Query Understanding via Intent Description Generation
CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所网络数据科学与技术重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Query understanding is a fundamental problem in information retrieval (IR), which has attracted continuous attention through the past decades. Many different tasks have been proposed for understanding users' search queries, e.g., query classification or query clustering. However, it is not that precise to understand a search query at the intent class/cluster level due to the loss of many detailed information. As we may find in many benchmark datasets, e.g., TREC and SemEval, queries are often associated with a detailed description provided by human annotators which clearly describes its intent to help evaluate the relevance of the documents. If a system could automatically generate a detailed and precise intent description for a search query, like human annotators, that would indicate much better query understanding has been achieved. In this paper, therefore, we propose a novel Query-to-Intent-Description (Q2ID) task for query understanding. Unlike those existing ranking tasks which leverage the query and its description to compute the relevance of documents, Q2ID is a reverse task which aims to generate a natural language intent description based on both relevant and irrelevant documents of a given query. To address this new task, we propose a novel Contrastive Generation model, namely CtrsGen for short, to generate the intent description by contrasting the relevant documents with the irrelevant documents given a query. We demonstrate the effectiveness of our model by comparing with several state-of-the-art generation models on the Q2ID task. We discuss the potential usage of such Q2ID technique through an example application.
Multi-Task Learning of Query Intent and Named Entities using Transfer Learning
Target Corporation（塔吉特公司）；AI Sciences（AI科学部门）
命名实体识别（NER）已被广泛研究，早期的算法是基于序列标记的，如隐马尔可夫模型（HMM）和条件随机场（CRF）。随后是基于神经网络的深度学习模型。最近，BERT在序列标记任务（如NER）中显示了新的最新水平的准确性。在这篇简短的文章中，我们研究了不同的方法来特定于任务的NER。任务特定的NER有两个组成部分-识别一段文本的意图（如搜索查询），然后用任务特定的命名实体标记查询。例如，我们考虑在搜索查询中标记Target商店位置的任务（可以在搜索框中输入或在Alexa或Google Home等设备中说出）。商店的位置是非常模糊的，有时很难区分，比如说一个位置和一个非位置。例如，“pickup my order at orange store”将“orange”作为商店位置，而“buy orange at target”将“orange”作为水果。我们通过多任务学习来探索这个困难，我们称之为全球到本地的信息转移。我们共同学习查询意图（即存储查找）和命名实体，通过使用多个损失函数在我们的BERT为基础的模型，并找到有趣的结果。
AbstRAG: Learning to Abstract for Retrieval Problems
Idiap Research Institute（Idiap 研究所）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院 (EPFL)）；São Paulo State University（圣保罗州立大学）；University of Manchester（曼彻斯特大学）；CRUK National Biomarker Centre（英国癌症研究中心国家生物标志物中心）
Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Massachusetts General Hospital, Harvard University（哈佛大学马萨诸塞州总医院）；Nota AI
Few-Shot Query Intent Detection via Relation-Aware Prompt Learning
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shenzhen MSU-BIT University（深圳MSU-BIT大学）；Harbin Institute of Technology（哈尔滨工业大学）
Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition
Nankai University（南开大学）；The Hong Kong Polytechnic University（香港理工大学）；Worcester Polytechnic Institute（沃斯通理工学院）；Zhejiang University（浙江大学）；Nanyang Technological University（南洋理工大学）；Indiana University Bloomington（印第安纳大学布利克学院）
Respond Beyond Language: A Benchmark for Video Generation in Response to Realistic User Intents
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学北京校区人工智能学院）；Serendipity One Inc.（Serendipity One公司）
Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests
Carnegie Mellon University Pittsburgh PA US；INESC-ID, Carnegie Mellon University Pittsburgh PA US；INESC-ID, Instituto Superior T\'ecnico, University of Lisbon Lisbon Portugal；NOVA LINCS\ University Lisbon Caparica Portugal；Carnegie Mellon University；INESC-ID, Carnegie Mellon University；INESC-ID, Instituto Superior T\'ecnico, University of Lisbon；NOVA LINCS\ University Lisbon
