Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases

Bhutani, Nikita; Zheng, Xinyi; Qian, Kun; Li, Yunyao; Jagadish, H. V.

doi:10.18653/v1/2020.nli-1.1

Cited by 19 publications

(13 citation statements)

References 26 publications

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“…Um dos principais problemas da RNN é a queda no desempenho para sequências mais longas e complexas. Para resolvê-lo, trabalhos recentes utilizam o mecanismo de atenc ¸ão para enfatizar as partes mais relevantes de uma NLQ e preservar o contexto das sentenc ¸as [Bhutani et al 2019, Ding et al 2019, Tong et al 2019, Bhutani et al 2020. Embora RNNs sejam amplamente utilizadas, Redes de Memória [Miller et al 2016, Hao et al 2019, Saha et al 2018, Hua et al 2020b, Hua et al 2020a] e Redes Neurais Convolucionais [Hu et al 2018, Bao et al 2016] podem ser usadas nesta etapa.…”

Section: Representac ¸ãO Das Perguntas E Gerac ¸ãO De Candidatosunclassified

“…Nesse caso, é necessário realizar consultas avanc ¸adas para coletar a resposta dos KBs, como a explorac ¸ão de relac ¸ões indiretas entre entidades, multirelac ¸ões, restric ¸ões qualitativas e quantitativas, entre outras [Bao et al 2016]. Atualmente, os sistemas de QA obtêm melhores resultados ao responder a perguntas simples e, por conta disso, os sistemas de QA para perguntas complexas estão recebendo atenc ¸ão [Ding et al 2019, Bhutani et al 2020.…”

Section: Introduc ¸ãOunclassified

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Uma revisão breve sobre perguntas complexas em bases de conhecimento para sistemas de perguntas e respostas

Gomes

Mello

Reis

et al. 2021

Anais Do XIII Simpósio Brasileiro De Tecnologia Da Informação E Da Linguagem Humana (STIL 2021)

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O avanço nos sistemas de Question Answering alcançou resultados importantes e novos problemas relacionados, como Complex Question Answering e Knowledge Base Question Answering, surgiram. No entanto, faltam estudos que analisam o problema e abordagens para Complex Knowledge Base Question Answering (C-KBQA). Este trabalho preenche essa lacuna apresentando uma visão geral do C-KBQA. Uma coleção de 54 artigos foi selecionada e um mapa dos métodos, abordagens, tendências e lacunas sobre C-KBQA foi realizado. É mostrado que as questões de múltiplos saltos e restritivas são os dois tipos de questões abordadas na literatura. Três etapas foram identificadas para criar um sistema C-KBQA e duas abordagens são geralmente usadas.

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Section: Representac ¸ãO Das Perguntas E Gerac ¸ãO De Candidatosunclassified

Section: Introduc ¸ãOunclassified

Uma revisão breve sobre perguntas complexas em bases de conhecimento para sistemas de perguntas e respostas

Gomes

Mello

Reis

et al. 2021

Anais Do XIII Simpósio Brasileiro De Tecnologia Da Informação E Da Linguagem Humana (STIL 2021)

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“…The sub-query graphs generated sequentially by MULTIQUE for an example question "What college did the author of 'The Hobbit' attend?" [5]. tackle the multi-hop relation questions, instead of generating the whole query graph at once, MULTIQUE [5] breaks the original question into simple partial queries and builds sub query graphs for partial queries one by one.…”

Section: Neural-enhanced Symbolic Reasoningmentioning

confidence: 99%

“…[5]. tackle the multi-hop relation questions, instead of generating the whole query graph at once, MULTIQUE [5] breaks the original question into simple partial queries and builds sub query graphs for partial queries one by one. The search space is shrinked since each time when extending the whole query graph by a new sub-query graph, the model only needs to consider the immediate answers queried by the previous most matched sub-query graph.…”

Section: Neural-enhanced Symbolic Reasoningmentioning

confidence: 99%

Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs

Zhang

Chen

Zhang

et al. 2020

Preprint

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Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval and recommendation. Since knowledge graph can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques. However, symbolic reasoning is intolerant of the ambiguous and noisy data. On the contrary, the recent advances of deep learning promote neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning. Considering the advantages and disadvantages of both methodologies, recent efforts have been made on combining the two reasoning methods. In this survey, we take a thorough look at the development of the symbolic reasoning, neural reasoning and the neural-symbolic reasoning on knowledge graphs. We survey two specific reasoning tasks, knowledge graph completion and question answering on knowledge graphs, and explain them in a unified reasoning framework. We also briefly discuss the future directions for knowledge graph reasoning.

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“…Papers' Selection: The research papers reviewed in this survey are high quality papers selected from the top NLP and AI conferences, including but not limited to, ACL 4 , SIGIR 5 , NeurIPS 6 , NAACL 7 , EMNLP 8 , ICLR 9 , AAAI 10 , IJCAI 11 CIKM 12 , SIGKDD 13 , and WSDM 14 . Other than published research papers in the aforementioned conferences, we have also considered good papers in e-Print archive 15 as they manifest the latest research outputs.…”

Section: Introductionmentioning

confidence: 99%

Conversational Question Answering: A Survey

Zaib¹,

Zhang²,

Sheng³

et al. 2021

Preprint

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Question answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on Conversational Question Answering (CQA), wherein a system is required to understand the given context and then engages in multi-turn QA to satisfy a user's information needs. Whilst the focus of most of the existing research work is subjected to single-turn QA, the field of multi-turn QA has recently grasped attention and prominence owing to the availability of large-scale, multiturn QA datasets and the development of pre-trained language models. With a good amount of models and research papers adding to the literature every year recently, there is a dire need of arranging and presenting the related work in a unified manner to streamline future research. This survey, therefore, is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers from 2016-2021. Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives. This survey is intended to provide an epitome for the research community with the hope of laying a strong foundation for the field of CQA.

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Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases

Cited by 19 publications

References 26 publications

Uma revisão breve sobre perguntas complexas em bases de conhecimento para sistemas de perguntas e respostas

Uma revisão breve sobre perguntas complexas em bases de conhecimento para sistemas de perguntas e respostas

Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs

Conversational Question Answering: A Survey

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