Knowledge and expertise in the real-world can be disjointedly owned. To solve a complex question, collaboration among experts is often called for. In this paper, we propose CollabQA, a novel QA task in which several expert agents coordinated by a moderator work together to answer questions that cannot be answered with any single agent alone. We make a synthetic dataset of a large knowledge graph that can be distributed to experts. We define the process to form a complex question from ground truth reasoning path, neural network agent models that can learn to solve the task, and evaluation metrics to check the performance. We show that the problem can be challenging without introducing prior of the collaboration structure, unless experts are perfect and uniform. Based on this experience, we elaborate extensions needed to approach collaboration tasks in real-world settings.
Nuance Communications Deutschland GmbH（Nuance德国通信有限公司）；L3S Research Center, Leibniz University of Hannover（汉诺威莱布尼茨大学L3S研究中心）；University of Dayton（代顿大学）；Microsoft Corporation（微软公司）；University of Minnesota（明尼苏达大学）；University of Bonn（波恩大学）；Fraunhofer IAIS（弗劳恩霍夫智能分析与信息系统研究所）；TIB Leibniz Information Centre for Science and Technology（TIB莱布尼茨科学技术信息中心）
Collaborative Question Answering (CQA) frameworks for knowledge graphs aim at integrating existing question answering (QA) components for implementing sequences of QA tasks (i.e. QA pipelines). The research community has paid substantial attention to CQAs since they support reusability and scalability of the available components in addition to the flexibility of pipelines. CQA frameworks attempt to build such pipelines automatically by solving two optimisation problems: 1) local collective performance of QA components per QA task and 2) global performance of QA pipelines. In spite offering sev…
We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. It's designed to enable researchers to use natural language queries to find precise answers and extract insights from a massive amount of academic papers. We present a large improvement over classic search engine baseline on several standard QA datasets and provide the community a collaborative data collection tool to curate the first natural language processing research QA dataset via a community effort.
The amount of publicly available biomedical literature has been growing rapidly in recent years, yet question answering systems still struggle to exploit the full potential of this source of data. In a preliminary processing step, many question answering systems rely on retrieval models for identifying relevant documents and passages. This paper proposes a weighted cosine distance retrieval scheme based on neural network word embeddings. Our experiments are based on publicly available data and tasks from the BioASQ biomedical question answering challenge and demonstrate significant performance…
Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.
Multi-granularity Contrastive Cross-modal Collaborative Generation for End-to-End Long-term Video Question Answering
School of Information Science and Technology, Hangzhou Normal University（杭州师范大学信息科学与技术学院）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；School of Computer Science and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机科学与技术学院）；Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术系）
Large neural language models are steadily contributing state-of-the-art performance to question answering and other natural language and information processing tasks. These models are expensive to train. We propose to evaluate whether such pre-trained models can benefit from the addition of explicit linguistics information without requiring retraining from scratch. We present a linguistics-informed question answering approach that extends and fine-tunes a pre-trained transformer-based neural language model with symbolic knowledge encoded with a heterogeneous graph transformer. We illustrate th…
University of California, Los Angeles（加州大学洛杉矶分校）；Microsoft Cognitive Services Research（微软认知服务研究院）；University of Notre Dame（圣母大学）
Answering open-domain questions requires world knowledge about in-context entities. As pre-trained Language Models (LMs) lack the power to store all required knowledge, external knowledge sources, such as knowledge graphs, are often used to augment LMs. In this work, we propose knOwledge REasOning empowered Language Model (OREO-LM), which consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. In this way, LM guides KG to walk towards the desired answer, …
Sir M. Visvesvaraya Institute of Technology（M·维斯维斯瓦亚爵士理工学院）
In rural regions of several developing countries, access to quality healthcare, medical infrastructure, and professional diagnosis is largely unavailable. Many of these regions are gradually gaining access to internet infrastructure, although not with a strong enough connection to allow for sustained communication with a medical practitioner. Several deaths resulting from this lack of medical access, absence of patient's previous health records, and the unavailability of information in indigenous languages can be easily prevented. In this paper, we describe an approach leveraging the phenomena…
DataFactory: Collaborative Multi-Agent Framework for Advanced Table Question Answering
Institute of Systems Engineering, Academy of Military Sciences（系统工程研究所，军事科学院）；School of Information Resource Management, Renmin University of China（信息资源管理学院，中国人民大学）
