Semantic Parsing to Probabilistic Programs for Situated Question Answering

@article{Krishnamurthy2016SemanticPT,
  title={Semantic Parsing to Probabilistic Programs for Situated Question Answering},
  author={J. Krishnamurthy and Oyvind Tafjord and Aniruddha Kembhavi},
  journal={ArXiv},
  year={2016},
  volume={abs/1606.07046}
}
Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using background knowledge to select the correct answer. We present Parsing to Probabilistic Programs (P3), a novel situated question answering model that can use background knowledge and global features of the question/environment interpretation while retaining efficient approximate inference. Our key… Expand
16 Citations
Parsing to Programs: A Framework for Situated QA
  • 2
  • PDF
Question Answering as Global Reasoning Over Semantic Abstractions
  • 50
  • PDF
Probabilistic Neural Programs
  • Highly Influenced
Probabilistic Neural Programs
  • 3
  • PDF
A Review on Neural Network Question Answering Systems
  • 1
  • PDF
Span-based Neural Structured Prediction
...
1
2
...

References

SHOWING 1-10 OF 47 REFERENCES
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input
  • 465
  • PDF
Learning Dependency-Based Compositional Semantics
  • 523
  • PDF
Question Answering on Freebase via Relation Extraction and Textual Evidence
  • 184
  • PDF
Large-scale Semantic Parsing without Question-Answer Pairs
  • 181
  • PDF
Modeling Biological Processes for Reading Comprehension
  • 137
  • PDF
Semantic Parsing on Freebase from Question-Answer Pairs
  • 1,053
  • PDF
Learning to Compose Neural Networks for Question Answering
  • 420
  • PDF
Deep Compositional Question Answering with Neural Module Networks
  • 134
  • PDF
Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary
  • 32
  • PDF
...
1
2
3
4
5
...