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Multi-Document Answer Generation for Non-Factoid Questions 非虚构问题的多文档答案生成
Valeriia Bolotova-Baranova
The current research will be devoted to the challenging and under-investigated task of multi-source answer generation for complex non-factoid questions. We will start with experimenting with generative models on one particular type of non-factoid questions - instrumental/procedural questions which often start with "how-to". For this, a new dataset, comprised of more than 100,000 QA-pairs which were crawled from a dedicated web-resource where each answer has a set of references to the articles it was written upon, will be used. We will also compare different ways of model evaluation to choose a metric which better correlates with human assessment. To be able to do this, the way people evaluate answers to non-factoid questions and set some formal criteria of what makes a good quality answer is needed to be understood. Eye-tracking and crowdsourcing methods will be employed to study how users interact with answers and evaluate them, and how the answer features correlate with task complexity. We hope that our research will help to redefine the way users interact and work with search engines so as to transform IR finally into the answer retrieval systems that users have always desired.
当前的研究将致力于复杂非因素问题的多源答案生成这一具有挑战性和研究不足的任务。我们将首先在一种特殊类型的非事实问题上实验生成模型——工具/程序问题,通常以“如何做”开头。为此,将使用一个新的数据集,该数据集由超过10万对问答对组成,这些问答对是从一个专门的网络资源中抓取的,其中每个答案都有一组参考文章。我们还将比较不同的模型评估方法,以选择一个与人类评估更好相关的度量。为了做到这一点,需要理解人们评估非事实性问题的答案的方式,并为什么是高质量的答案设定一些正式的标准。将采用眼动追踪和众包方法来研究用户如何与答案交互并对其进行评估,以及答案特征如何与任务复杂性相关联。我们希望我们的研究将有助于重新定义用户与搜索引擎交互和工作的方式,从而最终将IR转换为用户一直期望的答案检索系统。
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引用次数: 1
Recent Advances in Conversational Information Retrieval 会话信息检索的最新进展
Jianfeng Gao, Chenyan Xiong, Paul N. Bennett
Recent progress in deep learning has brought tremendous improvements in conversational AI, leading to a plethora of commercial conversational services that allow naturally spoken interactions, increasing the need for more human-centric interactions in IR. As a result, we have witnessed a resurgent interest in developing modern CIR systems in research communities and industry. This tutorial presents recent advances in CIR, focusing mainly on neural approaches and new applications developed in the past five years. Our goal is to provide a thorough and in-depth overview of the general definition of CIR, the components of CIR systems, new applications raised for its conversational aspects, and the (neural) techniques recently developed for it.
深度学习的最新进展为会话人工智能带来了巨大的进步,导致了大量的商业会话服务,这些服务允许自然的语音交互,从而增加了对IR中更多以人为中心的交互的需求。因此,我们目睹了在研究界和工业界对开发现代CIR系统的兴趣重新抬头。本教程介绍了CIR的最新进展,主要集中在过去五年中开发的神经方法和新应用。我们的目标是对CIR的一般定义、CIR系统的组件、为其会话方面提出的新应用以及最近为其开发的(神经)技术提供全面而深入的概述。
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引用次数: 14
Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation 基于群体感知的序列群推荐长短期图表示学习
Wen Wang, Wei Zhang, Jun Rao, Zhijie Qiu, Bo Zhang, Leyu Lin, H. Zha
Sequential recommendation and group recommendation are two important branches in the field of recommender system. While considerable efforts have been devoted to these two branches in an independent way, we combine them by proposing the novel sequential group recommendation problem which enables modeling group dynamic representations and is crucial for achieving better group recommendation performance. The major challenge of the problem is how to effectively learn dynamic group representations based on the sequential user-item interactions of group members in the past time frames. To address this, we devise a Group-aware Long- and Short-term Graph Representation Learning approach, namely GLS-GRL, for sequential group recommendation. Specifically, for a target group, we construct a group-aware long-term graph to capture user-item interactions and item-item co-occurrence in the whole history, and a group-aware short-term graph to contain the same information regarding only the current time frame. Based on the graphs, GLS-GRL performs graph representation learning to obtain long-term and short-term user representations, and further adaptively fuse them to gain integrated user representations. Finally, group representations are obtained by a constrained user-interacted attention mechanism which encodes the correlations between group members. Comprehensive experiments demonstrate that GLS-GRL achieves better performance than several strong alternatives coming from sequential recommendation and group recommendation methods, validating the effectiveness of the core components in GLS-GRL.
