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.
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Question: A recently published dataset called SCAN BIBREF2 (Simplified version of the CommAI Navigation tasks), tests compositional generalization in a sequence-to-sequence (seq2seq) setting by systematically holding out of the training set all inputs containing a basic primitive verb ("jump"), and testing on sequences containing that verb. 
 Question: How does the SCAN dataset evaluate compositional generalization?

Answer: it systematically holds out inputs in the training set containing basic primitive verb, "jump", and tests on sequences containing that verb.


Question: The focus of this section is on recently published datasets and LID research applicable to the South African context. An in depth survey of algorithms, features, datasets, shared tasks and evaluation methods may be found in BIBREF0.

The datasets for the DSL 2015 & DSL 2017 shared tasks BIBREF1 are often used in LID benchmarks and also available on Kaggle . The recently published JW300 parallel corpus BIBREF2 covers over 300 languages with around 100 thousand parallel sentences per language pair on average. The WiLI-2018 benchmark dataset BIBREF4 for monolingual written natural language identification includes around 1000 paragraphs of 235 languages. The NCHLT text corpora BIBREF7 is likely a good starting point for a shared LID task dataset for the South African languages BIBREF8. 
 Question: Which datasets are employed for South African languages LID?

Answer: DSL 2015 DSL 2017 JW300 parallel corpus  NCHLT text corpora


Question: With this challenge in mind, we introduce Torch-Struct with three specific contributions:

Modularity: models are represented as distributions with a standard flexible API integrated into a deep learning framework.

Completeness: a broad array of classical algorithms are implemented and new models can easily be added in Python.

Efficiency: implementations target computational/memory efficiency for GPUs and the backend includes extensions for optimization. 
 Question: Is this library implemented into Torch or is framework agnostic?

Answer:
It uses deep learning framework (pytorch)