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Though end-to-end neural approaches have recently been dominating NLP tasks in both performance and ease-of-use, they lack interpretability and robustness.
Tabfact: A large-scale dataset for table-based fact verification
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A syntactic neural model for general-purpose code generation
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Neural modular control for embodied question answering
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Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
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Seq2sql: Generating structured queries from natural language using reinforcement learning
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Sentence mover’s similarity: Automatic evaluation for multi-sentence texts
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Iterative search for weakly supervised semantic parsing
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, and Matt Gardner · 2019
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Multimodalqa: Complex question answering over text, tables and images
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Codet: Code generation with generated tests
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Tapas: Weakly supervised table parsing via pre-training
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Exploring the limits of transfer learning with a unified text-to-text transformer
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On the potential of lexico-logical alignments for semantic parsing to sql queries
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Break it down: A question understanding benchmark
Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, and Jonathan Berant · 2020
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Program enhanced fact verification with verbalization and graph attention network
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Training compute-optimal large language models
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Evaluating the text-to-sql capabilities of large language models
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