Search Results for author: Vadim Sheinin

Found 8 papers, 4 papers with code

Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management

no code implementations RANLP 2021 Ngoc Phuoc An Vo, Irene Manotas, Octavian Popescu, Algimantas Cerniauskas, Vadim Sheinin

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

Management

LiMiT: The Literal Motion in Text Dataset

1 code implementation Findings of the Association for Computational Linguistics 2020 Irene Manotas, Ngoc Phuoc An Vo, Vadim Sheinin

Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically.

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

1 code implementation EMNLP 2018 Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Vadim Sheinin

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.

Graph-to-Sequence SQL-to-Text +1

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

1 code implementation EMNLP 2018 Kun Xu, Lingfei Wu, Zhiguo Wang, Mo Yu, Li-Wei Chen, Vadim Sheinin

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 such as dependency graph or constituent trees.

Graph-to-Sequence Semantic Parsing

Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

4 code implementations ICLR 2019 Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Michael Witbrock, Vadim Sheinin

Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings.

Graph-to-Sequence SQL-to-Text +1

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