Paper Reading AI Learner

Semantic Parsing for Conversational Question Answering over Knowledge Graphs

2023-01-28 14:45:11
Laura Perez-Beltrachini, Parag Jain, Emilio Monti, Mirella Lapata

Abstract

In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at this https URL.

Abstract (translated)

在本文中,我们旨在开发语义解析器,能够理解在与用户的交谈中嵌入的自然语言问题,并将其转化为对通用知识图(KG)中定义的正式查询。为此,我们开发了一个有数千个概念名称和关系、数百万个实体的巨大词汇表的数据集,并将用户问题进行词法分析注释,并将系统回答与执行结果对应起来。我们介绍了两种不同的语义解析方法,并突出了任务的挑战:处理巨大的词汇表、建模对话上下文、预测包含多个实体的查询,并在测试时将其 generalise 到新的问题。我们希望我们的数据集将成为开发对话语义解析器有用的测试平台。我们的数据和模型都发布在这个 https URL 上。

URL

https://arxiv.org/abs/2301.12217

PDF

https://arxiv.org/pdf/2301.12217.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot