NAMER : A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering (2021)

Abstract: We present NAMER, an open-domain Chinese knowledge base question answering system based on a novel node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question. Equipped with techniques including data augmentation and multitasking, we show that the proposed framework outperforms the previous SoTA on CCKS CKBQA dataset. Moreover, we develop a novel data annotation strategy that facilitates the node-to-mention alignment, a dataset ( https://github.com/ridiculouz/CKBQA ) with such strategy is also published to promote further research. An online demo of NAMER ( http://kbqademo.gstore.cn ) is provided to visualize our framework and supply extra information for users, a video illustration ( https://youtu.be/yetnVye_hg4 ) of NAMER is also available.

## introduction with the rapid popularization of knowledge bases (kb), knowledge base question answering (kbqa) @xcite has witnessed much research effort to fulfill a robust system to simplify users' access to kbs. for any given factoid question in natural language, kbqa system utilizes its background kb for answers. recently, many sota kbqa systems adopt a semantic parsing @xcite framework, in which they convert the question to a kb query (e.g. sparql, @xcite to get answers. since queries are highly structured, a robust kbqa system needs to grasp the structural mapping (figure 1 ) between a question and its query. however, most previous works either adopted an end-to-end model that failed to directly use such mappings @xcite or devised a template or rule-based pipeline @xcite structuralmapping originalquestion whichschooldidyaoming's daughtergraduatefrom? sparql select?ywhere{ <@xmath0. ?@xmath1.} ?y ?@xmath2: structural mapping between a question and its corresponding sparql query. @xcite that may lose generality in realworld applications. to preserve generality, @xcite incorporated a pointer generator @xcite into the pipeline to learn the mapping between an entity and its mention. nevertheless, the system failed to utilize the mappings of variables, literals, and types in a query. in this paper, we argue that learning the complete question-query mapping (i.e. the alignments of all nodes to their mention, as in figure 1 ) aids the system to achieve better performance. hence, we supplement an open-domain complex chinese kbqa dataset with annotations of all node mentions. based on the additional data, we propose a novel node-based multi-hop kbqa framework that fully grasps the mappings of entities, variables, literals, and types. unlike prior works, we generate the pointer of all query nodes to their mention to represent the mapping and exploit such mappings in downstream relation extraction task. also, we explore techniques including multitasking to further improve model performance. based on the framework, we implement a publicly available chinese kbqa system, namer, for users to query kbs by natural language, which offers convenience for non-expert users to use kb and is thus fairly useful in practice. in short, the contributions of this work are: 1) we propose a novel kbqa framework with a strong ability to grasp structural mapping, nodeextraction mention detection entity linking relationextraction candidate relationgenerator relation ranking select?⼥⼉where{<姚明><⼥⼉>?⼥⼉.} sparql amy_(姚明⼥⼉) answer querygeneration nodementionpointergenerator nodetypeclassifier transformer decoder 姚明的⼥⼉是谁？ (whoisyaoming's daughter?) inputquestion knowledgebase pkubase user inputquestion intermediateresults sparql&answer(s) namerkbqasystem figure 2: architecture of the proposed system the approach reaches sota results on a chinese kbqa dataset, 2) we present a new data annotation format to better train kbqa models and publish a supplementary dataset of this format to prompt future research and 3) we implement an online demonstration of namer that can visualize our framework and aid users to explore kbs. ## system overview this section explains the overall architecture of the proposed framework and the ui of the system. ## model consider the question "where was yao ming's daughter born?", the following section elaborates how each module process the question to compose the correct sparql "select ?y where { <@xmath3. ?@xmath4. }". ## conclusion we present a robust chinese kbqa system, namer, based on a novel node-based multitasking framework. with three cooperative modules, our system grasps the structural mapping between a question and its corresponding query. hence, namer reaches superior performance compared to previous sota on an open-domain chinese complex kbqa dataset. further experiments also demonstrate the effectiveness of the architecture and the techniques adopted in namer. as a system intended for easier access to kb for all users, the ui of namer provides not only the answers to a given question but also the query structure accompanied by a series of intermediate results (e.g. candidates & scores), assisting users to visualize our system pipeline and explore more kb information to their interest. for the future, we will incorporate more visualization functions into namer to further reduce the barrier to kb for nonspecialist users. since extra data annotations are required to support namer, we also plan to study the effects of the scale of annotated data on system performance. moreover, we expect to implement and optimize namer in multilingual scenarios.