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Found 17 papers, 4 papers with code
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding
Satisfying these constraints simultaneously is difficult for the two predominant paradigms in language generation: neural language modeling and rule-based generation.
Value-Agnostic Conversational Semantic Parsing
Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses.
Task-Oriented Dialogue as Dataflow Synthesis
We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph.
Structured Set Matching Networks for One-Shot Part Labeling
Diagrams often depict complex phenomena and serve as a good test bed for visual and textual reasoning.
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables.
Learning a Neural Semantic Parser from User Feedback
We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention.
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Probabilistic Neural Programs
We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling the use of deep neural networks.
Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
However, all prior approaches to open vocabulary semantic parsing replace a formal KB with textual information, making no use of the KB in their models.
Semantic Parsing to Probabilistic Programs for Situated Question Answering
Situated question answering is the problem of answering questions about an environment such as an image or diagram.
Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary
Crucially, our approach uses an open predicate vocabulary, enabling it to produce denotations for phrases such as {``}Republican front-runner from Texas{''} whose semantics cannot be represented using the Freebase schema.
Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World
LSP learns physical representations for both categorical ({``}blue,{''} {``}mug{''}) and relational ({``}on{''}) language, and also learns to compose these representations to produce the referents of entire statements.
