Original: E XPLAINING THE E FFICACY OF C OUNTERFACTUALLY A UGMENTED D ATA
Revised: E XPLAINING THE E FFICACY OF C OUNTERFACTUALLY A UGMENTED D ATA

Original: Anonymous authors

Original: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: In attempts to produce machine learning models less reliant on spurious patterns in training data, researchers have recently proposed  counterfactually augmented data, a human-in-the-loop process. in which given some documents and their (initial) labels, humans must revise the text to make a counterfactual label applicable.
Revised: In attempts to produce machine learning models less reliant on spurious patterns in NLP datasets, researchers have recently proposed curating counterfactually augmented data (CAD) via a human-in-the-loop process in which given some documents and their (initial) labels, humans must revise the text to make a counterfactual label applicable.

Original: Importantly, edits that are not necessary to ﬂip the applicable label are prohibited.
Revised: Importantly, edits that are not necessary to ﬂip the applicable label are prohibited.

Original: Models trained on the augmented (original and revised) data appear to rely less on semantically irrelevant words and to generalize better out-of-domain.
Revised: Models trained on the augmented (original and revised) data appear, empirically, to rely less on semantically irrelevant words and to generalize better out of domain.

Original: While this work draws  on causal thinking, the underlying causal model remains unclear as do the principles underlying  improvements out-of-domain.
Revised: While this work draws loosely on causal thinking, the underlying causal model (even at an abstract level) and the principles underlying the observed out-of-domain improvements remain unclear.

Original: In this paper, we introduce a toy analog, using linear Gaussian models, revealing interesting relationships between causal models, measurement noise, out-of-domain generalization, and reliance on spurious signals.
Revised: In this paper, we introduce a toy analog based on linear Gaussian models, observing interesting relationships between causal models, measurement noise, out-of-domain generalization, and reliance on spurious signals.

Original: Interestingly, our theory suggests that noising causal features will degrade out-of-domain performance, while noising non-causal features may robustify models out-of-domain.
Revised: Our analysis provides some insights that help to explain the efﬁcacy of CAD. Moreover, we develop the hypothesis that while adding noise to causal features should degrade both in-domain and out-ofdomain performance, adding noise to non-causal features should lead to relative improvements in out-of-domain performance.

Original: This analysis yields insights that help toexplain the efﬁcacy of counterfactually augmented data.
Revised: This idea inspires a speculative test for determining whether a feature attribution technique has identiﬁed the causal spans . If adding noise (e.g., by random word ﬂips) to the highlighted spans degrades both in-domain and out-of-domain performance on a battery of challenge datasets, but adding noise to the complement gives improvements out-of-domain, this suggests we have identiﬁed causal spans. Thus, we present a large-scale empirical study comparing spans edited to create CAD to those selected by attention and saliency maps.

Original: Finally, we present a large-scale empirical study that supports this hypothesis.
Revised: Across numerous challenge domains and models, we ﬁnd that the hypothesized phenomenon is pronounced for CAD

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Despite machine learning (ML)’s many practical breakthroughs, formidable obstacles obstruct its deployment in consequential applications.
Revised: Despite machine learning (ML)’s many practical breakthroughs, formidable obstacles obstruct its deployment in consequential applications.

Original: Of particular concern, these models have been shown to rely on spurious signals, such as surface-level textures in images (Jo & Bengio, 2017; Geirhos et al., 2018), and background scenery—even when the task is to recognize foreground objects (Beery et al., 2018).
Revised: Of particular concern, these models have been shown to rely on spurious signals, such as surface-level textures in images (Jo & Bengio, 2017; Geirhos et al., 2018), and background scenery—even when the task is to recognize foreground objects (Beery et al., 2018).

Original: Other studies have uncovered a worrisome reliance on gender in models trained for the purpose of recommending jobs (Dastin, 2018), and on race in prioritizing patients for medical care (Obermeyer et al., 2019).
Revised: Other studies have uncovered a worrisome reliance on gender in models trained for the purpose of recommending jobs (Dastin, 2018), and on race in prioritizing patients for medical care (Obermeyer et al., 2019).

Original: Moreover, while modern ML performs remarkably well on iid holdout data, performance often decays catastrophically under  distribution shift (Quionero-Candela et al., 2009; Sugiyama &Kawanabe, 2012;
Revised: Moreover, while modern ML performs remarkably well on independent and identically distributed (iid) holdout data, performance often decays catastrophically under both naturally occurring and adversarial distribution shift (Quionero-Candela et al., 2009; Sugiyama &

Original:  Szegedy et al., 2014; Ovadia et al., 2019; Filos et al., 2020).
Revised: Kawanabe, 2012; Szegedy et al., 2014; Ovadia et al., 2019; Filos et al., 2020).

Original: These two problems: (i) reliance on semantically irrelevant signals, raising concerns about bias; and (ii) the brittleness of models under distributions shift; might appear unrelated, but share important conceptual features.
Revised: These two problems: (i) reliance on semantically irrelevant signals, raising concerns about bias; and (ii) the brittleness of models under distributions shift; might appear unrelated, but share important conceptual features.

Original: Concerns about bias stem in part from principles of procedural fairness (Blader & Tyler, 2003; Miller, 2017; Grgic-Hlaca et al., 2018; Lipton et al., 2018), according to which decisions should be based on qualiﬁcations, not on distant proxies that are spuriously associated with the outcome of interest. 
Revised: Concerns about bias stem in part from principles of procedural fairness (Blader & Tyler, 2003; Miller, 2017; Grgic-Hlaca et al., 2018; Lipton et al., 2018), according to which decisions should be based on qualiﬁcations, not on distant proxies that are spuriously associated with the outcome of interest. Arguably one key distinction of an actual qualiﬁcation might be that it [Figure]

Revised: actually exerts causal inﬂuence on the outcome of interest.

Original: In an interesting parallel, one line of work on distribution shift has focused on causal graphical models, addressing settings where some parts of the model remain stable over time but others do not.
Revised: In an interesting parallel, one line of work on distribution shift has focused on causal graphical models, addressing settings where some parts of the model remain stable over time but others do not.

