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Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献

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A Multilingual Approach for Unsupervised Search Task Identification 一种多语言的无监督搜索任务识别方法
Luis Lugo, Jose G. Moreno, G. Hubert
Users convert their information needs to search queries, which are then run on available search engines. Query logs registered by search engines enable the automatic identification of the search tasks that users perform to fulfill their information needs. Search engine logs contain queries in multiple languages, but most existing methods for search task identification are not multilingual. Some methods rely on search context training of custom embeddings or external indexed collections that support a single language, making it challenging to support the multiple languages of queries run in search engines. Other methods depend on supervised components and user identifiers to model search tasks. The supervised components require labeled collections, which are difficult and costly to get in multiple languages. Also, the need for user identifiers renders these methods unfeasible in user agnostic scenarios. Hence, we propose an unsupervised multilingual approach for search task identification. The proposed approach is user agnostic, enabling its use in both user-independent and personalized scenarios. Furthermore, the multilingual query representation enables us to address the existing trade-off when mapping new queries to the identified search tasks.
用户将他们的信息需求转换为搜索查询,然后在可用的搜索引擎上运行。搜索引擎注册的查询日志可以自动识别用户为满足信息需求而执行的搜索任务。搜索引擎日志包含多种语言的查询,但大多数现有的搜索任务识别方法都不是多语言的。有些方法依赖于自定义嵌入的搜索上下文训练或支持单一语言的外部索引集合,这使得支持搜索引擎中运行的查询的多语言变得很困难。其他方法依赖于监督组件和用户标识符来建模搜索任务。受监督的组件需要标记集合,这在多种语言中是困难和昂贵的。此外,对用户标识符的需求使得这些方法在与用户无关的场景中不可行。因此,我们提出了一种无监督的多语言搜索任务识别方法。所提出的方法与用户无关,因此可以在用户独立和个性化的场景中使用。此外,多语言查询表示使我们能够在将新查询映射到已识别的搜索任务时解决现有的权衡问题。
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引用次数: 2
Attending to Inter-sentential Features in Neural Text Classification 关注神经文本分类中的句子间特征
Billy Chiu, Sunil Kumar Sahu, Neha Sengupta, Derek Thomas, Mohammady Mahdy
Text classification requires a deep understanding of the linguistic features in text; in particular, the intra-sentential (local) and inter-sentential features (global). Models that operate on word sequences have been successfully used to capture the local features, yet they are not effective in capturing the global features in long-text. We investigate graph-level extensions to such models and propose a novel architecture for combining alternative text features. It uses an attention mechanism to dynamically decide how much information to use from a sequence- or graph-level component. We evaluated different architectures on a range of text classification datasets, and graph-level extensions were found to improve performance on most benchmarks. In addition, the attention-based architecture, as adaptively-learned from the data, outperforms the generic and fixed-value concatenation ones.
文本分类需要深刻理解文本的语言特征;特别是句子内(局部)和句子间(全局)的特征。对词序列进行操作的模型已经成功地用于捕获局部特征,但它们在捕获长文本的全局特征方面并不有效。我们研究了这些模型的图级扩展,并提出了一种用于组合替代文本特征的新架构。它使用注意机制来动态地决定从序列级或图级组件中使用多少信息。我们在一系列文本分类数据集上评估了不同的架构,发现图级扩展可以提高大多数基准测试的性能。此外,从数据中自适应学习的基于注意力的体系结构优于通用的和固定值连接的体系结构。
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引用次数: 2
Having Your Cake and Eating it Too: Training Neural Retrieval for Language Inference without Losing Lexical Match 鱼和熊掌兼得:在不丢失词汇匹配的情况下训练语言推理的神经检索
Vikas Yadav, Steven Bethard, M. Surdeanu
We present a study on the importance of information retrieval (IR) techniques for both the interpretability and the performance of neural question answering (QA) methods. We show that the current state-of-the-art transformer methods (like RoBERTa) encode poorly simple information retrieval (IR) concepts such as lexical overlap between query and the document. To mitigate this limitation, we introduce a supervised RoBERTa QA method that is trained to mimic the behavior of BM25 and the soft-matching idea behind embedding-based alignment methods. We show that fusing the simple lexical-matching IR concepts in transformer techniques results in improvement a) of their (lexical-matching) interpretability, b) retrieval performance, and c) the QA performance on two multi-hop QA datasets. We further highlight the lexical-chasm gap bridging capabilities of transformer methods by analyzing the attention distributions of the supervised RoBERTa classifier over the context versus lexically-matched token pairs.
