ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought

Recently Large Language Models (LLMs) have been proven to have strong abilities in various domains and tasks. We study the problem of prompt designing in the text-to-SQL task and attempt to improve the LLMs' reasoning ability when generating SQL queries. Besides the trivial few-shot in-context learning setting, we design our chain-of-thought (CoT) prompt with a similar method to schema linking. We provide a method named ACT-SQL to automatically generate auto-CoT exemplars and thus the whole process doesn't need manual labeling. Our approach is cost-saving since we only use the LLMs' API call once when generating one SQL query. Furthermore, we extend our in-context learning method to the multi-turn text-to-SQL task. The experiment results show that the LLMs' performance can benefit from our ACT-SQL approach. Our approach achieves SOTA performance on the Spider dev set among existing in-context learning approaches

Semantic Parsing with Dual Learning

Semantic parsing converts natural language queries into structured logical forms. The paucity of annotated training samples is a fundamental challenge in this field. In this work, we develop a semantic parsing framework with the dual learning algorithm, which enables a semantic parser to make full use of data (labeled and even unlabeled) through a dual-learning game. This game between a primal model (semantic parsing) and a dual model (logical form to query) forces them to regularize each other, and can achieve feedback signals from some prior-knowledge. By utilizing the prior-knowledge of logical form structures, we propose a novel reward signal at the surface and semantic levels which tends to generate complete and reasonable logical forms. Experimental results show that our approach achieves new state-of-the-art performance on ATIS dataset and gets competitive performance on Overnight dataset.Comment: Accepted by ACL 2019 Long Pape

Formation of a tiny flux rope in the center of an active region driven by magnetic flux emergence, convergence, and cancellation

Flux ropes are generally believed to be core structures of solar eruptions that are significant for the space weather, but their formation mechanism remains intensely debated. We report on the formation of a tiny flux rope beneath clusters of active region loops on 2018 August 24. Combining the high-quality multiwavelength observations from multiple instruments, we studied the event in detail in the photosphere, chromosphere, and corona. In the source region, the continual emergence of two positive polarities (P1 and P2) that appeared as two pores (A and B)is unambiguous. Interestingly, P2 and Pore B slowly approached P1 and Pore A, implying a magnetic flux convergence. During the emergence and convergence, P1 and P2 successively interacted with a minor negative polarity (N3) that emerged, which led to a continuous magnetic flux cancellation. As a result, the overlying loops became much sheared and finally evolved into a tiny twisted flux rope that was evidenced by a transient inverse S-shaped sigmoid, the twisted filament threads with blueshift and redshift signatures, and a hot channel. All the results show that the formation of the tiny flux rope in the center of the active region was closely associated with the continuous magnetic flux emergence, convergence, and cancellation in the photosphere. Hence, we suggest that the magnetic flux emergence, convergence, and cancellation are crucial for the formation of the tiny flux rope

ASTormer: An AST Structure-aware Transformer Decoder for Text-to-SQL

Text-to-SQL aims to generate an executable SQL program given the user utterance and the corresponding database schema. To ensure the well-formedness of output SQLs, one prominent approach adopts a grammar-based recurrent decoder to produce the equivalent SQL abstract syntax tree (AST). However, previous methods mainly utilize an RNN-series decoder, which 1) is time-consuming and inefficient and 2) introduces very few structure priors. In this work, we propose an AST structure-aware Transformer decoder (ASTormer) to replace traditional RNN cells. The structural knowledge, such as node types and positions in the tree, is seamlessly incorporated into the decoder via both absolute and relative position embeddings. Besides, the proposed framework is compatible with different traversing orders even considering adaptive node selection. Extensive experiments on five text-to-SQL benchmarks demonstrate the effectiveness and efficiency of our structured decoder compared to competitive baselines

International nosocomial infection control consortium (INICC) report, data summary of 36 countries, for 2004-2009

The results of a surveillance study conducted by the International Nosocomial Infection Control Consortium (INICC) from January 2004 through December 2009 in 422 intensive care units (ICUs) of 36 countries in Latin America, Asia, Africa, and Europe are reported. During the 6-year study period, using Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN; formerly the National Nosocomial Infection Surveillance system [NNIS]) definitions for device-associated health care-associated infections, we gathered prospective data from 313,008 patients hospitalized in the consortium's ICUs for an aggregate of 2,194,897 ICU bed-days. Despite the fact that the use of devices in the developing countries' ICUs was remarkably similar to that reported in US ICUs in the CDC's NHSN, rates of device-associated nosocomial infection were significantly higher in the ICUs of the INICC hospitals; the pooled rate of central line-associated bloodstream infection in the INICC ICUs of 6.8 per 1,000 central line-days was more than 3-fold higher than the 2.0 per 1,000 central line-days reported in comparable US ICUs. The overall rate of ventilator-associated pneumonia also was far higher (15.8 vs 3.3 per 1,000 ventilator-days), as was the rate of catheter-associated urinary tract infection (6.3 vs. 3.3 per 1,000 catheter-days). Notably, the frequencies of resistance of Pseudomonas aeruginosa isolates to imipenem (47.2% vs 23.0%), Klebsiella pneumoniae isolates to ceftazidime (76.3% vs 27.1%), Escherichia coli isolates to ceftazidime (66.7% vs 8.1%), Staphylococcus aureus isolates to methicillin (84.4% vs 56.8%), were also higher in the consortium's ICUs, and the crude unadjusted excess mortalities of device-related infections ranged from 7.3% (for catheter-associated urinary tract infection) to 15.2% (for ventilator-associated pneumonia). Copyright © 2012 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved

Identification of Microseismic Signals Based on Multiscale Singular Spectrum Entropy

The accurate identification of effective microseismic events has great significance in the monitoring, early warning, and forecasting of rockburst hazards. However, the conventional identification methods have displayed difficulties in achieving satisfactory results. A microseismic signal identification method which combines variational mode decomposition (VMD) and multiscale singular spectrum entropy was proposed in this paper. The original signal was firstly broken down into a given number K variational mode components, which are ranked by frequency in descending order. Then, the characteristic pattern matrix was constructed according to the mode component signals, and the identification model of the microseismic signals based on the support vector machine was built by performing a multiscale singular spectrum entropy calculation of the collected vibration signals, constructing eigenvectors of signals. Finally, a comparative analysis of the microseismic events and blasting vibration signals in the experiment proved that the different characteristics of the two kinds of signals can be fully expressed by using multiscale singular spectrum entropy. Experimental results further confirmed the effective identification performance of this proposed method

TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages

Recently, the structural reading comprehension (SRC) task on web pages has attracted increasing research interests. Although previous SRC work has leveraged extra information such as HTML tags or XPaths, the informative topology of web pages is not effectively exploited. In this work, we propose a Topological Information Enhanced model (TIE), which transforms the token-level task into a tag-level task by introducing a two-stage process (i.e. node locating and answer refining). Based on that, TIE integrates Graph Attention Network (GAT) and Pre-trained Language Model (PLM) to leverage the topological information of both logical structures and spatial structures. Experimental results demonstrate that our model outperforms strong baselines and achieves state-of-the-art performances on the web-based SRC benchmark WebSRC at the time of writing. The code of TIE will be publicly available at https://github.com/X-LANCE/TIE.Comment: Accepted to NAACL 202