Original: One common assumption is that the relationship between the target and its direct causal ancestors remains invariant (Peters et al., 2016; Ghassami et al., 2017; Rojas-Carulla et al., 2018; Kuang et al., 2018; Magliacane et al., 2018; Christiansen & Peters, 2020; Weichwald & Peters, 2020).
Revised: One common assumption is that the relationship between the target and its direct causal ancestors remains invariant (Peters et al., 2016; Ghassami et al., 2017; Rojas-Carulla et al., 2018; Kuang et al., 2018; Magliacane et al., 2018; Christiansen & Peters, 2020; Weichwald & Peters, 2020).

Original: While these papers contribute insight, they focus on toy settings, with few variables related by a known model.
Revised: While these papers contribute insight, they focus on toy settings, with few variables related by a known model.

Original: However, in complex domains with high-dimensional data, what  are relevantvariables and what graph relates them is typically unclear.
Revised: However, in complex domains with high-dimensional data, what variables are relevant and what graph relates them is typically unclear.

Original: Recently, Kaushik et al.
Revised: Recently in NLP, Kaushik et al.

Original: (2020) proposed Counterfactually Augmented Data (CAD) , injecting causal thinking into real world settings by leveraging human-in-the-loop feedback to identify causally relevant features (versus those that merely happen to be predictive due to confounding).
Revised: (2020) proposed Counterfactually Augmented Data (CAD) , injecting causal thinking into real world settings by leveraging human-in-the-loop feedback to identify causally relevant features (versus those that merely happen to be predictive due to confounding).

Original: Editors are presented with document-label pairs and tasked with editing the document to render counterfactual[Figure] labels applicable.
Revised: Human editors are presented with document-label pairs and tasked with editing documents to render counterfactual labels applicable.

Original: The instructions restrict editors to only make modiﬁcations that are necessary to ﬂip the label’s applicability.
Revised: The instructions restrict editors to only make modiﬁcations that are necessary to ﬂip the label’s applicability.

Original: The key result is that many spurious correlations present in the original dataset are absent in the CAD.
Revised: The key result is that many spurious correlations present in the original dataset are absent in the CAD.

Original: In case of sentiment analysis, Kaushik et al.
Revised: In case of sentiment analysis, Kaushik et al.

Original: (2020) demonstrated that linear classiﬁers trained to predict the sentiment of movie reviews based on bag-of-words representations assign high-magnitude weights to seemingly irrelevant terms, including “will”, “my”, “has”, “especially”, and “script”, among others.
Revised: (2020) demonstrated that linear classiﬁers trained to predict the sentiment of movie reviews based on bagof-words representations assign high-magnitude weights to seemingly irrelevant terms, including “will”, “my”, “has”, “especially”, and “script”, among others.

Original: Notably, “horror” featured among the most negative terms, while “romance” featured among the most positive, despite both communicating genre, not sentiment.
Revised: Notably, “horror” featured among the most negative terms, while “romance” featured among the most positive, despite both communicating genre, not sentiment.

Original: Interestingly, in the revised data, each “horror” review retains the word “horror” (per the instruction not to make unnecessary edits) but is associated with the opposite sentiment label.
Revised: Interestingly, in the revised data, each “horror” review retains the word “horror” (per the instruction not to make unnecessary edits) but is associated with the opposite sentiment label.

Original: Models trained on the augmented data (original and revised) perform well on both original and revised data, and assign little weight to the associated but irrelevant terms.
Revised: Models trained on the augmented data (original and revised) perform well on both original and revised data, and assign little weight to the associated but irrelevant terms.

Original: Intuitively, one might imagine that the spurious patterns would generalize less reliably out of domain.
Revised: Intuitively, one might imagine that the spurious patterns would generalize less reliably out of domain.

Original: Most consumer products do not belong to movie genres, but words like “excellent” and “awful” continue to connote positive and negative sentiment, respectively.
Revised: Most consumer products do not belong to movie genres, but words like “excellent” and “awful” continue to connote positive and negative sentiment, respectively.

Original: Indeed, Kaushik et al.
Revised: Indeed, Kaushik et al.

Original: (2020) demonstrated that models trained on CAD enjoyed out-of-domain performance beneﬁts on Amazon, SemEval, and Yelp reviews.
Revised: (2020) demonstrated that models trained on CAD enjoyed out-of-domain performance beneﬁts on Tweets, and Amazon and Yelp reviews.

Original: In this paper, we take strides towards explaining CAD’s efﬁcacy.
Revised: In this paper, we make some initial attempts towards explaining CAD’s efﬁcacy.

Original: While CAD  draws on causal thinking,  foundational questions remain open: What  causal structures underlie settings where CAD ought to be effective?
Revised: While CAD plainly draws on causal thinking, (invoking interventions and counterfactuals), foundational questions remain open: What is the assumed causal structure underlying settings where CAD might be effective?

Original: What are the principles underlying its out-of-domain beneﬁts?
Revised: What are the principles underlying its out-of-domain beneﬁts?

Original: Must humans really intervene or could automatic feature attribution methods, e.g., attention (DeYoung et al., 2020), or cheaper feedback mechanisms, e.g., feature feedback (Zaidan et al., 2007), produce similar results?
Revised: Must humans really intervene, or could automatic feature attribution methods, e.g., attention (DeYoung et al., 2020), or cheaper feedback mechanisms, e.g., feature feedback (Zaidan et al., 2007), produce similar results?

Original: To begin, we analyze toy linear Gaussian models (Figure 1; Wright, 1934), with the following goals:  (i) gain qualitative insights into when a predictor might rely on spurious patterns; and (ii) provide a mechanism of action to explain the efﬁcacy of CAD.
Revised: To begin, we consider linear Gaussian models (Figure 1; Wright, 1934), with the following goals: to (i) gain qualitative insights into when a predictor might rely on spurious signals in the ﬁrst place; and (ii) provide a mechanism of action to explain the efﬁcacy of CAD.

Original: First, we analyze the causal setting (features cause the label).
Revised: First, we analyze the causal setting (features cause the label).

Original: When the features share a common cause and the predictor is well-speciﬁed (linear), it will assign zero weight (in expectation) to non-causal features.
Revised: When the features share a common cause and a predictor is wellspeciﬁed (linear), it will assign zero weight (in expectation) to non-causal features.

