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.

[Q]: In this section, we analyze the Hamming distance between the projections of the sentences from the enwik9 dataset and the corresponding projections of the same sentences after applying character level perturbations. We experiment with three types of character level perturbation BIBREF11 and two types of word level perturbation operations.

Perturbation Study ::: Character Level Perturbation Operations
insert(word, n) : We randomly choose n characters from the character vocabulary and insert them at random locations into the input word. We however retain the first and last characters of the word as is. Ex. transformation: $sample \rightarrow samnple$.

swap(word, n): We randomly swap the location of two characters in the word n times. As with the insert operation, we retain the first and last characters of the word as is and only apply the swap operation to the remaining characters. Ex. transformation: $sample \rightarrow sapmle$.

duplicate(word, n): We randomly duplicate a character in the word by n times. Ex. transformation: $sample \rightarrow saample$.

Perturbation Study ::: Character Level Perturbation Operations ::: Word Level Perturbation Operations
drop(sentence, n): We randomly drop n words from the sentence. Ex. transformation: This is a big cat. $\rightarrow $ This is a cat.

duplicate(sentence, n): Similar to duplicate(word, n) above, we randomly duplicate a word in the sentence n times. Ex. transformation: This is a big cat. $\rightarrow $ This is a big big cat.

swap(sentence, n): Similar to swap(word, n), we randomly swap the location of two words in the sentence n times. Ex. transformation: This is a big cat. $\rightarrow $ This cat is big. 
 Question: How does their perturbation algorihm work?
[A]: same sentences after applying character level perturbations


[Q]: The base sentence embedding model is a BiLSTM over all words in the respective sequences with randomly initialised word embeddings, following BIBREF30 . We opt for this strong baseline sentence encoding model, as opposed to engineering sentence embeddings that work particularly well for this dataset, to showcase the dataset. We would expect pre-trained contextual encoding models, e.g. ELMO BIBREF33 , ULMFit BIBREF34 , BERT BIBREF35 , to offer complementary performance gains, as has been shown for a few recent papers BIBREF36 , BIBREF37 . 
 Question: What were the baselines?
[A]: a BiLSTM over all words in the respective sequences with randomly initialised word embeddings, following BIBREF30


[Q]: We introduce here the use of an L-PCFG with two layers of latent states: one layer is intended to capture the usual syntactic information, and the other aims to capture semantic and topical information by using a large set of states with specific feature functions. 
 Question: What latent variables are modeled in the PCFG?
[A]:
syntactic information semantic and topical information


