Demo Data from VLDB 2026 / reviewers in the wild / expert
Jocelyn Coulmance
dblp:169/3320
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Representation and self-supervised learning · 87% Information extraction and text analysis · 13% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Representation and self-supervised learning › word representation › word embedding
cross-lingual word embedding |
0.2 | 1 | 2015 | Trans-gram, Fast Cross-lingual Word-embeddings · EMNLP 2015 |
Machine learning › Representation and self-supervised learning › word representation
word embedding |
0.2 | 1 | 2015 | Trans-gram, Fast Cross-lingual Word-embeddings · EMNLP 2015 |
Natural language and speech › Information extraction and text analysis › text classification › transfer learning for text classification
cross-lingual text classification |
0.1 | 1 | 2015 | Trans-gram, Fast Cross-lingual Word-embeddings · EMNLP 2015 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Trans-gram, Fast Cross-lingual Word-embeddingsabstractWe introduce Trans-gram, a simple and computationally-efficient method to simultaneously learn and align wordembeddings for a variety of languages, using only monolingual data and a smaller set of sentence-aligned data.We use our new method to compute aligned wordembeddings for twenty-one languages using English as a pivot language.We show that some linguistic features are aligned across languages for which we do not have aligned data, even though those properties do not exist in the pivot language.We also achieve state of the art results on standard cross-lingual text classification and word translation tasks. Jocelyn Coulmance, Jean-Marc Marty, Guillaume Wenzek, Amine Benhalloum |
EMNLP | 1 |