Original: However, when the causal features are subject to observation noise (measurement error), the non-causal features are assigned non-zero weight.
Revised: However, when the causal features are subject to observation noise (measurement error), the non-causal features are assigned non-zero weight.

Original: Conversely, when we inject noise on non-causal features, predictors rely more on causal features, which we expect to result in better out-of-domain generalization.
Revised: Conversely, when we inject noise on non-causal features, predictors rely more on causal features, which we expect to result in better out-of-domain generalization.

Original: In the causal framework, we observe that CAD might be usefully formalized as a process analogous to intervening on the causal features, thus d-separating the label from the non-causal features (Pearl, 1985).
Revised: In the causal framework, we observe that CAD might be usefully formalized as a process analogous to intervening on the causal features, thus d-separating the label from the non-causal features (Pearl, 1985).

Original: Alternatively, we might conceptualize CAD with an anticausal model (Sch¨olkopf et al., 2012).
Revised: Alternatively, we might conceptualize CAD with an anticausal model (Sch¨olkopf et al., 2012).

Original: In this setup, the label of interest is one of several latent attributes that directly causes some (but not all features).
Revised: In this setup, the label of interest is one of several latent attributes that directly causes some (but not all features).

Original: In this interpretation, we imagine that we have intervened on the label and the editor’s role is to simulate the counterfactual document that would ﬂow from the alternative label, holding otherlatent attributes constant.
Revised: In this interpretation, we imagine that we have intervened on the label and the editor’s role is to simulate the counterfactual document that would ﬂow from the alternative label, holding other attributes constant.

Original: Note that this too d-separates the label from the spurious correlate.
Revised: Note that this too d-separates the label from the spurious correlate.

Original: In both cases, any model trained on the resulting data ought to rely only on the causal features.
Revised: In both cases, any model trained on the resulting data ought to rely only on the causal features.

Original: Our toy abstraction points to a useful diagnostic test.
Revised: Our toy abstraction points to a useful diagnostic test.

Original: If indeed CAD involves interventions on spans that are (in some sense) analogous to the causal features in our toy model, then injecting noise on these words should increase model reliance on the non-causal features and thus (in general) lead to deteriorating performance out-of-domain.
Revised: If indeed CAD involves interventions on spans that are (in some sense) analogous to the causal features in our toy model, then injecting noise on these words should increase model reliance on the non-causal features and thus (in general) lead to deteriorating performance out-of-domain.

Original: On the other hand, injecting noise on the non-causal features should lead the model to rely more on the causal features, leading to improved performance out of domain.
Revised: On the other hand, injecting noise on the non-causal features should lead the model to rely more on the causal features, leading to improved performance out of domain.

Original: Through a series of large-scale empirical experiments addressing sentiment anal- ysis and natural language inference (NLI) tasks, we inject noise on the spans marked as causal vs non-causal.
Revised: Through a series of large-scale empirical experiments addressing sentiment analysis and natural language inference (NLI) tasks, we inject noise on the spans marked as causal vs non-causal.

Original: We compare the effects of injecting noise on the spans revised by the CAD editors, the spans selected through feature feedback (Zaidan et al., 2007), and to spans selected automatically using feature attribution heuristics such as attention- and gradient-based saliency methods.
Revised: We compare the effects of injecting noise on the spans revised by the CAD editors, the spans selected through feature feedback (Zaidan et al., 2007), and to spans selected automatically using feature attribution heuristics such as attention- and gradient-based saliency methods.

Original: If indeed the hypotheses that (i) identifying causal features requires human intervention; and (ii) models relying on causal features generalize better out of domain; hold, we might expect that (compared to automatic attribution methods) noising human-provided rationales would deteriorate out-of-domain performance, while noising non-rationales should prove beneﬁcial.
Revised: If indeed the hypotheses that (i) identifying causal features requires human intervention; and (ii) models relying on causal features generalize better out of domain; hold, we might expect that (compared to automatic attribution methods) noising human-provided rationales would deteriorate out-of-domain performance, while noising non-rationales should prove beneﬁcial.

Original: We show that an SVM sentiment analysis model trained on the original 1 .
Revised: We show that an SVM sentiment analysis model trained on the original 1 .

Original: 7 k IMDb reviews from
Revised: 7 k IMDb reviews from

Original: Kaushik et al.
Revised: Kaushik et al.

Original: (2020) obtains 87 .
Revised: (2020) obtains 87 .

Original: 8% accuracy on the IMDb test set and 79 .
Revised: 8% accuracy on the IMDb test set and 79 .

Original: 9% on Yelp reviews
Revised: 9% on Yelp reviews

Original: but when all rationales are replaced with noise, the classiﬁer experiences ≈ 11% drop on in-sample accuracy and an even bigger drop of ≈ 28 .
Revised: but when all rationales are replaced with noise, the classiﬁer experiences ≈ 11% drop on in-sample accuracy and an even bigger drop of ≈ 28 .

Original: 7% on Yelp.
Revised: 7% on Yelp.

Original: However, as non-rationales are replaced with noise, in-sample accuracy goes down by ≈ 10% but  accuracyon Yelp increases by 1 . 5% .
Revised: However, as non-rationales are replaced with noise, in-domain accuracy goes down by ≈ 10% but out-of-domain accuracy increases by 1 . 5% .

Original: Similarly, in NLI, the accuracy of a BERT classiﬁer ﬁne-tuned on a subsample of e-SNLI (DeYoung et al., 2020) goes down by ≈ 20% when rationales are replaced with noise, whereas the out-ofdomain accuracy goes down by 21 . 3 – 31 .
Revised: Similarly, in NLI, the accuracy of a BERT classiﬁer ﬁne-tuned on a subsample of e-SNLI (DeYoung et al., 2020) goes down by ≈ 20% when rationales are replaced with noise, whereas the out-ofdomain accuracy goes down by 21 . 3 – 31 .

Original: 5% on various datasets.
Revised: 5% on various datasets.

Original: If non-rationales are replaced with noise, in-sample accuracy goes down by 6 . 2% but out of domain accuracy drops by only 2 . 3 – 5 . 5% .
Revised: If non-rationales are replaced with noise, in-sample accuracy goes down by 6 . 2% but out of domain accuracy drops by only 2 . 3 – 5 . 5% .