我们研究了信息检索(IR)技术对神经问答(QA)方法的可解释性和性能的重要性。我们展示了当前最先进的转换方法(如RoBERTa)对简单的信息检索(IR)概念(如查询和文档之间的词法重叠)进行了很差的编码。为了减轻这一限制,我们引入了一种受监督的RoBERTa QA方法,该方法经过训练以模仿BM25的行为和基于嵌入的对齐方法背后的软匹配思想。我们表明,在变压器技术中融合简单的词汇匹配IR概念可以改善a)它们的(词汇匹配)可解释性,b)检索性能,以及c)两个多跳QA数据集上的QA性能。通过分析有监督RoBERTa分类器在上下文上的注意力分布与词汇匹配的令牌对的对比,我们进一步强调了transformer方法的词汇鸿沟桥接能力。
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引用次数: 2
DukeNet: A Dual Knowledge Interaction Network for Knowledge-Grounded Conversation DukeNet:基于知识对话的双重知识交互网络
Chuan Meng, Pengjie Ren, Zhumin Chen, Weiwei Sun, Z. Ren, Zhaopeng Tu, M. de Rijke
Today's conversational agents often generate responses that not sufficiently informative. One way of making them more informative is through the use of of external knowledge sources with so-called Knowledge-Grounded Conversations (KGCs). In this paper, we target the Knowledge Selection (KS) task, a key ingredient in KGC, that is aimed at selecting the appropriate knowledge to be used in the next response. Existing approaches to Knowledge Selection (KS) based on learned representations of the conversation context, that is previous conversation turns, and use Maximum Likelihood Estimation (MLE) to optimize KS. Such approaches have two main limitations. First, they do not explicitly track what knowledge has been used in the conversation nor how topics have shifted during the conversation. Second, MLE often relies on a limited set of example conversations for training, from which it is hard to infer that facts retrieved from the knowledge source can be re-used in multiple conversation contexts, and vice versa. We propose Dual Knowledge Interaction Network (DukeNet), a framework to address these challenges. DukeNet explicitly models knowledge tracking and knowledge shifting as dual tasks. We also design Dual Knowledge Interaction Learning (DukeL), an unsupervised learning scheme to train DukeNet by facilitating interactions between knowledge tracking and knowledge shifting, which, in turn, enables DukeNet to explore extra knowledge besides the knowledge encountered in the training set. This dual process also allows us to define rewards that help us to optimize both knowledge tracking and knowledge shifting. Experimental results on two public KGC benchmarks show that DukeNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, indicating that DukeNet enhanced by DukeL can select more appropriate knowledge and hence generate more informative and engaging responses.
今天的会话代理通常会产生信息不足的响应。一种使它们更具信息性的方法是通过使用所谓的基于知识的对话(kgc)的外部知识来源。在本文中,我们的目标是知识选择(KS)任务,这是KGC的一个关键组成部分,旨在选择在下一个回答中使用的适当知识。现有的知识选择方法基于学习到的会话上下文表示,即先前的会话回合,并使用最大似然估计(MLE)来优化知识选择。这种方法有两个主要的局限性。首先,它们没有明确地跟踪谈话中使用了哪些知识,也没有跟踪谈话中话题是如何转移的。其次,MLE通常依赖于一组有限的示例对话进行训练,从中很难推断出从知识来源检索到的事实可以在多个对话上下文中重用,反之亦然。我们提出双知识交互网络(DukeNet),这是一个解决这些挑战的框架。DukeNet明确地将知识跟踪和知识转移建模为双重任务。我们还设计了双知识交互学习(Dual Knowledge Interaction Learning, DukeL),这是一种无监督学习方案,通过促进知识跟踪和知识转移之间的交互来训练DukeNet,从而使DukeNet能够探索除训练集中遇到的知识之外的额外知识。这种双重过程还允许我们定义奖励,帮助我们优化知识跟踪和知识转移。在两个公共KGC基准测试上的实验结果表明,DukeNet在自动和人工评估方面都明显优于最先进的方法,这表明DukeL增强的DukeNet可以选择更合适的知识,从而产生更丰富、更有吸引力的响应。
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引用次数: 48
Encoding History with Context-aware Representation Learning for Personalized Search 基于上下文感知表示学习的个性化搜索历史编码
Yujia Zhou, Zhicheng Dou, Ji-rong Wen
The key to personalized search is to clarify the meaning of current query based on user's search history. Previous personalized studies tried to build user profiles on the basis of historical data to tailor the ranking. However, we argue that the user profile based methods do not really disambiguate the current query. They still retain some semantic bias when building user profiles. In this paper, we propose to encode history with context-aware representation learning to enhance the representation of current query, which is a direct way to clarify the user's information need. Specifically, endowed with the benefit from transformer on aggregating contextual information, we devise a query disambiguation model to parse the meaning of current query in multiple stages. Moreover, for covering the cases that current query is not sufficient to express the intent, we train a personalized language model to predict user intent from existing queries. Under the interaction of two sub-models, we can generate the context-aware representation of current query and re-rank the results based on it. Experimental results show the significant improvement of our model compared with previous methods.
