Question: What is the difference between KBMRC and KBQA?
Evidence: The task of KBMRC differs from machine reading comprehension (MRC) in both input and output aspects. The input of KBMRC is the knowledge including both word knowledge extracted from the document and world knowledge retrieved from external knowledge base, while the input of MRC is the unstructured text of a document. The output of KBMRC is a subject or an argument, while the output in MRC is a text span of the document. Meanwhile, KBMRC facilitates the accessing and leveraging of knowledge from external KBs because the document KB is consistent with the representation of facts in external KBs.
KBMRC also relates to knowledge-base question answering (KBQA) BIBREF23 , which aims to answer questions based on an external large-scale KB such as Freebase or ProBase. KBMRC differs from KBQA in that the original KB comes from the content of a document. External KB is used in this work to enhance the document KB. Moreover, existing benchmark datasets for KBQA such as WebQuestions BIBREF24 are typically limited to simple questions. The KBMRC task requires reasoning over two facts from the document KB.
Our approach draws inspiration from two main classes in existing approaches of KBQA, namely ranking based and parsing based. Ranking based approaches BIBREF17 , BIBREF25 are bottom-up, which typically first find a set of candidate answers and then rank between the candidates with features at different levels to get the answer. Parsing-based approaches BIBREF16 are top-down, which first interpret logical form from a natural language utterance, and then do execution to yield the answer. Ranking-based approaches achieve better performances than parsing-based approaches on WebQuestions, a benchmark dataset for KBQA. We follow ranking-based approaches, and develop both a matching-based model with features at different levels and a question generation model. More references can be found at https://aclweb.org/aclwiki/Question_Answering_(State_of_the_art).
Our work also relates to BIBREF21 khot2017answering, which uses open IE outputs from external text corpora to improve multi-choice question answering. However, our work differs from them in that their task does not contain document information. Furthermore, we develop a question generation approach while they regard the QA task as subgraph search based on an integer linear programming (ILP) approach. Our work also relates to BIBREF26 khashabi2018question, which focuses on multi-choice question answering based on the semantics of a document. They use semantic role labeling and shallow parsing of a document to construct a semantic graph, based on which an ILP based approach is developed to find the supporting subgraph. The difference of our approach is that predicates from our document KB form are not limited to a predefined set, so that they do not take into consideration the knowledge from external KBs, and also the difference in terms of methodology. BIBREF19 miller2016key answer questions based on KBs in the movie domain or information extraction results from Wikipedia documents. Unlike this method, our approach focuses on entities from an external KB, our doc KB is obtained via open IE, and we combine the document KB with an open KB for question answering.
KBMRC also relates to knowledge-base question answering (KBQA) (Yih et al. 2015), which aims to answer questions based on an external large-scale KB such as Freebase or ProBase. KBMRC differs from KBQA in that the original KB comes from the content of a document. External KB is used in this work to enhance the document KB. Moreover, existing benchmark datasets for KBQA such as WebQuestions (Berant et al. 2013) are typically limited to simple questions. The KBMRC task requires reasoning over two facts from the document KB.
Answer: KBMRC also relates to knowledge-base question answering (KBQA) (Yih et al. 2015), which aims to answer questions based on an external large-scale KB such as Freebase or ProBase. KBMRC differs from KBQA in that the original KB comes from the content of a document. External KB is used in this work to enhance the document KB. Moreover, existing benchmark datasets for KBQA such as WebQuestions (Berant et al. 2013) are typically limited to simple questions. The KBMRC task requires reasoning over two facts from the document KB.