Name
Title | ||
|---|---|---|
Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations. |
Abstract | ||
|---|---|---|
Neural models of Knowledge Base data have typically employed compositional representations of graph objects: entity and relation embeddings are systematically combined to evaluate the truth of a candidate Knowedge Base entry. Using a model inspired by Harmonic Grammar, we propose to tokenize triplet embeddings by subjecting them to a process of optimization with respect to learned well-formedness conditions on Knowledge Base triplets. The resulting model, known as Gradient Graphs, leads to sizable improvements when implemented as a companion to compositional models. Also, we show that the supracompositional triplet token embeddings it produces have interpretable properties that prove helpful in performing inference on the resulting triplet representations. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Computation and Language | Journal |
Volume | Citations | PageRank |
abs/1811.01062 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Lalisse | 1 | 0 | 0.34 |
| Paul Smolensky | 2 | 215 | 93.76 |