顺序推荐和分组推荐是推荐系统领域的两个重要分支。虽然这两个分支已经以独立的方式投入了大量的努力,但我们通过提出新颖的顺序组推荐问题将它们结合起来,该问题能够对组动态表示进行建模,对于获得更好的组推荐性能至关重要。该问题的主要挑战是如何基于过去时间框架内群组成员的顺序用户-项目交互有效地学习动态群组表示。为了解决这个问题,我们设计了一种组感知的长期和短期图表示学习方法,即GLS-GRL,用于顺序组推荐。具体来说,对于目标群体,我们构建了一个群体感知的长期图来捕获整个历史中的用户-项目交互和项目-项目共现,以及一个群体感知的短期图来包含仅关于当前时间框架的相同信息。基于图,GLS-GRL进行图表示学习,获得长期用户表示和短期用户表示,并进一步自适应融合,得到综合用户表示。最后,利用受约束的用户交互注意机制对群体成员之间的关联进行编码,获得群体表征。综合实验表明,GLS-GRL的性能优于序列推荐和分组推荐的几种强替代方法,验证了GLS-GRL核心组件的有效性。
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引用次数: 26
Deep Neural Matching Models for Graph Retrieval 图检索的深度神经匹配模型
Kunal Goyal, Utkarsh Gupta, A. De, Soumen Chakrabarti
Graph retrieval from a large corpus of graphs has a wide variety of applications, e.g., sentence retrieval using words and dependency parse trees for question answering, image retrieval using scene graphs, and molecule discovery from a set of existing molecular graphs. In such graph search applications, nodes, edges and associated features bear distinctive physical significance. Therefore, a unified, trainable search model that efficiently returns corpus graphs that are highly relevant to a query graph has immense potential impact. In this paper, we present an effective, feature and structure-aware, end-to-end trainable neural match scoring system for graphs. We achieve this by constructing the product graph between the query and a candidate graph in the corpus, and then conduct a family of random walks on the product graph, which are then aggregated into the match score, using a network whose parameters can be trained. Experiments show the efficacy of our method, compared to competitive baseline approaches.
从大量的图语料库中检索图具有各种各样的应用,例如,使用单词和依赖解析树进行句子检索,使用场景图进行图像检索,以及从一组现有的分子图中发现分子。在这样的图搜索应用中,节点、边和相关特征具有显著的物理意义。因此,一个统一的、可训练的搜索模型,能够有效地返回与查询图高度相关的语料库图,具有巨大的潜在影响。在本文中,我们提出了一个有效的,特征和结构感知的,端到端可训练的神经匹配评分系统。我们通过在查询和语料库中的候选图之间构建产品图来实现这一点,然后使用可训练参数的网络在产品图上进行一系列随机行走,然后将其聚合成匹配分数。实验表明,与竞争对手的基线方法相比,我们的方法是有效的。
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引用次数: 4
Towards Explainable Retrieval Models for Precision Medicine Literature Search 面向精准医学文献检索的可解释检索模型
Jiaming Qu, Jaime Arguello, Yue Wang
In professional search tasks such as precision medicine literature search, queries often involve multiple aspects. To assess the relevance of a document, a searcher often painstakingly validates each aspect in the query and follows a task-specific logic to make a relevance decision. In such scenarios, we say the searcher makes a structured relevance judgment, as opposed to the traditional univariate (binary or graded) relevance judgment. Ideally, a search engine can support searcher's workflow and follow the same steps to predict document relevance. This approach may not only yield highly effective retrieval models, but also open up opportunities for the model to explain its decision in the same "lingo" as the searcher. Using structured relevance judgment data from the TREC Precision Medicine track, we propose novel retrieval models that emulate how medical experts make structured relevance judgments. Our experiments demonstrate that these simple, explainable models can outperform complex, black-box learning-to-rank models.
