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RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL [PDF]
AAAI Conference on Artificial Intelligence, 2023 One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i.e., tables and columns) and the skeleton ...
Haoyang Li +3 more
semanticscholar +1 more source
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding [PDF]
International Conference on Machine Learning, 2022 Visually-situated language is ubiquitous -- sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms.
Kenton Lee +9 more
semanticscholar +1 more source
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing [PDF]
AAAI Conference on Artificial Intelligence, 2023 The task of text-to-SQL parsing, which aims at converting natural language questions into executable SQL queries, has garnered increasing attention in recent years. One of the major challenges in text-to-SQL parsing is domain generalization, i.e., how to
Jinyang Li +9 more
semanticscholar +1 more source
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models [PDF]
Conference on Empirical Methods in Natural Language Processing, 2021 Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10,000s of sub-word tokens. When fine-tuned to target constrained formal languages like SQL, these models often generate
Torsten Scholak +2 more
semanticscholar +1 more source
LILAC: Log Parsing using LLMs with Adaptive Parsing Cache [PDF]
Proc. ACM Softw. Eng., 2023 Log parsing transforms log messages into structured formats, serving as the prerequisite step for various log analysis tasks. Although a variety of log parsing approaches have been proposed, their performance on complicated log data remains compromised ...
Zhihan Jiang +8 more
semanticscholar +1 more source
Log-based Anomaly Detection Without Log Parsing [PDF]
International Conference on Automated Software Engineering, 2021 Software systems often record important runtime information in system logs for troubleshooting purposes. There have been many studies that use log data to construct machine learning models for detecting system anomalies.
Van-Hoang Le, Hongyu Zhang
semanticscholar +1 more source
Polo: Adaptive Trie-Based Log Parser for Anomaly Detection
Mathematics, 2023 Automated log parsing is essential for many log-mining applications, as logs provide a vast range of information on events and variations within an operating system or software at runtime.
Yuezhou Zhou, Yuxin Su
doaj +1 more source
Pyramid Scene Parsing Network [PDF]
Computer Vision and Pattern Recognition, 2016 Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the ...
Hengshuang Zhao +4 more
semanticscholar +1 more source
TaPas: Weakly Supervised Table Parsing via Pre-training [PDF]
Annual Meeting of the Association for Computational Linguistics, 2020 Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms ...
Jonathan Herzig +4 more
semanticscholar +1 more source
Named Entity Recognition as Dependency Parsing [PDF]
Annual Meeting of the Association for Computational Linguistics, 2020 Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity
Juntao Yu, Bernd Bohnet, Massimo Poesio
semanticscholar +1 more source