Original: Similar behavior is observed across both tasks, on all datasets and models.
Revised: Similar patterns are observed across both tasks, on all datasets and models.

Original: However, when using attention masks, the resulting changes in model performance do not  follow these trends.
Revised: However, when using attention masks, the resulting changes in model performance do not appear to follow these trends.

Original: In another test to probe whether human feedback is indeed necessary to produce datasets with the observed quantitative results of CAD, we experiment with style transfer methods for converting
Revised: In another test to probe whether human feedback is indeed necessary to produce datasets with the observed quantitative results of CAD, we experiment with style transfer methods for converting

Original: Positive reviews into Negative and vice versa.
Revised: Positive reviews into Negative and vice versa.

Original: Compared to an SVM classiﬁer trained on styletransfer-augmented data, training on CAD leads to a gain of 5 – 16 .
Revised: Compared to an SVM classiﬁer trained on styletransfer-augmented data, training on CAD leads to a gain of 5 – 16 .

Original: 4% in accuracy on Amazon and 3 . 7 – 17 .
Revised: 4% in accuracy on Amazon and 3 . 7 – 17 .

Original: 8% on Yelp.
Revised: 8% on Yelp.

Original: Similarly, a BERT classiﬁer ﬁne-tuned on CAD outperforms the same classiﬁer ﬁne-tuned on style-transfer-augmented data by 4 . 9 – 21 . 5% on Amazon and 1 . 9 – 9 . 5% on Yelp.
Revised: Similarly, a BERT classiﬁer ﬁne-tuned on CAD outperforms the same classiﬁer ﬁne-tuned on style-transfer-augmented data by 4 . 9 – 21 . 5% on Amazon and 1 . 9 – 9 . 5% on Yelp.

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: NLP papers on spurious associations have addressed social biases (Dixon et al., 2018;
Revised: NLP papers on spurious associations have addressed social biases (Dixon et al., 2018;

Original: Zhao et al., 2018; Kiritchenko & Mohammad, 2018; Dinan et al., 2019; May et al., 2019), spurious signals owing to annotation heuristics (Gururangan et al., 2018; Poliak et al., 2018), and artifacts from automatic data generation (Chen et al., 2016; Kaushik & Lipton, 2018), Researchers have also demonstrated vulnerabilities to synthetic transformations, such as distractor phrases (Jia & Liang, 2017; Wallace et al., 2019), document paraphrases (Iyyer et al., 2018;
Revised: Zhao et al., 2018; Kiritchenko & Mohammad, 2018; Dinan et al., 2019; May et al., 2019), spurious signals owing to annotation heuristics (Gururangan et al., 2018; Poliak et al., 2018), and artifacts from automatic data generation (Chen et al., 2016; Kaushik & Lipton, 2018), Researchers have also demonstrated vulnerabilities to synthetic transformations, such as distractor phrases (Jia & Liang, 2017; Wallace et al., 2019), document paraphrases (Iyyer et al., 2018;

Original: Pfeiffer et al., 2019), and synthetic but meaning-preserving modiﬁcations (Ribeiro et al., 2018; Glockner et al., 2018;
Revised: Pfeiffer et al., 2019), and synthetic but meaning-preserving modiﬁcations (Ribeiro et al., 2018; Glockner et al., 2018;

Original: Shen et al., 2018).
Revised: Shen et al., 2018).

Original: Researchers have proposed incorporating human feedback solicited through a variety of mechanisms including highlighting rationales , spans of text indicative of the label (Zaidan et al., 2007; Zaidan &
Revised: Researchers have proposed incorporating human feedback solicited through a variety of mechanisms including highlighting rationales , spans of text indicative of the label (Zaidan et al., 2007; Zaidan &

Original: Eisner, 2008; Poulis & Dasgupta, 2017).
Revised: Eisner, 2008; Poulis & Dasgupta, 2017).

Original: To combat gender stereotypes, Lu et al.
Revised: To combat gender stereotypes, Lu et al.

Original: ; Zmigrod et al.
Revised: ; Zmigrod et al.

Original: ; Maudslay et al.
Revised: ; Maudslay et al.

Original: (2019) describe data augmentation approaches that programmatically alter text.
Revised: (2019) describe data augmentation approaches that programmatically alter text.

Original: More recently, Kaushik et al.
Revised: More recently, Kaushik et al.

Original: (2020) employed crowd workers to edit text to make an opposite label applicable.
Revised: (2020) employed crowd workers to edit text to make an opposite label applicable.

Original: Through their experiments they show that classiﬁers trained on CAD generalize well out of domain.
Revised: Through their experiments they show that classiﬁers trained on CAD generalize well out of domain.

Original: Teney et al.
Revised: Teney et al.

Original: (2020) show the beneﬁts of CAD in computer vision and
Revised: (2020) show the beneﬁts of CAD in computer vision and

Original: NLP, and Srivastava et al.
Revised: NLP, and Srivastava et al.

Original: (2020) employ crowdworkers to augment their training data to capture potential unmeasured variables.
Revised: (2020) employ crowdworkers to augment their training data to capture potential unmeasured variables.

Original: A growing body of work has also looked at reducing reliance on spurious correlations by exploiting the stability of relationships between the target variable and its (graph) neighbors.
Revised: A growing body of work has also looked at reducing reliance on spurious correlations by exploiting the stability of relationships between the target variable and its (graph) neighbors.

Original: Peters et al.
Revised: Peters et al.

Original: (2016) propose invariant causal prediction to obtain a causal predictor from multiple datasets.
Revised: (2016) propose invariant causal prediction to obtain a causal predictor from multiple datasets.

Original: Ghassami et al.
Revised: Ghassami et al.

Original: (2017) discuss a similar approach but do not assume that the exogenous noise of the target variable stays ﬁxed among environments.
Revised: (2017) discuss a similar approach but do not assume that the exogenous noise of the target variable stays ﬁxed among environments.

Original: They also demonstrate the beneﬁts of their approach (compared to Peters et al.
Revised: They also demonstrate the beneﬁts of their approach (compared to Peters et al.

Original: in identifying all direct ancestors of the target variable.
Revised: in identifying all direct ancestors of the target variable.

