Conversational Question Answering on Heterogeneous Sources

Conversational question answering (ConvQA) tackles sequential informationneeds where contexts in follow-up questions are left implicit. Current ConvQAsystems operate over homogeneous sources of information: either a knowledgebase (KB), or a text corpus, or a collection of tables. This paper addressesthe novel issue of jointly tapping into all of these together, this wayboosting answer coverage and confidence. We present CONVINSE, an end-to-endpipeline for ConvQA over heterogeneous sources, operating in three stages: i)learning an explicit structured representation of an incoming question and itsconversational context, ii) harnessing this frame-like representation touniformly capture relevant evidences from KB, text, and tables, and iii)running a fusion-in-decoder model to generate the answer. We construct andrelease the first benchmark, ConvMix, for ConvQA over heterogeneous sources,comprising 3000 real-user conversations with 16000 questions, along with entityannotations, completed question utterances, and question paraphrases.Experiments demonstrate the viability and advantages of our method, compared tostate-of-the-art baselines.<br

Complex Knowledge Base Question Answering: A Survey

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performance on complex questions is still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances on KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and relevant background. Then, we describe benchmark datasets for complex KBQA task and introduce the construction process of these datasets. Next, we present two mainstream categories of methods for complex KBQA, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods. Specifically, we illustrate their procedures with flow designs and discuss their major differences and similarities. After that, we summarize the challenges that these two categories of methods encounter when answering complex questions, and explicate advanced solutions and techniques used in existing work. Finally, we conclude and discuss several promising directions related to complex KBQA for future research.Comment: 20 pages, 4 tables, 7 figures. arXiv admin note: text overlap with arXiv:2105.1164

CompMix: A Benchmark for Heterogeneous Question Answering

Fact-centric question answering (QA) often requires access to multiple, heterogeneous, information sources. By jointly considering several sources like a knowledge base (KB), a text collection, and tables from the web, QA systems can enhance their answer coverage and confidence. However, existing QA benchmarks are mostly constructed with a single source of knowledge in mind. This limits capabilities of these benchmarks to fairly evaluate QA systems that can tap into more than one information repository. To bridge this gap, we release CompMix, a crowdsourced QA benchmark which naturally demands the integration of a mixture of input sources. CompMix has a total of 9,410 questions, and features several complex intents like joins and temporal conditions. Evaluation of a range of QA systems on CompMix highlights the need for further research on leveraging information from heterogeneous sources

Question Answering over Curated and Open Web Sources

The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language processing, and artificial intelligence. This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithms that are currently being used. We partition research contributions by the underlying source from where answers are retrieved: curated knowledge graphs, unstructured text, or hybrid corpora. We choose this dimension of partitioning as it is the most discriminative when it comes to algorithm design. Other key dimensions are covered within each sub-topic: like the complexity of questions addressed, and degrees of explainability and interactivity introduced in the systems. We would conclude the tutorial with the most promising emerging trends in the expanse of QA, that would help new entrants into this field make the best decisions to take the community forward. Much has changed in the community since the last tutorial on QA in SIGIR 2016, and we believe that this timely overview will indeed benefit a large number of conference participants

Answering Count Questions with Structured Answers from Text

In this work we address the challenging case of answering count queries in web search, such as ``number of songs by John Lennon''. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries, including existing benchmark show the benefits of our method, and the influence of specific parameter settings. Our code, data and an interactive system demonstration are publicly available at https://github.com/ghoshs/CoQEx and https://nlcounqer.mpi-inf.mpg.de/

Systematic review of question answering over knowledge bases

Over the years, a growing number of semantic data repositories have been made available on the web. However, this has created new challenges in exploiting these resources efficiently. Querying services require knowledge beyond the typical user’s expertise, which is a critical issue in adopting semantic information solutions. Several proposals to overcome this dif- ficulty have suggested using question answering (QA) systems to provide user‐friendly interfaces and allow natural language use. Because question answering over knowledge bases (KBQAs) is a very active research topic, a comprehensive view of the field is essential. The purpose of this study was to conduct a systematic review of methods and systems for KBQAs to identify their main advantages and limitations. The inclusion criteria rationale was English full‐text articles published since 2015 on methods and systems for KBQAs.info:eu-repo/semantics/publishedVersio