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.

As a first experiment, we compare the quality of fastText embeddings trained on (high-quality) curated data and (low-quality) massively extracted data for Twi and Yoruba languages. The huge ambiguity in the written Twi language motivates the exploration of different approaches to word embedding estimations. In this work, we compare the standard fastText methodology to include sub-word information with the character-enhanced approach with position-based clustered embeddings (CWE-LP as introduced in Section SECREF17).
Question: What two architectures are used?
fastText CWE-LP

We evaluate our proposed model on a Twitter dataset obtained from the authors of BIBREF12. Our final dataset consists of 11,576 users (i.e, fact-checkers), 4,732 fact-checking URLs and 63,429 interactions. The dataset also contains each user's social network information. Note that each user's social relationship is restricted within available users in the dataset.
Question: What dataset is used?
Twitter dataset obtained from the authors of BIBREF12

Defining Faithfulness ::: Assumption 1 (The Model Assumption).
Two models will make the same predictions if and only if they use the same reasoning process. Defining Faithfulness ::: Assumption 2 (The Prediction Assumption).
On similar inputs, the model makes similar decisions if and only if its reasoning is similar. Defining Faithfulness ::: Assumption 3 (The Linearity Assumption).
Certain parts of the input are more important to the model reasoning than others. Moreover, the contributions of different parts of the input are independent from each other.
Question: Which are three assumptions in current approaches for defining faithfulness?
Two models will make the same predictions if and only if they use the same reasoning process. On similar inputs, the model makes similar decisions if and only if its reasoning is similar. Certain parts of the input are more important to the model reasoning than others. Moreover, the contributions of different parts of the input are independent from each other.