In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Ex Input:
Second, scrapping Tweets directly from Twitter website. Using the second option, the daily Tweets for stocks of interest from 2015 January to 2017 June were downloaded. For this reason, two companies from the same industry, Tesla and Ford are investigated on how Twitter sentiment could impact the stock price. To translate each tweet into a sentiment score, the Stanford coreNLP software was used.

Ex Output:
Which tweets are used to output the daily sentiment signal?


Ex Input:
Task 1: Quora Duplicate Question Pair Detection Task 2: Ranking questions in Bing's People Also Ask

Ex Output:
On which tasks do they test their conflict method?


Ex Input:
 The Base is trained only on clean data while Base+Noise is trained on both the clean and the noisy data without noise handling. Global-CM uses a global confusion matrix for all noisy instances to model the noise as proposed by BIBREF3 and presented in Section SECREF3. The same architecture is used for Global-ID-CM, but the confusion matrix is initialized with the identity matrix (instead of Formula DISPLAY_FORM5) and only adapted during training.

The cluster-based models we propose in Section SECREF4 are Brown-CM and K-Means-CM. We experimented with numbers of clusters of 5, 10, 25 and 50. The models that select only the largest groups $G$ are marked as *-Freq and select either 30% or 50% of the clusters. The interpolation models have the postfix *-IP with $\lambda \in \lbrace 0.3, 0.5, 0.7\rbrace $ . The combination of both is named *-Freq-IP. As for all other hyperparameters, the choice was taken on the development set.

We implemented the Cleaning BIBREF15 and Dynamic-CM BIBREF14 models. Both were not developed for sequence labeling tasks and therefore needed to be adapted. For the Cleaning model, we followed the instructions by BIBREF3. The embedding and prediction components of the Dynamic-CM model were replaced according to our base model. The output of the dense layer was used as input to the dynamic matrix generation. We experimented with and without their proposed trace loss.

Ex Output:

What is baseline used?