Original: Arjovsky et al.
Revised: Arjovsky et al.

Original: (2019) propose invariant risk minimization , with the goal of learning a data representation such that the optimal predictor is shared across environment.
Revised: (2019) propose invariant risk minimization , with the goal of learning a data representation such that the optimal predictor is shared across environments.

Original:  A NALYSIS OF A T OY M ODEL
Revised:  A NALYSIS OF A T OY M ODEL

Original: We brieﬂy review the OLS estimator for the model Y =
Revised: We brieﬂy review the OLS estimator for the model Y =

Original: Xβ + (cid:15) , where Y ∈ R n is the target, X ∈
Revised: Xβ + (cid:15) , where Y ∈ R n is the target, X ∈

Original: R n × p the design matrix, β ∈ R p the coefﬁcient vector we want to estimate, and (cid:15) ∼ N (0 , σ 2 (cid:15) I n ) an iid noise term.
Revised: R n × p the design matrix, β ∈ R p the coefﬁcient vector we want to estimate, and (cid:15) ∼ N (0 , σ 2 (cid:15) I n ) an iid noise term.

Original: The OLS estimate β ols is given by Cov( X, X ) β ols =
Revised: The OLS estimate β ols is given by Cov( X, X ) β ols =

Original: Cov( X, Y ) .
Revised: Cov( X, Y ) .

Original: Representing
Revised: Representing

Original: Var [ X i ] as σ 2 x i and Cov( X i , X j ) as σ x i ,x j , if we observe only two covariates ( p = 2) , then:
Revised: Var [ X i ] as σ 2 x i and Cov( X i , X j ) as σ x i ,x j , if we observe only two covariates ( p = 2) , then:

Original: [Equation]
Revised: [Equation]

Original: Our analysis adopts the structural causal model (SCM) framework (Pearl, 2009), formalizing causal relationships via Directed Acyclic Graphs (DAGs).
Revised: Our analysis adopts the structural causal model (SCM) framework (Pearl, 2009), formalizing causal relationships via Directed Acyclic Graphs (DAGs).

Original: Each edge of the form A → B ∈ E in a DAG
Revised: Each edge of the form A → B ∈ E in a DAG

Original: G =
Revised: G =

Original: ( V , E ) indicates that the variable A is (potentially) a direct cause of variable B .
Revised: ( V , E ) indicates that the variable A is (potentially) a direct cause of variable B .

Original: All measured variables X ∈ V in the model are deterministic functions of their corresponding parents
Revised: All measured variables X ∈ V in the model are deterministic functions of their corresponding parents

Original: Pa ( X ) ⊆
Revised: Pa ( X ) ⊆

Original: V and a set of jointly independent noise terms.
Revised: V and a set of jointly independent noise terms.

Original: For simplicity, we work with linear Gaussian SCMs in the presence of a single confounder where each variable is a linear function of its parents and the noise terms are assumed to be additive and Gaussian.
Revised: For simplicity, we work with linear Gaussian SCMs in the presence of a single confounder where each variable is a linear function of its parents and the noise terms are assumed to be additive and Gaussian.

Original: We look at both causal and anticausal learning settings.
Revised: We look at both causal and anticausal learning settings.

Original: In the former, we assume that a document causes the applicability of the label (as in annotation, where the document truly causes the label).
Revised: In the former, we assume that a document causes the applicability of the label (as in annotation, where the document truly causes the label).

Original: In the latter interpretation, we assume that the label is one latent variable (among many) that causes features of the document (as when a reviewer’s “actual sentiment” inﬂuences what they write).
Revised: In the latter interpretation, we assume that the label is one latent variable (among many) that causes features of the document (as when a reviewer’s “actual sentiment” inﬂuences what they write).

Original: For simplicity, we assume that the latent variables are correlated due to confounding but that each latent causes a distinct set of observed features.
Revised: For simplicity, we assume that the latent variables are correlated due to confounding but that each latent causes a distinct set of observed features.

Original: Without loss of generality, we assume that all variables have zero mean.
Revised: Without loss of generality, we assume that all variables have zero mean.

Original: Both DAGs contain the four random variables z, x 1 , x 2 , y and the anticausal DAG also contains some additional latent variables q (Figure 1).
Revised: Both DAGs contain the four random variables z, x 1 , x 2 , y and the anticausal DAG also contains some additional latent variables q (Figure 1).

Original: We relegate all derivations to Appendix A.
Revised: The derivations are standard and are included in Appendix A.

Original: 3. T HE C AUSAL S ETTING
Revised: 3. T HE C AUSAL S ETTING

Original: We now focus on the causal setting (Figure 1a, 1b) Let the Gaussian SCM be deﬁned as follows where the noise term for variable x is deﬁned as u x :
Revised: We now focus on the causal setting (Figure 1a, 1b) Let the Gaussian SCM be deﬁned as follows where the noise term for variable x is deﬁned as u x :

Original: [Equation]
Revised: [Equation]

Original: Applying OLS [Equation] we obtain β ols= a and β ols= 0 .
Revised: Applying OLS, we obtain β ols= a and β ols= 0 .

Original: However, consider what happens if we only observe x 1 via a noisy proxy (cid:102) x 1 ∼ N ( x 1 , σ 2 u x 1 + σ 2 (cid:15) x 1 ) (Figure 1b).
Revised: However, consider what happens if we only observe x 1 via a noisy proxy (cid:102) x 1 ∼ N ( x 1 , σ 2 u x 1 + σ 2 (cid:15) x 1 ) (Figure 1b).

Original: Assuming, (cid:15) x 1 ⊥⊥ ( x 1 , x 2 , y ) , from Eq.
Revised: Assuming, (cid:15) x 1 ⊥⊥ ( x 1 , x 2 , y ) , from Eq.

Original: 1 we get the estimates (cid:100) β ols 1 and (cid:100)
Revised: 1 we get the estimates (cid:100) β ols 1 and (cid:100)

Original: β ols 2 (Eq. 3) in the presence of observation noise on x 1 .
Revised: β ols 2 (Eq. 3) in the presence of observation noise on x 1 .

