Interpretable NLPhas taking increasing interest in ensuring that explanations are faithful to the model's decision-making process . This property is crucial for machine learning researchers and practitioners using explanations to better understand models . While prior work focuses primarily on extractive rationales (a subset of the input elements ), we investigate their less-studied counterpart: free-text natural language rationales. We demonstrate that  existing models for faithful interpretability do not extend cleanly to tasks where free-text rationales are needed . We turn to models that jointly predict and rationalize, a common class of models for free-text rationalization whose faithfulness is not yet established. We propose measurements of label-rationale association , a necessary property of faithful rationales, for these models. Using our measurements , we show that a state-of-the-art joint model based on T5 has strengths and weaknesses for producing faithful  rationales.
In interpretable NLP, we require faithful rationales that reflect the model's decision-making process for an explained instance . While prior work focuses on extractive rationales (a subset of the input words ), we investigate their less-studied counterpart: free-text natural language rationales. We demonstrate that pipelines, existing models for faithful extractive rationalization on information-extraction style tasks, do not extend as reliably to "reasoning" tasks requiring free-text rationales . We turn to models that jointly predict and rationalize, a class of widely used high-performance models for free-text rationalization whose faithfulness is not yet established. We define label-rationale association as a necessary property for faithfulness: the internal mechanisms of the model producing the label and the rationale must be meaningfully correlated. We propose two measurements to test this property: robustness equivalence and feature importance agreement. We find that state-of-the-art T5-based joint models exhibit both properties for rationalizing commonsense question-answering and natural language inference, indicating their potential for producing faithful free-text rationales.
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Interpretable NLPhas taking increasing interest in ensuring that explanations are faithful to
In interpretable NLP, we require faithful rationales that reflect
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. This property is crucial for machine learning researchers and practitioners using explanations to better understand models
for an explained instance
D
primarily
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elements
words
A
pipelines,
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interpretability
extractive rationalization on information-extraction style tasks,
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cleanly to tasks where
as reliably to "reasoning" tasks requiring
D
are needed
R
common class of
class of widely used high-performance
R
propose measurements of
define
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,
as
R
of faithful rationales, for these models. Using our measurements , we show that a
for faithfulness: the internal mechanisms of the model producing the label and the rationale must be meaningfully correlated. We propose two measurements to test this property: robustness equivalence and feature importance agreement. We find that
R
joint model based on T5 has strengths and weaknesses
T5-based joint models exhibit both properties for rationalizing commonsense question-answering and natural language inference, indicating their potential
A
free-text