K a FSP : Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base (2022)

Abstract: In this paper, we study two issues of semantic parsing approaches to conversational question answering over a large-scale knowledge base: (1) The actions defined in grammar are not sufficient to handle uncertain reasoning common in real-world scenarios. (2) Knowledge base information is not well exploited and incorporated into semantic parsing. To mitigate the two issues, we propose a knowledge-aware fuzzy semantic parsing framework (KaFSP). It defines fuzzy comparison operations in the grammar system for uncertain reasoning based on the fuzzy set theory. In order to enhance the interaction between semantic parsing and knowledge base, we incorporate entity triples from the knowledge base into a knowledge-aware entity disambiguation module. Additionally, we propose a multi-label classification framework to not only capture correlations between entity types and relations but also detect knowledge base information relevant to the current utterance. Both enhancements are based on pre-trained language models. Experiments on a large-scale conversational question answering benchmark demonstrate that the proposed KaFSP achieves significant improvements over previous state-of-the-art models, setting new SOTA results on 8 out of 10 question types, gaining improvements of over 10% F1 or accuracy on 3 question types, and improving overall F1 from 83.01% to 85.33%. The source code of KaFSP is available at https://github.com/tjunlp-lab/KaFSP .

## introduction with the growing popularity of intelligent virtual assistants (e.g., alexa, siri, cortana) and the availability of large-scale knowledge bases (e.g., dbpedia @xcite , wikidata @xcite , yago @xcite ), conversational question answering (qa) over knowledge bases (kb) has attracted broad interests. it aims to satisfy users' information needs by retrieving answers from a given knowledge graph to users' questions in a multi-turn conversational setting with a wide range of discourse phenomena (e.g., ellipsis, coreference, lexical cohesion). while conversational qa over large-scale kbs can be realized without explicit semantic parsing (e.g., hred-kvm @xcite ), the majority of effort is dedicated to the exploration of contextual semantic parsers @xcite @xcite @xcite . the semantic parsing based approaches usually project an utterance into a logical form that can be executed on a given knowledge base. early semantic parsing method d2a @xcite suffers from the stepwise error propagation issue, which is improved by masp @xcite that jointly learns pointer-equipped semantic parsing and typeaware entity detection in a multi-task learning framework. the very recent work lasagne @xcite further enhances masp via a graph attention network that exploits the correlation (missing in masp) between entity types and relations and achieves the state-of-the-art results on the csqa benchmark @xcite . despite the aforementioned progress, we argue that current semantic parsing approaches to conversational qa over large-scale kbs still suffer from two critical issues. first, grammar rules that form the base for the mapping of questions to logical forms, although being constantly updated in d2a, masp, and lasagne, are still not sufficient to cover all real-world situations, e.g., fuzzy inference on numbers. consider the question "which nutrients can interact with approximately 89 chemical substances and drugs?". it is difficult for existing grammar to represent "approximately 89". second, the interaction between questions and knowledge base is not adequate for entity disambiguation and redundancy detection in semantic parsing. for the question "which educational institution is the alma mater of pierre lefebvre?", without using relevant information from kb, it is difficult for semantic parsing to distinguish whether "pierre lefebvre" is a french military physician or a french politician as more than one persons named "pierre lefebvre" are in the knowledge base. to address these two issues, we propose a knowledge-aware fuzzy semantic parsing (kafsp) model to enhance both grammar rules and the interaction between kb and semantic parsing. particularly, we introduce fuzzy operations into the grammar system used in previous work, enabling the system to perform uncertainty reasoning on numbers. such updates have a significant impact on answering quantitative and comparative questions. in order to make the knowledge base well facilitate semantic parsing, we incorporate deep entity knowledge in the given knowledge base into different modules in the proposed semantic parsing framework. in the entity disambiguation module, entity triples from the knowledge base are exploited to disambiguate candidate entities. in the entity type and relation prediction module, a multi-label classification framework is proposed to capture correlations between entity types and relations and to pinpoint kb information relevant to the current utterance. contributions our main contributions are as follows: recent years have witnessed that semantic parsing has been shifted from traditional statistical models with feature engineering to neural approaches that learn continuous representations for generating logical forms @xcite @xcite @xcite @xcite @xcite . for example, @xcite use the encoder-decoder framework equipped with a neural attention mechanism to cast semantic parsing into seq2seq generation. as knowledge bases are becoming large, semantic parsing for kbqa is usually performed in a stepwise, modular framework. @xcite recognize entities in questions and link them to the given large-scale knowledge graph at the first stage and then learn to map the entity-linked questions into logical forms. @xcite propose a coarse-to-fine two-stage decoding method for semantic parsing, which generates a coarse sketch for a question with low-level features at the first stage and then continues to decode the final logical form based on the output of the first stage as well as the question itself. as mentioned in section 1, such stepwise methods are confronted with error propagation across stages (e.g., from entity linking to mapping, from coarse parse to fine parse). in order to alleviate such problem, @xcite and @xcite use a multi-task learning framework to jointly learn entity detection, linking, and semantic parsing in a single model. @xcite al. (2021) also use a graph attention network @xcite to explore entity type and relation information in the knowledge base. due to the superiority of multi-task learning for semantic parsing tailored for kbqa, our work is also based on the multi-task learning framework. however, our model is significantly different from existing works in both fuzzy grammar rules and knowledge-aware entity disambiguation together with entity type and relation prediction. ## kafsp we use a multi-task learning framework to map an input (current question concatenated with context) into a logical form where entities are detected and linked to the given knowledge base. figure 1 shows the architecture of kafsp. the backbone network of kafsp follows lasagne @xcite consisting of a seq2seq network, an entity recognition module and a graph attention network module (section 3.2). our contributions lie in the fuzzy grammar (section 3.1), the knowledgeaware entity disambiguation module (section 3.3), and the entity type and relation prediction module (section 3.4). the two knowledge-aware modules are shown in the black dashed box in figure 1 . ## experiments we carried out experiments and analyses to validate the effectiveness of the proposed kafsp. ## conclusion in this paper, we have presented a knowledgeaware fuzzy semantic parsing framework kafsp for conversational question answering over a largescale knowledge base. kafsp defines fuzzy comparison actions in grammar based on the fuzzy set theory to cover approximately comparative reasoning. in addition to this, we propose two knowledgeaware components in kafsp to incorporate information from the knowledge base for entity disambiguation and entity type & relation prediction. experiment results demonstrate that kafsp is substantially better than all previous state-of-the-art models, setting new sota results on 8 out of 10 question types on the csqa dataset and achieving over 90% f1 or accuracy in 3 question types for the first time.