个性化搜索的关键是根据用户的搜索历史来明确当前查询的含义。以前的个性化研究试图在历史数据的基础上建立用户档案,以定制排名。然而,我们认为基于用户配置文件的方法并不能真正消除当前查询的歧义。在建立用户档案时,他们仍然保留了一些语义上的偏见。在本文中,我们提出用上下文感知表示学习对历史进行编码,以增强当前查询的表示,这是一种明确用户信息需求的直接方法。具体而言,我们利用转换器在聚合上下文信息方面的优势,设计了查询消歧模型,对当前查询进行多阶段的语义解析。此外,为了涵盖当前查询不足以表达意图的情况,我们训练了一个个性化的语言模型来从现有查询中预测用户意图。在两个子模型的交互作用下,我们可以生成当前查询的上下文感知表示,并在此基础上对结果进行重新排序。实验结果表明,与以往的方法相比,我们的模型有了显著的改进。
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引用次数: 25
Humor Detection in Product Question Answering Systems 产品问答系统中的幽默检测
Yftah Ziser, Elad Kravi, David Carmel
Community question-answering (CQA) has been established as a prominent web service enabling users to post questions and get answers from the community. Product Question Answering (PQA) is a special CQA framework where questions are asked (and are answered) in the context of a specific product. Naturally, humorous questions are integral part of such platforms, especially as some products attract humor due to their unreasonable price, their peculiar functionality, or in cases that users emphasize their critical point-of-view through humor. Detecting humorous questions in such systems is important for sellers, to better understand user engagement with their products. It is also important to signal users about flippancy of humorous questions, and that answers for such questions should be taken with a grain of salt. In this study we present a deep-learning framework for detecting humorous questions in PQA systems. Our framework utilizes two properties of the questions - Incongruity and Subjectivity, demonstrating their contribution for humor detection. We evaluate our framework over a real-world dataset, demonstrating an accuracy of 90.8%, up to 18.3% relative improvement over baseline methods. We then demonstrate the existence of product bias in PQA platforms, when some products attract more humorous questions than others. A classifier trained over unbiased data is outperformed by the biased classifier, however, it excels in the task of differentiating between humorous and non-humorous questions that are both related to the same product. To the best of our knowledge this work is the first to detect humor in PQA setting.
社区问答(CQA)已经成为一种重要的web服务,使用户可以在社区中发布问题并获得答案。产品问答(PQA)是一种特殊的CQA框架,其中在特定产品的上下文中提出(并回答)问题。当然,幽默的问题是这些平台不可或缺的一部分,特别是一些产品由于其不合理的价格,特殊的功能,或者用户通过幽默强调自己的批评观点而吸引了幽默。在这样的系统中发现幽默问题对卖家来说很重要,可以更好地了解用户对他们产品的参与度。同样重要的是,要提醒用户注意幽默问题的轻率性,对这类问题的回答应持保留态度。在这项研究中,我们提出了一个深度学习框架,用于在PQA系统中检测幽默问题。我们的框架利用了问题的两个属性——不一致性和主观性,展示了它们对幽默检测的贡献。我们在真实数据集上评估了我们的框架,证明准确率为90.8%,比基线方法相对提高了18.3%。然后,我们证明了PQA平台中存在产品偏见,当一些产品比其他产品吸引更多幽默的问题时。在无偏数据上训练的分类器优于有偏分类器,然而,它在区分与同一产品相关的幽默和非幽默问题的任务中表现出色。据我们所知,这项工作是第一次在PQA设置中发现幽默。
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引用次数: 10
Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic 视频推荐与多门混合专家软演员评论家
Dingcheng Li, Xu Li, Jun Wang, P. Li
In this paper, we propose a reinforcement learning based large scale multi-objective ranking system for optimizing short-video recommendation on an industrial video sharing platform. Multiple competing ranking objective and implicit selection bias in user feedback are the main challenges in real-world platform. In order to address those challenges, we integrate multi-gate mixture of experts and soft actor critic into the ranking system. We demonstrated that our proposed framework can greatly reduce the loss function compared with systems only based on single strategies.