Original: [Equation]
Revised: [Equation]

Original: As we can see, (cid:100) β ols∝ 1 σ 2 (cid:15)x 1 .
Revised: As we can see, (cid:100) β ols∝ 1 σ 2 (cid:15)x 1 .

Original: This shows us that as σ 2 (cid:15) x 1 increases, | (cid:100)
Revised: This shows us that as σ 2 (cid:15) x 1 increases, | (cid:100)

Original: β ols 1 | (magnitude of the coefﬁcient for x 1 ) decreases and | (cid:100)
Revised: β ols 1 | (the magnitude of the coefﬁcient for x 1 ) decreases and | (cid:100)

Original: β
Revised: β

Original: ols 2 | (magnitude of the coefﬁcient for x 2 ) increases.
Revised: ols 2 | (the magnitude of the coefﬁcient for x 2 ) increases.

Original: The asymptotic OLS estimates in the presence of inﬁnite observational noise can be seen to be: limσ 2 (cid:15)x 1 →∞ (cid:100) β ols [Equation]
Revised: The asymptotic OLS estimates in the presence of inﬁnite observational noise is lim

Original: where as (cid:100) β ols 2 converges to a ﬁnite non-zero value.
Revised: σ 2 (cid:15)x 1 →∞ (cid:100) β ols= 0 , whereas (cid:100) β olsconverges to a ﬁnite non-zero value.

Original: On the other hand, observing a noisy version of x 2 will not affect our OLS estimates if there is no measurement error on x 1 .
Revised: On the other hand, observing a noisy version of x 2 will not affect our OLS estimates if there is no measurement error on x 1 .

Original: These simple graphs provide qualitative insights into when we should expect a model to rely on spurious patterns.
Revised: These simple graphs provide qualitative insights into when we should expect a model to rely on spurious patterns.

Original: In the causal setting, under perfect measurement, the causal variable d-separates the non-causal variable from the label (Figure 1a).
Revised: In the causal setting, under perfect measurement, the causal variable d-separates the non-causal variable from the label (Figure 1a).

Original: However, under observation noise, a predictor will rely on the non-causal variable (Eq. 3).
Revised: However, under observation noise, a predictor will rely on the non-causal variable (Eq. 3).

Original: Moreover, when the causal feature is noisily observed, additional observation noise on non-causal features yields models that are more reliant on causal features.
Revised: Moreover, when the causal feature is noisily observed, additional observation noise on non-causal features yields models that are more reliant on causal features.

Original: We argue that while review text is not noisily observed per se, learning with imperfect feature representations acquired by training ML models on ﬁnite samples has an effect that is analogous to learning with observation noise.
Revised: We argue that while review text is not noisily observed per se, learning with imperfect feature representations acquired by training deep networks on ﬁnite samples has an effect that is analogous to learning with observation noise.

Original: Connection to Counterfactually Augmented Data In the causal setting, intervening on the causal feature, d-separates the label y from the non-causal feature x 2 , and thus models trained on samples from the interventional distribution will rely solely on the causal feature, even when it is noisily observed.
Revised: Connection to Counterfactually Augmented Data In the causal setting, intervening on the causal feature, d-separates the label y from the non-causal feature x 2 , and thus models trained on samples from the interventional distribution will rely solely on the causal feature, even when it is noisily observed.

Original: We argue that in a qualitative sense, the process of generating CAD resembles such an intervention, however instead of intervening randomly, we ensure that for each example, we produce two sets of values of x 1 , one such that the label is applicable and one such that it is not applicable.
Revised: We argue that in a qualitative sense, the process of generating CAD resembles such an intervention, however instead of intervening randomly, we ensure that for each example, we produce two sets of values of x 1 , one such that the label is applicable and one such that it is not applicable.

Original: One is given in the dataset, and the other is produced via the revision.
Revised: One is given in the dataset, and the other is produced via the revision.

Original: 3. A N A NTICAUSAL I NTERPRETATION
Revised: 3. A N A NTICAUSAL I NTERPRETATION

Original: Alternatively, rather than thinking of features causing the applicable label, we might think of the “causal feature” as a direct effect of the label (not a cause).
Revised: Alternatively, rather than thinking of features causing the applicable label, we might think of the “causal feature” as a direct effect of the label (not a cause).

Original: In this case, so long as the relationship is truly not deterministic, even absent noisy observation, conditioning on the causal feature does not d-separate the label from the non-causal feature and thus models should be expected to assign weight to both causal and non-causal variables.
Revised: In this case, so long as the relationship is truly not deterministic, even absent noisy observation, conditioning on the causal feature does not d-separate the label from the non-causal feature and thus models should be expected to assign weight to both causal and non-causal variables.

Original: As in the causal setting, as we increase observation noise on the causal variable, the weight assigned to the non-causal variable should increase.
Revised: As in the causal setting, as we increase observation noise on the causal variable, the weight assigned to the non-causal variable should increase.

Original: Conversely, as in the causal setting with observation noise on x 1 , as observation noise on the non-causal feature x 2 increases, we expect the learned predictor to rely more on the causal feature.
Revised: Conversely, as in the causal setting with observation noise on x 1 , as observation noise on the non-causal feature x 2 increases, we expect the learned predictor to rely more on the causal feature.

Original: We derive the OLS coefﬁcients (including under the presence of observational noise, Fig.
Revised: We derive the OLS coefﬁcients (including under the presence of observational noise, Fig.

Original: 1d) in this setting in Appendix A.2.
Revised: 1d) in this setting in Appendix A.2.

Original: Connection to Counterfactually Augmented Data In this interpretation, we think of CAD as a process by which we (the designers of the experiment) intervene on the label itself and the human editors, play the role of a simulator that we imagine to be capable of generating a counterfactual example, holding all other latent variables constant.
Revised: Connection to Counterfactually Augmented Data In this interpretation, we think of CAD as a process by which we (the designers of the experiment) intervene on the label itself and the human editors, play the role of a simulator that we imagine to be capable of generating a counterfactual example, holding all other latent variables constant.

Original: In the sentiment case, we could think of the editors as providing us with the review that would have existed had the sentiment been ﬂipped, holding all other aspects of the review constant.
Revised: In the sentiment case, we could think of the editors as providing us with the review that would have existed had the sentiment been ﬂipped, holding all other aspects of the review constant.