The Hong Kong Polytechnic University（香港理工大学）；Shandong University（山东大学）
In the past years, Knowledge-Based Question Answering (KBQA), which aims to answer natural language questions using facts in a knowledge base, has been well developed. Existing approaches often assume a static knowledge base. However, the knowledge is evolving over time in the real world. If we directly apply a fine-tuning strategy on an evolving knowledge base, it will suffer from a serious catastrophic forgetting problem. In this paper, we propose a new incremental KBQA learning framework that can progressively expand learning capacity as humans do. Specifically, it comprises a margin-distil…
It is essential for dialogue-based spatial reasoning systems to maintain memory of historical states of the world. In addition to conveying that the dialogue agent is mentally present and engaged with the task, referring to historical states may be crucial for enabling collaborative planning (e.g., for planning to return to a previous state, or diagnosing a past misstep). In this paper, we approach the problem of spatial memory in a multi-modal spoken dialogue system capable of answering questions about interaction history in a physical blocks world setting. This work builds upon a full spatia…
Key Laboratory of Computational Linguistics, MOE, Peking University（北京大学计算语言学教育部重点实验室）；Graduate School of Informatics, Kyoto University（京都大学信息学研究生院）；University of Washington（华盛顿大学）
In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu (https://nba.hupu.com/games) website. Derived from the characteristics of sports games, LiveQA can potentially test the reasoning ability across timeline-based live broadcasts, which is challenging compared to the existing datasets. In LiveQA, the questions require understanding the timeline, tracking events or doing mathematical computations…
Explanation of an AI agent requires knowledge of its design and operation. An open question is how to identify, access and use this design knowledge for generating explanations. Many AI agents used in practice, such as intelligent tutoring systems fielded in educational contexts, typically come with a User Guide that explains what the agent does, how it works and how to use the agent. However, few humans actually read the User Guide in detail. Instead, most users seek answers to their questions on demand. In this paper, we describe a question answering agent (AskJill) that uses the User Guide …
Ubiquitous Knowledge Processing Lab (UKP Lab)（泛在知识处理实验室）；Department of Computer Science（计算机科学系）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）；Technical University of Darmstadt（达姆施塔特工业大学）
Recent years have seen impressive progress in AI-assisted writing, yet the developments in AI-assisted reading are lacking. We propose inline commentary as a natural vehicle for AI-based reading assistance, and present CARE: the first open integrated platform for the study of inline commentary and reading. CARE facilitates data collection for inline commentaries in a commonplace collaborative reading environment, and provides a framework for enhancing reading with NLP-based assistance, such as text classification, generation or question answering. The extensible behavioral logging allows uniqu…
Open Domain Question Answering (ODQA) on a large-scale corpus of documents (e.g. Wikipedia) is a key challenge in computer science. Although transformer-based language models such as Bert have shown on SQuAD the ability to surpass humans for extracting answers in small passages of text, they suffer from their high complexity when faced to a much larger search space. The most common way to tackle this problem is to add a preliminary Information Retrieval step to heavily filter the corpus and only keep the relevant passages. In this paper, we propose a more direct and complementary solution whic…
First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65%-76% accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. We train a recurrent neural network to solve each problem. On the SimpleQuestions dataset, our approach yields sub…
Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering
School of Computing and Mathematics, Manchester Metropolitan University（曼彻斯特城市大学计算与数学学院）；Electrical and Computer Engineering, Technion – Israel Institute of Technology（以色列理工学院电气与计算机工程系）；School of Science and Technology, Hong Kong Metropolitan University（香港都会大学科学与科技学院）
Zhejiang University（浙江大学）；University of Oxford（牛津大学）；The University of Edinburgh（爱丁堡大学）
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge. Most existing methods exploit pre-trained language models or/and unstructured text, but the knowledge in these resources are often incomplete and noisy. Some other methods prefer to use knowledge graphs (KGs) which often have intensive structured knowledge, but the research is still quite preliminary. In this paper, we propose LaKo, a knowledge-driven VQA method via Late Knowledge-to-text Injection. To effectively incorporate an external KG, we transfer…
ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Institute of Artificial Intelligence and Future Networks, Beijing Normal University（北京师范大学人工智能与未来网络研究院）；Faculty of Arts and Sciences, Beijing Normal University（北京师范大学文理学院）；Beijing Normal-Hong Kong Baptist University（北京师范大学-香港 Baptist大学）
CURE: Confidence-driven Unified Reasoning Ensemble Framework for Medical Question Answering
