activity
20162022
most citedProbabilistic Neural Programs

4 citations · 4 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CL2022

The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding

Hao Fang, Anusha Balakrishnan, Harsh Jhamtani +7

In a real-world dialogue system, generated text must be truthful and informative while remaining fluent and adhering to a prescribed style. Satisfying these constraints simultaneou…

cs.CL2020

Task-Oriented Dialogue as Dataflow Synthesis

Semantic Machines, Jacob Andreas, John Bufe +43

We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends th…

cs.CV2017

Structured Set Matching Networks for One-Shot Part Labeling

Jonghyun Choi, Jayant Krishnamurthy, Aniruddha Kembhavi +1

Diagrams often depict complex phenomena and serve as a good test bed for visual and textual reasoning. However, understanding diagrams using natural image understanding approaches…

cs.CL2017

Learning a Neural Semantic Parser from User Feedback

Srinivasan Iyer, Ioannis Konstas, Alvin Cheung +2

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 requir…

cs.NE2016★ 4 cited

Probabilistic Neural Programs

Kenton W. Murray, Jayant Krishnamurthy

We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultan…

cs.CL2016

Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge

Matt Gardner, Jayant Krishnamurthy

Traditional semantic parsers map language onto compositional, executable queries in a fixed schema. This mapping allows them to effectively leverage the information contained in la…