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Found 11 papers, 4 papers with code
Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL
Most attempts on Text-to-SQL task using encoder-decoder approach show a big problem of dramatic decline in performance for new databases.
Tackling Execution-Based Evaluation for NL2Bash
We create a set of 50 prompts to evaluate some popular LLMs for NL2Bash.
Domain Adaptation of a State of the Art Text-to-SQL Model: Lessons Learned and Challenges Found
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.
Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management
Business Process Management (BPM) is the discipline which is responsible for management of discovering, analyzing, redesigning, monitoring, and controlling business processes.
Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition
Motion recognition is one of the basic cognitive capabilities of many life forms, however, detecting and understanding motion in text is not a trivial task.
LiMiT: The Literal Motion in Text Dataset
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
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.
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
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.
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
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.
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