Name
Title | ||
|---|---|---|
CL-IMS @ DIACR-Ita - Volente o Nolente - BERT is still not Outperforming SGNS on Semantic Change Detection. |
Abstract | ||
|---|---|---|
We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian. We exploit Average Pairwise Distance of token-based BERT embeddings between time points and rank 5 (of 8) in the official ranking with an accuracy of $.72$. While we tune parameters on the English data set of SemEval-2020 Task 1 and reach high performance, this does not translate to the Italian DIACR-Ita data set. Our results show that we do not manage to find robust ways to exploit BERT embeddings in lexical semantic change detection. |
Year | Venue | DocType |
|---|---|---|
2020 | EVALITA | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Severin Laicher | 1 | 0 | 0.34 |
| Gioia Baldissin | 2 | 0 | 0.34 |
| Enrique Castañeda | 3 | 0 | 0.34 |
| Dominik Schlechtweg | 4 | 2 | 7.85 |
| Sabine Schulte im Walde | 5 | 440 | 65.65 |