本文提出了一种基于强化学习的大规模多目标排名系统,用于优化工业视频分享平台上的短视频推荐。用户反馈中的多重竞争排名目标和隐式选择偏差是现实平台中的主要挑战。为了解决这些挑战,我们将专家和软演员评论家的多门混合集成到排名系统中。我们证明,与仅基于单一策略的系统相比,我们提出的框架可以大大减少损失函数。
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引用次数: 20
Evaluation of Cross Domain Text Summarization 跨领域文本摘要的评价
Liam Scanlon, Shiwei Zhang, Xiuzhen Zhang, M. Sanderson
Extractive-abstractive hybrid summarization can generate readable, concise summaries for long documents. Extraction-then-abstraction and extraction-with-abstraction are two representative approaches to hybrid summarization. But their general performance is yet to be evaluated by large scale experiments.We examined two state-of-the-art hybrid summarization algorithms from three novel perspectives: we applied them to a form of headline generation not previously tried, we evaluated the generalization of the algorithms by testing them both within and across news domains; and we compared the automatic assessment of the algorithms to human comparative judgments. It is found that an extraction-then-abstraction hybrid approach outperforms an extraction-with-abstraction approach, particularly for cross-domain headline generation.
抽取-抽象混合摘要可以为长文档生成可读的、简洁的摘要。先提取后抽象和先提取后抽象是混合摘要的两种代表性方法。但它们的总体性能还有待于大规模实验的评估。我们从三个新颖的角度研究了两种最先进的混合摘要算法:我们将它们应用于以前从未尝试过的标题生成形式,我们通过在新闻域内和跨新闻域测试来评估算法的泛化性;我们将算法的自动评估与人类的比较判断进行了比较。研究发现,提取-抽象-混合方法优于提取-抽象方法,特别是对于跨域标题生成。
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引用次数: 2
An Analysis of BERT in Document Ranking BERT在文献排序中的应用分析
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma
Although BERT has shown its effectiveness in a number of IR-related tasks, especially document ranking, the understanding of its internal mechanism remains insufficient. To increase the explainability of the ranking process performed by BERT, we investigate a state-of-the-art BERT-based ranking model with focus on its attention mechanism and interaction behavior. Firstly, we look into the evolving of the attention distribution. It shows that in each step, BERT dumps redundant attention weights on tokens with high document frequency (such as periods). This may lead to a potential threat to the model robustness and should be considered in future studies. Secondly, we study how BERT models interactions between query and document and find that BERT aggregates document information to query token representations through their interactions, but extracts query-independent representations for document tokens. It indicates that it is possible to transform BERT into a more efficient representation-focused model. These findings help us better understand the ranking process by BERT and may inspire future improvement.
尽管BERT在许多与ir相关的任务中显示出其有效性,特别是文档排序,但对其内部机制的理解仍然不足。为了提高BERT进行排序过程的可解释性,我们研究了一种基于BERT的排名模型,重点研究了其注意机制和交互行为。首先,我们研究了注意力分布的演变。它表明,在每个步骤中,BERT都会将冗余的注意力权重转储到具有高文档频率(例如句号)的令牌上。这可能会对模型的稳健性造成潜在威胁,应在今后的研究中加以考虑。其次,我们研究了BERT如何建模查询和文档之间的交互,发现BERT通过它们之间的交互聚合文档信息来查询令牌表示,但提取文档令牌的查询无关表示。这表明将BERT转换为更有效的以表示为中心的模型是可能的。这些发现有助于我们更好地理解BERT的排名过程,并可能启发未来的改进。
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引用次数: 30
Nonintrusive-Sensing and Reinforcement-Learning Based Adaptive Personalized Music Recommendation 基于非侵入式感知和强化学习的自适应个性化音乐推荐
Daocheng Hong, Yangmei Li, Qiwen Dong
As a particularly prominent application of recommender systems on automated personalized service, the music recommendation has been widely used in various music network platforms, music education and music therapy. Importantly, the individual music preference for a certain moment is closely related to personal experience of the music and music literacy, as well as temporal scenario without any interruption. Therefore, this paper proposes a novel policy for music recommendation NRRS (Nonintrusive-Sensing and Reinforcement-Learning based Recommender Systems) by integrating prior research streams. Specifically, we develop a novel recommendation framework for sensing, learning and adaptation to user's current preference based on wireless sensing and reinforcement learning in real time during a listening session. The established music recommendation prototype monitors individual vital signals for listening music, and captures song characters, individual dynamic preferences, and that it can yield better listening experience for users.
音乐推荐作为推荐系统在自动化个性化服务上的一个特别突出的应用,已经广泛应用于各种音乐网络平台、音乐教育、音乐治疗等领域。重要的是,个人对某一时刻的音乐偏好与个人对音乐的体验和音乐素养密切相关,也与没有任何中断的时间情景密切相关。因此,本文通过整合前人的研究成果,提出了一种新的基于非侵入式感知和强化学习的音乐推荐系统策略。具体来说,我们开发了一个新的推荐框架,用于在听力会话期间实时基于无线传感和强化学习的用户当前偏好的感知、学习和适应。已建立的音乐推荐原型可以监控个人听音乐的重要信号,捕捉歌曲特征、个人动态偏好,从而为用户提供更好的听音乐体验。
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引用次数: 11
期刊
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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