Name
Abstract | ||
|---|---|---|
Transformers are arguably the main workhorse in recent Natural Language Processing research. By definition a Transformer is invariant with respect to reorderings of the input. However, language is inherently sequential and word order is essential to the semantics and syntax of an utterance. In this paper, we provide an overview of common methods to incorporate position information into Transformer models. The objectives of this survey are to i) showcase that position information in Transformer is a vibrant and extensive research area; ii) enable the reader to compare existing methods by providing a unified notation and meaningful clustering; iii) indicate what characteristics of an application should be taken into account when selecting a position encoding; iv) provide stimuli for future research. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1162/coli_a_00445 | Computational Linguistics |
DocType | Volume | Issue |
Journal | 48 | 3 |
ISSN | Citations | PageRank |
0891-2017 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philipp Dufter | 1 | 1 | 4.74 |
| Martin Schmitt | 2 | 0 | 1.35 |
| Hinrich Schütze | 3 | 2113 | 362.21 |