CoSaTa: A Constraint Satisfaction Solver and Interpreted Language for Semi-Structured Tables of Sentences

Peter Jansen


Abstract
This work presents CoSaTa, an intuitive constraint satisfaction solver and interpreted language for knowledge bases of semi-structured tables expressed as text. The stand-alone CoSaTa solver allows easily expressing complex compositional “inference patterns” for how knowledge from different tables tends to connect to support inference and explanation construction in question answering and other downstream tasks, while including advanced declarative features and the ability to operate over multiple representations of text (words, lemmas, or part-of-speech tags). CoSaTa also includes a hybrid imperative/declarative interpreted language for expressing simple models through minimally-specified simulations grounded in constraint patterns, helping bridge the gap between question answering, question explanation, and model simulation. The solver and interpreter are released as open source. Screencast Demo: https://youtu.be/t93Acsz7LyE
Anthology ID:
2020.emnlp-demos.10
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
October
Year:
2020
Address:
Online
Editors:
Qun Liu, David Schlangen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
70–76
Language:
URL:
https://aclanthology.org/2020.emnlp-demos.10
DOI:
10.18653/v1/2020.emnlp-demos.10
Bibkey:
Cite (ACL):
Peter Jansen. 2020. CoSaTa: A Constraint Satisfaction Solver and Interpreted Language for Semi-Structured Tables of Sentences. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 70–76, Online. Association for Computational Linguistics.
Cite (Informal):
CoSaTa: A Constraint Satisfaction Solver and Interpreted Language for Semi-Structured Tables of Sentences (Jansen, EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-demos.10.pdf
Code
 clulab/cosata