Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results. Current approaches build universal paraphrasing or ranking models for the whole questions, which are likely to fail in generating queries for complex, long-tail questions. In this paper, we propose SubQG, a new query generation approach based on frequent query substructures, which helps rank the existing (but nonsignificant) query structures or build new query structures. Our experiments on two benchmark datasets show that our approach significantly outperforms the existing ones, especially for complex questions. Also, it achieves promising performance with limited training data and noisy entity/relation linking results.
Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significantly different. Span decoders are frequently used for question answering, fixed-class, classification layers for text classification, and similarity-scoring layers for regression tasks, We show that this distinction is not necessary and that all three can be unified as span extraction. A unified, span-extraction approach leads to superior or comparable performance in supplementary supervised pre-trained, low-data, and …
The University of Tokyo（东京大学）；National Institute of Informatics（国立信息学研究所）
Question answering (QA) models for reading comprehension have been demonstrated to exploit unintended dataset biases such as question-context lexical overlap. This hinders QA models from generalizing to under-represented samples such as questions with low lexical overlap. Question generation (QG), a method for augmenting QA datasets, can be a solution for such performance degradation if QG can properly debias QA datasets. However, we discover that recent neural QG models are biased towards generating questions with high lexical overlap, which can amplify the dataset bias. Moreover, our analysi…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Tsinghua University（清华大学）
Product Community Question Answering (PCQA) provides useful information about products and their features (aspects) that may not be well addressed by product descriptions and reviews. We observe that a product's compatibility issues with other products are frequently discussed in PCQA and such issues are more frequently addressed in accessories, i.e., via a yes/no question "Does this mouse work with windows 10?". In this paper, we address the problem of extracting compatible and incompatible products from yes/no questions in PCQA. This problem can naturally have a two-stage framework: first, w…
Knowledge Base Question Answering (KBQA) tasks that involve complex reasoning are emerging as an important research direction. However, most existing KBQA datasets focus primarily on generic multi-hop reasoning over explicit facts, largely ignoring other reasoning types such as temporal, spatial, and taxonomic reasoning. In this paper, we present a benchmark dataset for temporal reasoning, TempQA-WD, to encourage research in extending the present approaches to target a more challenging set of complex reasoning tasks. Specifically, our benchmark is a temporal question answering dataset with the…
Developing Question Answering systems has been one of the important research issues because it requires insights from a variety of disciplines,including,Artificial Intelligence,Information Retrieval, Information Extraction,Natural Language Processing, and Psychology.In this paper we realize a formal model for a lightweight semantic based open domain yes/no Arabic question answering system based on paragraph retrieval with variable length. We propose a constrained semantic representation. Using an explicit unification framework based on semantic similarities and query expansion synonyms and ant…
Table Question Answering (TQA) is an important but under-explored task. Most of the existing QA datasets are in unstructured text format and only few of them use tables as the context. To the best of our knowledge, none of TQA datasets exist in the biomedical domain where tables are frequently used to present information. In this paper, we first curate a table question answering dataset, BioTABQA, using 22 templates and the context from a biomedical textbook on differential diagnosis. BioTABQA can not only be used to teach a model how to answer questions from tables but also evaluate how a mod…
Most Outside-Knowledge Visual Question Answering (OK-VQA) systems employ a two-stage framework that first retrieves external knowledge given the visual question and then predicts the answer based on the retrieved content. However, the retrieved knowledge is often inadequate. Retrievals are frequently too general and fail to cover specific knowledge needed to answer the question. Also, the naturally available supervision (whether the passage contains the correct answer) is weak and does not guarantee question relevancy. To address these issues, we propose an Entity-Focused Retrieval (EnFoRe) mo…
Question answering (QA) aims to understand questions and find appropriate answers. In real-world QA systems, Frequently Asked Question (FAQ) based QA is usually a practical and effective solution, especially for some complicated questions (e.g., How and Why). Recent years have witnessed the great successes of knowledge graphs (KGs) in KBQA systems, while there are still few works focusing on making full use of KGs in FAQ-based QA. In this paper, we propose a novel Knowledge Anchor based Question Answering (KAQA) framework for FAQ-based QA to better understand questions and retrieve more approp…
