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.

[EX Q]: A "coding scheme" is defined, which is a set of labels, annotations, or codes that items in the corpus may have. Schemes include formal definitions or procedures, and often include examples, particularly for borderline cases. Next, coders are trained with the coding scheme, which typically involves interactive feedback. Training sometimes results in changes to the coding scheme, in which the first round becomes a pilot test. Then, annotators independently review at least a portion of the same items throughout the entire process, with a calculation of "inter-annotator agreement" or "inter-rater reliability." Finally, there is a process of "reconciliation" for disagreements, which is sometimes by majority vote without discussion and other times discussion-based.
Question: What are the core best practices of structured content analysis?
[EX A]: "coding scheme" is defined coders are trained with the coding scheme Training sometimes results in changes to the coding scheme calculation of "inter-annotator agreement" or "inter-rater reliability." there is a process of "reconciliation" for disagreements

[EX Q]: Mathur et al. in their paper for detecting offensive tweets proposed a Ternary Trans-CNN model where they train a model architecture comprising of 3 layers of Convolution 1D having filter sizes of 15, 12 and 10 and kernel size of 3 followed by 2 dense fully connected layer of size 64 and 3. The first dense FC layer has ReLU activation while the last Dense layer had Softmax activation. They were able to train this network on a parallel English dataset provided by Davidson et al. The authors were able to achieve Accuracy of 83.9%, Precision of 80.2%, Recall of 69.8%.

The approach looked promising given that the dataset was merely 3189 sentences divided into three categories and thus we replicated the experiment but failed to replicate the results. The results were poor than what the original authors achieved. But, most of the model hyper-parameter choices where inspired from this work.
Question: What is the previous work's model?
[EX A]: Ternary Trans-CNN

[EX Q]: We have training and testing sets in three different languages: English, Chinese and Korean. When fine-tuning, we simply adopted the official training script of BERT, with default hyperparameters, to fine-tune each model until training loss converged.
Question: what does the model learn in zero-shot setting?
[EX A]:
we simply adopted the official training script of BERT, with default hyperparameters, to fine-tune each model until training loss converged