Original: Note that by intervening on the label, we d-separate it from the spurious correlate x 2 (Figure 1c).
Revised: Note that by intervening on the label, we d-separate it from the spurious correlate x 2 (Figure 1c).

Original: 3. I NSIGHTS AND T ESTABLE H YPOTHESES
Revised: 3. I NSIGHTS AND T ESTABLE H YPOTHESES

Original: In both the causal and anticausal models, the mechanism underlying the causal relationship that binds x 1 to y (regardless of direction) is that binding language to a semantic concept (such as sentiment), which we expect to be more durable than the more capricious relationships among the background variables, e.g., those linking genre and production quality.
Revised: In both the causal and anticausal models, the mechanism underlying the causal relationship that binds x 1 to y (regardless of direction) is that binding language to a semantic concept (such as sentiment), which we expect to be more stable across settings than the more capricious relationships among the background variables, e.g., those linking genre and production quality.

Original: In that spirit, if spans edited to generate counterfactually revised data (CRD) aretruly analogous to the causal (or anticausal) variables, in the causal (or anticausal) graphs, then we might expect that noising those spans (e.g. by random word replacement) should lead to models that rely more on noncausal features and perform worse on out of domain data.
Revised: In that spirit, if spans edited to generate counterfactually revised data (CRD) are analogous to the causal (or anticausal) variables, in the causal (or anticausal) graphs, then we might expect that noising those spans (e.g. by random word replacement) should lead to models that rely more on noncausal features and perform worse on out of domain data.

Original: On the other hand, we expect that noising unedited spans should have the opposite behavior, leading to degraded in-domain performance, but comparatively better out-of-domain performance.
Revised: On the other hand, we expect that noising unedited spans should have the opposite behavior, leading to degraded in-domain performance, but comparatively better out-of-domain performance.

Original: In the remainder of the paper, we investigate these hypotheses, ﬁnding evidence that qualitatively conﬁrms the predictions of our theory.
Revised: In the remainder of the paper, we investigate these hypotheses, ﬁnding evidence that qualitatively conﬁrms the predictions of our theory.

Original: We freely acknowledge the speculative nature of this analysis and concede that the mapping between the messy unstructured data we wish to model and the neatly disentangled portrait captured by our linear Gaussian models leaves a gap to be closed through further iterations of theoretical reﬁnement and scientiﬁc experiment.
Revised: We freely acknowledge the speculative nature of this analysis and concede that the mapping between the messy unstructured data we wish to model and the neatly disentangled portrait captured by our linear Gaussian models leaves a gap to be closed through further iterations of theoretical reﬁnement and scientiﬁc experiment.

Original: That said, we stress a couple points: (i) the theory doesn’t in fact place so onerous features are disentangled as on might assume at ﬁrst glance—models learned on any linear projection of the causal and anti-causal features will give identical results even if the basis were not recoverable; (ii) we reiterate that while our model does not explicitly handle the dynamics of learning deep models on real-world dataset (settings for which most remains, in some sense, gestural), we believe that observation noise in our models provides a useful analogy for ﬁtting models on imperfect representations learned from ﬁnite samples.

Original: Ultimately, our argument is not that the theory fully accounts for counterfactually augmented data but instead that it is a useful abstraction for formalizing two (very different) perspectives on how to conceive of CAD, and for suggesting interesting hypotheses amenable to empirical veriﬁcation.
Revised: Ultimately, our argument is not that this simple analysis fully accounts for counterfactually augmented data but instead that it is a useful abstraction for formalizing two (very different) perspectives on how to conceive of CAD, and for suggesting interesting hypotheses amenable to empirical veriﬁcation.

Original:  E MPIRICAL R ESULTS
Revised:  E MPIRICAL R ESULTS

Original: If spans marked as rationales by humans via editing or highlighting are truly indicative of causal variables then noising those spans should lead to models that rely more on non-causal features and  perform worse on out-of-domain data, and noising  unmarked spans  should have the opposite behavior.
Revised: If spans marked as rationales by humans via editing or highlighting are analogous to causal features, then noising those spans should lead to models that rely more on non-causal features and thus perform worse on out-of-domain data, and noising the unmarked spans (analagous to non-causal features) should have the opposite behavior.

Original: In this section, we test these hypotheses empirically on real-world datasets.
Revised: In this section, we test these hypotheses empirically on real-world datasets.

Original: Additionally, we investigate whether indeed feedback from human workers provides us with capabilities that are qualitatively different from what might be achieved via automated feature attribution methods such as attention and  whether CAD  offers qualitative advantages over what might be achieved via automatic sentiment-ﬂipping methods through experiments with text style transfer algorithms.
Revised: Additionally, we investigate whether the feedback from human workers is yielding anything qualitatively different from what might be seen with spans marked by automated feature attribution methods such as attention and saliency. Along similar, lines we ask whether CAD in the ﬁrst place offers qualitative advantages over what might be achieved via automatic sentimentﬂipping methods through experiments with text style transfer algorithms.

Original: We conduct experiments on sentiment analysis (Zaidan et al., 2007; Kaushik et al., 2020) and NLI (DeYoung et al., 2020).
Revised: We conduct experiments on sentiment analysis (Zaidan et al., 2007; Kaushik et al., 2020) and NLI (DeYoung et al., 2020).

Original: All datasets are accompanied with human feedback (tokens deemed relevant to the label’s applicability) which we refer to as rationales .
Revised: All datasets are accompanied with human feedback (tokens deemed relevant to the label’s applicability) which we refer to as rationales .

Original: For the ﬁrst set of experiments, we rely on four models: Support Vector Machines (SVMs), Bidirectional Long Short-Term Memory Networks (BiLSTMs) with Self-Attention (Graves & Schmidhuber, 2005), BERT (Devlin et al., 2019), and Longformer (Beltagy et al., 2020).
Revised: For the ﬁrst set of experiments, we rely on four models: Support Vector Machines (SVMs), Bidirectional Long Short-Term Memory Networks (BiLSTMs) with Self-Attention (Graves & Schmidhuber, 2005), BERT (Devlin et al., 2019), and Longformer (Beltagy et al., 2020).

