了解用户生成的话语，同时解决可挖掘意图挖掘的注释数据的稀缺性。预计研究结果和见解将带来更好的自然语言理解，对话管理，并减少了对人类注释工作的要求。拟议的研究将适用于新问题/保护理解系统的设计，这些系统可以改善服务，用户对注释成本的满意度。研究项目将吸引研究生和本科生参加。研究结果将纳入课程课程中。拟议的项目通过制定了涵盖发现，注释，无人监督的学习和序列建模阶段的四个基本意图采矿任务，从而为从用户生成的话语中挖掘的基础提供了重大进步。提出了研究任务，其特定而始终如一地专注于处理标签稀缺问题，因为它耗时且劳动密集型，以获取一个大规模标记的数据，在这些数据中，用户意图准确地定义并从多样性和噪音说法中正确注释。该项目将包括针对意图发现，关节意图和插槽注释，无监督意图学习和意图发展建模的原理，模型和算法的发展。将引入大量的学习模式，例如零射门学习，强化学习，生成建模和多模式学习，这些方案将引入不足的注释数据，无法使当前的学习理由无法开箱即用。研究小组计划与研究社区共享包括数据集和软件在内的结果，以促进未来的研究。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来支持的。

项目成果

Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning

• DOI: 10.18653/v1/2021.emnlp-main.144
• 发表时间: 2021-09
• 作者: Jianguo Zhang;Trung Bui;Seunghyun Yoon;Xiang Chen;Zhiwei Liu;Congying Xia;Quan Hung Tran;Walter Chang;P. Yu

SelfLRE: Self-refining Representation Learning for Low-resource Relation Extraction

• DOI: 10.1145/3539618.3592058
• 发表时间: 2023-07
• 期刊: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
• 作者: Xuming Hu;Junzhe Chen;Shiao Meng;Lijie Wen;Philip S. Yu

Dense Hierarchical Retrieval for Open-domain Question Answering

开放域问答的密集层次检索

• 发表时间: 2021
• 期刊: EMNLP (Findings
• 作者: Liu, Y.;Hashimoto, K.;Zhou, Y.;Yavuz, S.;Xiong, C.;Yu, P.S.
• 通讯作者: Yu, P.S.

Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System

• DOI: 10.1145/3357384.3358046
• 发表时间: 2019-08
• 期刊: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
• 作者: Ye Liu;Chenwei Zhang;Xiaohui Yan;Yi Chang;Philip S. Yu

BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network

• DOI: 10.1137/1.9781611976236.8
• 发表时间: 2020-01
• 期刊: ArXiv
• 作者: Zhiwei Liu;Mengting Wan;Stephen D. Guo;Kannan Achan;Philip S. Yu