Name
Abstract | ||
|---|---|---|
Cross-lingual Hypernymy Detection involves determining if a word in one language ("fruit") is a hypernym of a word in another language ("pomme" i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts. We show that BISPARSE-DEP can significantly improve performance on this task, compared to approaches based only on lexical context. Our approach is also robust, showing promise for low-resource settings: our dependency-based embeddings can be learned using a parser trained on related languages, with negligible loss in performance. We also crowd-source a challenging dataset for this task on four languages -- Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly available. |
Year | Venue | DocType |
|---|---|---|
2018 | NAACL-HLT | Conference |
Volume | Citations | PageRank |
abs/1803.11291 | 1 | 0.35 |
References | Authors | |
24 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shyam Upadhyay | 1 | 85 | 10.11 |
| Yogarshi Vyas | 2 | 4 | 1.74 |
| Marine Carpuat | 3 | 587 | 51.99 |
| Dan Roth | 4 | 7735 | 695.19 |