instruction:
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:
In particular, we use Semeval 2014 BIBREF34 Twitter Sentiment Analysis Dataset for the training Sarcasm Datasets Used in the Experiment
This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.

Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets.

We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics.
Question: Which benchmark datasets are used?
answer:
Semeval 2014 BIBREF34 Twitter Sentiment Analysis Dataset dataset was created by BIBREF8 English dataset from BIBREF8 dataset from The Sarcasm Detector

question:
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: $sample
ightarrow samnple$.

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: $sample
ightarrow sapmle$.

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

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. $
ightarrow $ 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. $
ightarrow $ 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. $
ightarrow $ This cat is big.
Question: How does their perturbation algorihm work?
answer:
same sentences after applying character level perturbations

question:
Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks.
Question: What are the specific tasks being unified?
answer:
three types of questions, namely tumor size, proximal resection margin and distal resection margin