instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
question:
We re-implement the model proposed in BIBREF3, and use it as a baseline for our problem. The rationale behind choosing this particular model as a baseline is it's proven good predictive performance on multilingual text classification. 
 Question: What is their baseline model?
answer:
the model proposed in BIBREF3


question:
This algorithm finds the optimal number by minimising a cost function based on the eigenvector structure of the word similarity matrix. We refer the reader to the relevant literature for further details.

Results and Discussion 
 Question: What are the six target languages?
answer:
Answer with content missing: (3 Experimental Setup) We experiment with six target languages: French (FR), Brazilian Portuguese (PT), Italian (IT), Polish (PL), Croatian (HR), and Finnish (FI).


question:
We evaluate our proposed models on three commonly used knowledge graph datasets—WN18RR BIBREF26, FB15k-237 BIBREF18, and YAGO3-10 BIBREF27. 
 Question: What benchmark datasets are used for the link prediction task?
answer:

WN18RR FB15k-237 YAGO3-10