Name
Abstract | ||
|---|---|---|
We demonstrate a library for the integration of domain knowledge in deep learning architectures. Using this library, the structure of the data is expressed symbolically via graph declarations and the logical constraints over outputs or latent variables can be seamlessly added to the deep models. The domain knowledge can be defined explicitly, which improves the explainability of the models in addition to their performance and generalizability in the low-data regime. Several approaches for such integration of symbolic and sub-symbolic models have been introduced; however, there is no library to facilitate the programming for such integration in a generic way while various underlying algorithms can be used. Our library aims to simplify programming for such integration in both training and inference phases while separating the knowledge representation from learning algorithms. We showcase various NLP benchmark tasks and beyond. The framework is publicly available at Github(1). |
Year | Venue | DocType |
|---|---|---|
2021 | 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021): PROCEEDINGS OF SYSTEM DEMONSTRATIONS | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hossein Rajaby Faghihi | 1 | 0 | 0.34 |
| Quan Guo | 2 | 0 | 0.34 |
| Andrzej Uszok | 3 | 0 | 0.34 |
| Aliakbar Nafar | 4 | 0 | 0.34 |
| Elaheh Raisi | 5 | 15 | 3.73 |
| Parisa Kordjamshidi | 6 | 143 | 18.52 |