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: After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset.
Question: How many sentence transformations on average are available per unique sentence in dataset?

SOLUTION: 27.41 transformation on average of single seed sentence is available in dataset.

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: We used the Pasokh dataset BIBREF42 that contains 100 Persian news documents each of which is associated with 5 summaries. Each summary consists of several sentences of the original text, selected by a human expert. Some sentences are slightly modified and are not, therefore, an exact copy of any original sentences. Documents are categorized into six categories such as political, economic and so on. The length of documents ranges from 4 to 156 sentences. Overall, it has about 2,500 sentences.
Question: What dataset is used for this task?

SOLUTION:
the Pasokh dataset BIBREF42