在精准医学文献检索等专业检索任务中,查询往往涉及多个方面。为了评估文档的相关性,搜索者通常会费力地验证查询中的每个方面,并遵循特定于任务的逻辑来做出相关性决策。在这种情况下,我们说搜索者做出结构化的相关性判断,而不是传统的单变量(二元或分级)相关性判断。理想情况下,搜索引擎可以支持搜索者的工作流程,并遵循相同的步骤来预测文档的相关性。这种方法不仅可以产生高效的检索模型,还可以为模型提供机会,用与搜索者相同的“行话”解释其决策。利用来自TREC精密医学轨道的结构化相关性判断数据,我们提出了新的检索模型,模拟医学专家如何做出结构化相关性判断。我们的实验表明,这些简单的、可解释的模型可以胜过复杂的、黑盒学习排序模型。
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引用次数: 7
Multi-Level Multimodal Transformer Network for Multimodal Recipe Comprehension 多模态配方理解的多级多模态变压器网络
Ao Liu, Shuai Yuan, Chenbin Zhang, Congjian Luo, Yaqing Liao, Kun Bai, Zenglin Xu
Multimodal Machine Comprehension ($rm M^3C$) has been a challenging task that requires understanding both language and vision, as well as their integration and interaction. For example, the RecipeQA challenge, which provides several $rm M^3C$ tasks, requires deep neural models to understand textual instructions, images of different steps, as well as the logic orders of food cooking. To address this challenge, we propose a Multi-Level Multi-Modal Transformer (MLMM-Trans) framework to integrate and understand multiple textual instructions and multiple images. Our model can conduct intensive attention mechanism at multiple levels of objects (e.g., step level and passage-image level) for sequences of different modalities. Experiments have shown that our model can achieve the state-of-the-art results on the three multimodal tasks of RecipeQA.
多模态机器理解一直是一项具有挑战性的任务,需要理解语言和视觉,以及它们的集成和交互。例如,RecipeQA挑战,它提供了几个任务,需要深度神经模型来理解文本指令,不同步骤的图像,以及食物烹饪的逻辑顺序。为了解决这一挑战,我们提出了一个多层次多模态转换器(MLMM-Trans)框架来整合和理解多个文本指令和多个图像。我们的模型可以针对不同模态的序列在物体的多个层次(例如步骤级和通道-图像级)上进行强化注意机制。实验表明,我们的模型可以在RecipeQA的三个多模态任务上达到最先进的结果。
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引用次数: 6
AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems AR-CF:在解决冷启动问题的协同过滤中增加虚拟用户和项目
Dong-Kyu Chae, Jihoo Kim, Duen Horng Chau, Sang-Wook Kim
Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF) used in recommender systems. When few ratings are available, CF models typically fail to provide satisfactory recommendations for cold-start users or to display cold-start items on users' top-N recommendation lists. Data imputation has been a popular choice to deal with such problems in the context of CF, filling empty ratings with inferred scores. Different from (and complementary to) data imputation, this paper presents AR-CF, which stands for Augmented Reality CF, a novel framework for addressing the cold-start problems by generating virtual, but plausible neighbors for cold-start users or items and augmenting them to the rating matrix as additional information for CF models. Notably, AR-CF not only directly tackles the cold-start problems, but is also effective in improving overall recommendation qualities. Via extensive experiments on real-world datasets, AR-CF is shown to (1) significantly improve the accuracy of recommendation for cold-start users, (2) provide a meaningful number of the cold-start items to display in top-N lists of users, and (3) achieve the best accuracy as well in the basic top-N recommendations, all of which are compared with recent state-of-the-art methods.
