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: Compared to the in-context-parsing-based system, the combination with exact string matching yields a gain in recall of over 6%, and the combination with inflectional string matching yields an even bigger gain of almost 8%, at precision losses of 0.6% and 0.8%, respectively. 
 Question: What compleentary PIE extraction methods are used to increase reliability further?

Answer: exact string matching inflectional string matching


Question: As a starting point, we used the DIP corpus BIBREF37 , a collection of 49 clusters of 100 web pages on educational topics (e.g. bullying, homeschooling, drugs) with a short description of each topic. 
 Question: Which collections of web documents are included in the corpus?

Answer: DIP corpus BIBREF37


Question: Our desiderata for the task collection were: sufficient diversity, existence of fairly large datasets for training, and success as standalone training objectives for sentence representations.

Multi-task training setup 
 Question: Which model architecture do they for sentence encoding?

Answer:
Answer with content missing: (Skip-thought vectors-Natural Language Inference paragraphs) The encoder for the current sentence and the decoders for the previous (STP) and next sentence (STN) are typically parameterized as separate RNNs
- RNN