Name
Abstract | ||
|---|---|---|
We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skipgram model together with a cross-lingual sentence similarity model. Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone. Our model shows competitive performance in a standard crosslingual document classification task. We also show the effectiveness of our method in a limited resource scenario. |
Year | Venue | Field |
|---|---|---|
2018 | PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2 | Computer science,Artificial intelligence,Natural language processing |
DocType | Volume | Citations |
Conference | P18-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| karan singla | 1 | 4 | 4.52 |
| Dogan Can | 2 | 128 | 10.64 |
| Narayanan Shrikanth | 3 | 5558 | 439.23 |