activity
20182026
most citedGraph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

160 citations · 192 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.CL2024

Execution-Based Evaluation of Natural Language to Bash and PowerShell for Incident Remediation

Ngoc Phuoc An Vo, Brent Paulovicks, Vadim Sheinin

Given recent advancements of Large Language Models (LLMs), code generation tasks attract immense attention for wide application in different domains. In an effort to evaluate and s…

cs.CL2023★ 2 cited

Domain Adaptation of a State of the Art Text-to-SQL Model: Lessons Learned and Challenges Found

Irene Manotas, Octavian Popescu, Ngoc Phuoc An Vo +1

There are many recent advanced developments for the Text-to-SQL task, where the Picard model is one of the the top performing models as measured by the Spider dataset competition.…

cs.CL2021★ 2 cited

Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management

Ngoc Phuoc An Vo, Irene Manotas, Octavian Popescu +2

Business Process Management (BPM) is the discipline which is responsible for management of discovering, analyzing, redesigning, monitoring, and controlling business processes. One…

cs.CL2018★ 6 cited

SQL-to-Text Generation with Graph-to-Sequence Model

Kun Xu, Lingfei Wu, Zhiguo Wang +2

Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this p…

cs.CL2018★ 22 cited

Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model

Kun Xu, Lingfei Wu, Zhiguo Wang +3

Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information su…