TASK DEFINITION: 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.
PROBLEM: Various multilingual extensions of NMT have already been proposed in the literature. The authors of BIBREF18 , BIBREF19 apply multitask learning to train models for multiple languages. Zoph and Knight BIBREF20 propose a multi-source model and BIBREF21 introduces a character-level encoder that is shared across several source languages. In our setup, we will follow the main idea proposed by Johnson et al. BIBREF22 . The authors of that paper suggest a simple addition by means of a language flag on the source language side (see Figure 2 ) to indicate the target language that needs to be produced by the decoder. This flag will be mapped on a dense vector representation and can be used to trigger the generation of the selected language. The authors of the paper argue that the model enables transfer learning and supports the translation between languages that are not explicitly available in training. 
 Question: What neural machine translation models can learn in terms of transfer learning?

SOLUTION: Multilingual Neural Machine Translation Models

PROBLEM: This section introduces our probabilistic model that infers keyword expectation and trains the target model simultaneously. 
 Question: What type of classifiers are used?

SOLUTION: probabilistic model

PROBLEM: For evaluation metrics, we used BLEU BIBREF8 and RIBES BIBREF9 to measure translation accuracy, and token-level delay to measure latency. We used Kytea BIBREF10 as a tokenize method for evaluations of Japanese translation accuracy. 
 Question: Which metrics do they use to evaluate simultaneous translation?

SOLUTION:
BLEU BIBREF8 RIBES BIBREF9 token-level delay