冷启动问题可以说是协同过滤(CF)在推荐系统中所面临的最大挑战。当可用的评级很少时,CF模型通常无法为冷启动用户提供令人满意的推荐,或者无法在用户的top-N推荐列表中显示冷启动项。在CF上下文中,数据输入一直是处理此类问题的流行选择,用推断的分数填充空评级。不同于(并补充)数据输入,本文提出了AR-CF,即增强现实CF,这是一个解决冷启动问题的新框架,它通过为冷启动用户或项目生成虚拟但可信的邻居,并将其作为CF模型的附加信息增加到评级矩阵中。值得注意的是,AR-CF不仅直接解决了冷启动问题,而且有效地提高了整体推荐质量。通过对真实世界数据集的大量实验,AR-CF被证明(1)显著提高了冷启动用户推荐的准确性,(2)提供了有意义的冷启动项目数量,以显示在用户的top-N列表中,(3)在基本的top-N推荐中也达到了最好的准确性,所有这些都与最近最先进的方法进行了比较。
{"title":"AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems","authors":"Dong-Kyu Chae, Jihoo Kim, Duen Horng Chau, Sang-Wook Kim","doi":"10.1145/3397271.3401038","DOIUrl":"https://doi.org/10.1145/3397271.3401038","url":null,"abstract":"Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF) used in recommender systems. When few ratings are available, CF models typically fail to provide satisfactory recommendations for cold-start users or to display cold-start items on users' top-N recommendation lists. Data imputation has been a popular choice to deal with such problems in the context of CF, filling empty ratings with inferred scores. Different from (and complementary to) data imputation, this paper presents AR-CF, which stands for Augmented Reality CF, a novel framework for addressing the cold-start problems by generating virtual, but plausible neighbors for cold-start users or items and augmenting them to the rating matrix as additional information for CF models. Notably, AR-CF not only directly tackles the cold-start problems, but is also effective in improving overall recommendation qualities. Via extensive experiments on real-world datasets, AR-CF is shown to (1) significantly improve the accuracy of recommendation for cold-start users, (2) provide a meaningful number of the cold-start items to display in top-N lists of users, and (3) achieve the best accuracy as well in the basic top-N recommendations, all of which are compared with recent state-of-the-art methods.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115812001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 42
How Deep Learning Works for Information Retrieval 深度学习如何用于信息检索
D. Tao
Information retrieval (IR) is the science of search, the search of user query relevant pieces of information from a collection of unstructured resources. Information in this context includes text, imagery, audio, video, xml, program, and metadata. The journey of an IR process begins with a user query sent to the IR system which encodes the query, compares the query with the available resources, and returns the most relevant pieces of information. Thus, the system is equipped with the ability to store, retrieve and maintain information. In the early era of IR, the whole process was completed using handcrafted features and ad-hoc relevance measures. Later, principled frameworks for relevance measure were developed with statistical learning as a basis. Recently, deep learning has proven essential to the introduction of more opportunities to IR. This is because data-driven features combined with data-driven relevance measures can effectively eliminate the human bias in either feature or relevance measure design. Deep learning has shown its significant potential to transform IR evidenced by abundant empirical results. However, we continue to strive to gain a comprehensive understanding of deep learning. This is done by answering questions such as why deep structures are superior to shallow structures, how skip connections affect a model's performance, uncovering the potential relationship between some of the hyper-parameters and a model's performance, and exploring ways to reduce the chance for deep models to be fooled by adversaries. Answering such questions can help design more effective deep models and devise more efficient schemes for model training.