School of Information Technology and Computer Science, Nile University（信息科技与计算机科学学院，尼罗大学）；Graduate School of Information Science, University of Hyogo（信息科学研究生院，播磨大学）；Advanced Medical Engineering Research Institute, University of Hyogo（先进医疗工程研究所，播磨大学）
Heudiasyc UMR 7253（HEUDIASYC混合研究单位7253）；Université de Technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Google（谷歌）
Building computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries. Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners. In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations. By mapping answers into the same space one can query any knowledge base independent of i…
Many Question-Answering (QA) datasets contain unanswerable questions, but their treatment in QA systems remains primitive. Our analysis of the Natural Questions (Kwiatkowski et al. 2019) dataset reveals that a substantial portion of unanswerable questions ($\sim$21%) can be explained based on the presence of unverifiable presuppositions. We discuss the shortcomings of current models in handling such questions, and describe how an improved system could handle them. Through a user preference study, we demonstrate that the oracle behavior of our proposed system that provides responses based on pr…
PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements
School of Electrical and Computer Engineering, AILS Laboratory, National Technical University of Athens（电气与计算机工程学院，AILS实验室，国家技术大学雅典）；Instituto de Telecomunicações（电信研究所）
L3S Research Center（L3S研究中心）；Leibniz University of Hannover（汉诺威莱布尼茨大学）；TIB Leibniz Information Centre for Science and Technology（TIB莱布尼茨科学技术信息中心）
Answering questions on scholarly knowledge comprising text and other artifacts is a vital part of any research life cycle. Querying scholarly knowledge and retrieving suitable answers is currently hardly possible due to the following primary reason: machine inactionable, ambiguous and unstructured content in publications. We present JarvisQA, a BERT based system to answer questions on tabular views of scholarly knowledge graphs. Such tables can be found in a variety of shapes in the scholarly literature (e.g., surveys, comparisons or results). Our system can retrieve direct answers to a variet…
In this paper, we propose a method to obtain robust explanations for visual question answering(VQA) that correlate well with the answers. Our model explains the answers obtained through a VQA model by providing visual and textual explanations. The main challenges that we address are i) Answers and textual explanations obtained by current methods are not well correlated and ii) Current methods for visual explanation do not focus on the right location for explaining the answer. We address both these challenges by using a collaborative correlated module which ensures that even if we do not train …
AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；Shanghai University of Electric Power（上海电力大学）；East China Normal University（华东师范大学）；Guangdong University of Finance and Economics（广东财经大学）；Alibaba Group（阿里巴巴集团）
Korea University（高丽大学）；AstraZeneca UK（阿斯利康英国分公司）；AstraZeneca SE（阿斯利康瑞典分公司）；AIGEN Sciences Inc.（AIGEN科学公司）
Current studies in extractive question answering (EQA) have modeled the single-span extraction setting, where a single answer span is a label to predict for a given question-passage pair. This setting is natural for general domain EQA as the majority of the questions in the general domain can be answered with a single span. Following general domain EQA models, current biomedical EQA (BioEQA) models utilize the single-span extraction setting with post-processing steps. In this article, we investigate the question distribution across the general and biomedical domains and discover biomedical que…
NVIDIA；University of Maryland, College Park（马里兰大学 College Park 分校）；University of Science and Technology of China（中国科学技术大学）；Seoul National University（首尔国立大学）；Adobe
ProMQA: Question Answering Dataset for Multimodal Procedural Activity Understanding
Language Technologies Institute, Carnegie Mellon University（语言技术研究所，卡内基梅隆大学）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）
Search engines based on keyword retrieval can no longer adapt to the way of information acquisition in the era of intelligent Internet of Things due to the return of keyword related Internet pages. How to quickly, accurately and effectively obtain the information needed by users from massive Internet data has become one of the key issues urgently needed to be solved. We propose an intelligent question-answering system based on structured KB and unstructured data, called OpenQA, in which users can give query questions and the model can quickly give accurate answers back to users. We integrate K…
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering. However, most of the graph-structured data in practice suffers from incompleteness, and thus link prediction becomes an important research problem. Though many models are proposed for link prediction, the following two problems are still less explored: (1) Most methods model each link independently without making use of the rich information from relevant links, and (2) existing models are mostly designed based on assoc…
Lancaster University Leipzig（兰卡斯特大学莱比锡校区）；University of Jyväskylä（于韦斯屈莱大学）；Wuhan University（武汉大学）；University of Southern Queensland（南昆士兰大学）
Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders' perspectives, designers' intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) inherits a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of so…
