TASK DEFINITION: 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.
PROBLEM: We have used a dataset of 14 TDs to conduct our experiments. 
 Question: Which dataset(s) do they use?

SOLUTION: 14 TDs BIBREF15

PROBLEM: Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed. 
 Question: Which matrix factorization methods do they use?

SOLUTION: weighted factorization of a word-context co-occurrence matrix 

PROBLEM: Guidelines for Evaluating Faithfulness
We propose the following guidelines for evaluating the faithfulness of explanations. These guidelines address common pitfalls and sub-optimal practices we observed in the literature.

Guidelines for Evaluating Faithfulness ::: Be explicit in what you evaluate.
Conflating plausability and faithfulness is harmful. You should be explicit on which one of them you evaluate, and use suitable methodologies for each one. Of course, the same applies when designing interpretation techniques—be clear about which properties are being prioritized.

Guidelines for Evaluating Faithfulness ::: Faithfulness evaluation should not involve human-judgement on the quality of interpretation.
We note that: (1) humans cannot judge if an interpretation is faithful or not: if they understood the model, interpretation would be unnecessary; (2) for similar reasons, we cannot obtain supervision for this problem, either. Therefore, human judgement should not be involved in evaluation for faithfulness, as human judgement measures plausability.

Guidelines for Evaluating Faithfulness ::: Faithfulness evaluation should not involve human-provided gold labels.
We should be able to interpret incorrect model predictions, just the same as correct ones. Evaluation methods that rely on gold labels are influenced by human priors on what should the model do, and again push the evaluation in the direction of plausability.

Guidelines for Evaluating Faithfulness ::: Do not trust “inherent interpretability” claims.
Inherent interpretability is a claim until proven otherwise. Explanations provided by “inherently interpretable” models must be held to the same standards as post-hoc interpretation methods, and be evaluated for faithfulness using the same set of evaluation techniques.

Guidelines for Evaluating Faithfulness ::: Faithfulness evaluation of IUI systems should not rely on user performance.
End-task user performance in HCI settings is merely indicative of correlation between plausibility and model performance, however small this correlation is. While important to evaluate the utility of the interpretations for some use-cases, it is unrelated to faithfulness. 
 Question: Which are key points in guidelines for faithfulness evaluation?

SOLUTION:
Be explicit in what you evaluate. Faithfulness evaluation should not involve human-judgement on the quality of interpretation. Faithfulness evaluation should not involve human-provided gold labels. Do not trust “inherent interpretability” claims. Faithfulness evaluation of IUI systems should not rely on user performance.