信息检索(Information retrieval, IR)是一门搜索的科学,是用户从非结构化资源的集合中查询相关的信息。此上下文中的信息包括文本、图像、音频、视频、xml、程序和元数据。IR流程从发送给IR系统的用户查询开始,IR系统对查询进行编码,将查询与可用资源进行比较,并返回最相关的信息片段。因此,该系统具有存储、检索和维护信息的能力。在IR的早期,整个过程是使用手工制作的特征和特别的相关性度量来完成的。后来,以统计学习为基础,开发了相关度量的原则框架。最近,深度学习已被证明对引入更多IR机会至关重要。这是因为数据驱动的特征与数据驱动的相关度量相结合,可以有效地消除在特征或相关度量设计中的人为偏见。大量的实证结果表明,深度学习已经显示出其改变IR的巨大潜力。然而,我们继续努力获得对深度学习的全面理解。这是通过回答以下问题来完成的:为什么深层结构优于浅层结构,跳过连接如何影响模型的性能,揭示一些超参数和模型性能之间的潜在关系,以及探索减少深层模型被对手欺骗的机会的方法。回答这些问题可以帮助设计更有效的深度模型,并设计更有效的模型训练方案。
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引用次数: 2
Expressions of Style in Information Seeking Conversation with an Agent 与代理人信息寻求对话中的风格表达
Paul Thomas, Daniel J. McDuff, M. Czerwinski, Nick Craswell
Past work in information-seeking conversation has demonstrated that people exhibit different conversational styles---for example, in word choice or prosody---that differences in style lead to poorer conversations, and that partners actively align their styles over time. One might assume that this would also be true for conversations with an artificial agent such as Cortana, Siri, or Alexa; and that agents should therefore track and mimic a user's style. We examine this hypothesis with reference to a lab study, where 24 participants carried out relatively long information-seeking tasks with an embodied conversational agent. The agent combined topical language models with a conversational dialogue engine, style recognition and alignment modules. We see that "style'' can be measured in human-to-agent conversation, although it looks somewhat different to style in human-to-human conversation and does not correlate with self-reported preferences. There is evidence that people align their style to the agent, and that conversations run more smoothly if the agent detects, and aligns to, the human's style as well.
过去在信息寻求对话方面的研究表明,人们表现出不同的对话风格——例如,在用词或韵律方面——风格的差异会导致更糟糕的对话,而且随着时间的推移,合作伙伴会主动调整他们的风格。有人可能会认为,这也适用于与人工智能(如Cortana、Siri或Alexa)的对话;因此,代理应该跟踪和模仿用户的风格。我们通过一项实验室研究来检验这一假设,在这项研究中,24名参与者通过一个具体化的对话代理执行了相对较长的信息搜索任务。该代理将主题语言模型与会话对话引擎、风格识别和对齐模块相结合。我们看到,“风格”可以在人与人之间的对话中测量,尽管它看起来与人与人之间的对话风格有些不同,并且与自我报告的偏好无关。有证据表明,人们会将自己的风格与智能体保持一致,如果智能体也检测到并与人类的风格保持一致,那么对话就会进行得更顺利。
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引用次数: 14
Context-Aware Term Weighting For First Stage Passage Retrieval 第一阶段文章检索的上下文感知词权
Zhuyun Dai, Jamie Callan
Term frequency is a common method for identifying the importance of a term in a document. But term frequency ignores how a term interacts with its text context, which is key to estimating document-specific term weights. This paper proposes a Deep Contextualized Term Weighting framework (DeepCT) that maps the contextualized term representations from BERT to into context-aware term weights for passage retrieval. The new, deep term weights can be stored in an ordinary inverted index for efficient retrieval. Experiments on two datasets demonstrate that DeepCT greatly improves the accuracy of first-stage passage retrieval algorithms.
术语频率是识别文档中术语重要性的常用方法。但是术语频率忽略了术语如何与其文本上下文相互作用,这是估计特定于文档的术语权重的关键。本文提出了一种深度上下文化术语加权框架(DeepCT),该框架将BERT的上下文化术语表示映射到上下文感知的术语权重,用于通道检索。新的深度项权重可以存储在一个普通的倒排索引中,以便有效地检索。在两个数据集上的实验表明,DeepCT极大地提高了第一阶段通道检索算法的准确性。
{"title":"Context-Aware Term Weighting For First Stage Passage Retrieval","authors":"Zhuyun Dai, Jamie Callan","doi":"10.1145/3397271.3401204","DOIUrl":"https://doi.org/10.1145/3397271.3401204","url":null,"abstract":"Term frequency is a common method for identifying the importance of a term in a document. But term frequency ignores how a term interacts with its text context, which is key to estimating document-specific term weights. This paper proposes a Deep Contextualized Term Weighting framework (DeepCT) that maps the contextualized term representations from BERT to into context-aware term weights for passage retrieval. The new, deep term weights can be stored in an ordinary inverted index for efficient retrieval. Experiments on two datasets demonstrate that DeepCT greatly improves the accuracy of first-stage passage retrieval algorithms.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126814395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 96
期刊
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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