IBM Research（IBM研究院）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；IIT Bombay（印度理工学院孟买分校）
Recent advances in transformers have enabled Table Question Answering (Table QA) systems to achieve high accuracy and SOTA results on open domain datasets like WikiTableQuestions and WikiSQL. Such transformers are frequently pre-trained on open-domain content such as Wikipedia, where they effectively encode questions and corresponding tables from Wikipedia as seen in Table QA dataset. However, web tables in Wikipedia are notably flat in their layout, with the first row as the sole column header. The layout lends to a relational view of tables where each row is a tuple. Whereas, tables in domai…
Existing tools for Question Answering (QA) have challenges that limit their use in practice. They can be complex to set up or integrate with existing infrastructure, do not offer configurable interactive interfaces, and do not cover the full set of subtasks that frequently comprise the QA pipeline (query expansion, retrieval, reading, and explanation/sensemaking). To help address these issues, we introduce NeuralQA - a usable library for QA on large datasets. NeuralQA integrates well with existing infrastructure (e.g., ElasticSearch instances and reader models trained with the HuggingFace Tran…
Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation
University of Bonn（波恩大学）；Lamarr-Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习和人工智能研究所）；Universitat Rovira i Virgili（罗维拉-维尔吉利大学）；Fraunhofer IAIS（弗劳恩霍夫智能分析和信息系统研究所）
The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area. In this paper, we show that question rewriting (QR) of the conversational context allows to shed more light on this phenomenon and also use it to evaluate robustness of different answer selection approaches. We introduce a simple framework that enables an automated analysis of the conversational question answering (QA) performance using question rewrites, and present the results of this analysis on the TREC CAsT and QuAC (CANARD) datasets. Our experiments unco…
Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems
University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Colorado Denver（科罗拉多大学丹佛分校）；Memorial Healthcare System（纪念医疗系统）；Johannes Gutenberg University Mainz（美因茨约翰内斯·古腾堡大学）
Hybrid Retrieval-Augmented Generation Agent for Trustworthy Legal Question Answering in Judicial Forensics
Department of Electronic Engineering, City University of Hong Kong(DongGuan)（香港城市大学（东莞）电子工程系）；Department of Electronic Engineering, City University of Hong Kong（香港城市大学电子工程系）
Multi-modal Record Linkage is the process of matching multi-modal records from multiple sources that represent the same entity. This field has not been explored in research and we propose two solutions based on Deep Learning architectures that are inspired by recent work in Visual Question Answering. The neural networks we propose use two different fusion modules, the Recurrent Neural Network + Convolutional Neural Network fusion module and the Stacked Attention Network fusion module, that jointly combine the visual and the textual data of the records. The output of these fusion models is the …
Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private
Computer Science and Engineering University of California, San Diego（计算机科学与工程大学加州大学圣地亚哥分校）；Halıcıoğlu Data Science Institute University of California, San Diego（Halıcıoğlu数据科学研究所加州大学圣地亚哥分校）；Department of Computer Science University of California, Los Angeles（计算机科学系加州大学洛杉矶分校）
SCRA-VQA: Summarized Caption-Rerank for Augmented Large Language Models in Visual Question Answering
School of Computer Science, Hubei University, Wuhan, China（湖北大学计算机学院）；Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University)（湖北大数据智能分析与应用重点实验室）；Key Laboratory of Intelligent Sensing System and Security (Hubei University)（智能传感系统与安全重点实验室）；Institute of Vocational Education, Guangdong Industry Polytechnic University（广东行业职业大学职业教育学院）；Shandong Police College（山东警察学院）
Query2Diagram: Answering Developer Queries with UML Diagrams
HSE University（俄罗斯高等经济学院）；St. Petersburg Department of Steklov Mathematical Institute, RAS（圣彼得堡斯捷克洛夫数学研究所（俄罗斯科学院））；St. Petersburg State University（圣彼得堡国立大学）
Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models
BOSS Zhipin（BOSS直聘）；University of Science and Technology of China（中国科学技术大学）；PBC School of Finance, Tsinghua University（清华大学五道口金融学院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Zhongguancun Laboratory（中关村实验室）；The Hong Kong University of Science and Technology (GuangZhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Guangzhou HKUST Fok Ying Tung Research Institute（广州港科大霍英东研究院）
Visual question answering is fundamentally compositional in nature---a question like "where is the dog?" shares substructure with questions like "what color is the dog?" and "where is the cat?" This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning *neural module networks*, which compose collections of jointly-trained neural "modules" into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and…
Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering
State Key Laboratory of Media Convergence Production Technology and Systems（媒体融合生产技术与系统国家重点实验室）；Institute of Computer Science and Technology, Peking University（北京大学计算机学院）
