• No results found

Weakly Supervised Attentional Model for Low Resource Ad hoc Cross lingual Information Retrieval

N/A
N/A
Protected

Academic year: 2020

Share "Weakly Supervised Attentional Model for Low Resource Ad hoc Cross lingual Information Retrieval"

Copied!
6
0
0

Loading.... (view fulltext now)

Full text

Loading

Figure

Figure 1: Query relevance attentional neural network(QRANN) model architecture. Each word in the queryhas an attention mechanism with the sentence to iden-tify relevant spans, followed by a residual connectionand layer normalization
Table 1:Retrieval performance (MAP scores) ofall models on Swahili and Tagalog CLIR evaluationdatasets

References

Related documents

• in-silico analysis and relation between staphylococcal enterotoxins and hypothetical toxins: a prediction study for Staphylococcus

Literature survey reveals different liquid chromatographic method like reverse-phase HPLC method for estimation of deflazacort in pharmaceutical preparations (Cardoso

We note that mStoner does not offer hosting services and has no financial stake in Miami’s hosting decision. Our motivation is solely to help Miami make the best decision in

To provide a unified approach to the study of various properties of these classes, we introduce the following most general- ized subclass of H by using both the Hadamard product

We remark that operators of type ( 1.3 ) are microlocal equivalents via the MKE (Maslov-Kuranishi-Egorov) setup, of a more general class of operators R = PQ 2 + AQ + B where P, Q

Pretražena je Medline baza podataka korištenjem ključnih riječi leisure- time physical activity I ( sick leave ILI sickness absence ILI absenteeism ). U završnu analizu

*post-symposium excursion fees include overnight accommodation on the fieldtrip (5 nights), meals, transport (from Selje to drop-offs Thursday, July 3, Lepsøya with short

We then present Auld Leaky, a lightweight contextual link server that stores and serves structures represented in FOHM, using Context to filter query results..