Original: For the second set of experiments, we rely on four state-of-the-art style transfer models representative of different methodologies, each representative of a different approach to automatically generate new examples with ﬂipped labels (Hu et al., 2017;
Revised: For the second set of experiments, we rely on four state-of-the-art style transfer models representative of different methodologies, each representative of a different approach to automatically generate new examples with ﬂipped labels (Hu et al., 2017;

Original: Li et al., 2018; Sudhakar et al., 2019; Madaan et al., 2020).
Revised: Li et al., 2018; Sudhakar et al., 2019; Madaan et al., 2020).

Original: To evaluate classiﬁer performance on the resulting augmented data, we consider SVMs, Naive Bayes (NB), BiLSTMs with Self Attention, and BERT.
Revised: To evaluate classiﬁer performance on the resulting augmented data, we consider SVMs, Naive Bayes (NB), BiLSTMs with Self Attention, and BERT.

Original: We relegate implementation details to Appendix B.
Revised: We relegate implementation details to Appendix B.

Original: For sentiment analysis, we use SVM, BiLSTM with Self Attention, BERT, and Longformer models.
Revised: For sentiment analysis, we use SVM, BiLSTM with Self Attention, BERT, and Longformer models.

Original: In each document, we replace a fraction of rationale (or non-rationale ) tokens with random tokens sampled from the vocabulary, and train our models, repeating the process 5 times.
Revised: In each document, we replace a fraction of rationale (or non-rationale ) tokens with random tokens sampled from the vocabulary, and train our models, repeating the process 5 times.

Original: We perform similar experiments for NLI using BERT.
Revised: We perform similar experiments for NLI using BERT.

Original: As an individual premise-hypothesis pair is often not as long as a movie review, many pairs only have one or two words marked as rationales .
Revised: As an individual premise-hypothesis pair is often not as long as a movie review, many pairs only have one or two words marked as rationales .

Original: To observe the effects from gradually injecting noise on rationales or non-rationales , we select only those premise-hypothesis pairs that have a minimum 10 tokens marked as rationales .
Revised: To observe the effects from gradually injecting noise on rationales or non-rationales , we select only those premise-hypothesis pairs that have a minimum 10 tokens marked as rationales .

Original: Since no neutral pairs exist with 10 or more rationale tokens, we consider only a binary classiﬁcation setting (entailment-contradiction), and downsample the majority class to ensure a 50 : 50 label split.
Revised: Since no neutral pairs exist with 10 or more rationale tokens, we consider only a binary classiﬁcation setting (entailment-contradiction), and downsample the majority class to ensure a 50 : 50 label split.

Original: Figures 2 and 3 show the difference in mean accuracy over 5 runs.
Revised: Figures 2 and 3 show the difference in mean accuracy over 5 runs.

Original: For all classiﬁers, as theamount of noise in rationales increases, in-sample accuracy stays relatively stable compared to out-of-domain accuracy.
Revised: For all classiﬁers, as the noise in rationales increases, in-sample accuracy stays relatively stable compared to out-of-domain accuracy.

Original: An SVM classiﬁer trained on the original 1 . 7 k IMDb reviews from Kaushik et al.
Revised: An SVM classiﬁer trained on the original 1 . 7 k IMDb reviews from Kaushik et al.

Original: (2020) obtains 87 .
Revised: (2020) obtains 87 .

Original: 8% accuracy on the IMDb test set and 79 .
Revised: 8% accuracy on the IMDb test set and 79 .

Original: 9% on Yelp reviews.
Revised: 9% on Yelp reviews.

Original: 1 As a greater fraction of rationales are replaced with random words from the vocabulary, the classiﬁer experiences a drop of ≈ 11% by the time all rationale tokens are replaced with noise.
Revised: 1 As a greater fraction of rationales are replaced with random words from the vocabulary, the classiﬁer experiences a drop of ≈ 11% by the time all rationale tokens are replaced with noise.

Original: However, it experiences an 28 .
Revised: However, it experiences an 28 .

Original: 7% drop in accuracy on Yelp reviews.
Revised: 7% drop in accuracy on Yelp reviews.

Original: Similarly, on the same datasets, a ﬁne-tuned BERT classiﬁer sees its in-sample accuracy drop by 18 . 4% , and by 31 .
Revised: Similarly, on the same datasets, a ﬁne-tuned BERT classiﬁer sees its in-sample accuracy drop by 18 . 4% , and by 31 .

Original: 4% on Yelp as rationale tokens replaced by noise go from 0 to 100% .
Revised: 4% on Yelp as rationale tokens replaced by noise go from 0 to 100% .

Original: However, as more non-rationales are replaced with noise, in-sample accuracy for SVM goes down by ≈ 10% but increases by 1 . 5% on Yelp.
Revised: However, as more non-rationales are replaced with noise, in-sample accuracy for SVM goes down by ≈ 10% but increases by 1 . 5% on Yelp.

Original: For BERT, in-sample accuracy decreases by only 16 .
Revised: For BERT, in-sample accuracy decreases by only 16 .

Original: 1% and only 13 .
Revised: 1% and only 13 .

Original: 6% on Yelp. (Figure 2a as well as Appendix Table 3, and Appendix Figure 4a)
Revised: 6% on Yelp (Also see Appendix Table 3, and Appendix Figure 4a).

Original: We obtain similar results using rationales identiﬁed via feature feedback.
Revised: We obtain similar results using rationales identiﬁed via feature feedback.

Original: An SVM classiﬁer trained on reviews from Zaidan et al.
Revised: An SVM classiﬁer trained on reviews from Zaidan et al.

Original: (2007) sees in-sample accuracy drop by 11% , and accuracy on Yelpdrop by 16 .
Revised: (2007) sees in-sample accuracy drop by 11% , and accuracy on Yelp

Original: 9% as noise is inserted on rationales but goes down by 17 .

Original: 3% and 14 .

Original: 6% , respectively when noise is inserted in non-rationales .

Original: For Longformer, in-sample accuracy drops by 14% and accuracy on Yelp goes down by 26 .

Original: 4% compared to a drop of 17 .

Original: 3% and gain of 3 . 9% respectively when noise is inserted in non-rationales .

Original: Similar behavior is observed across datasets and models (Figure 3a as well as Appendix Table 6, and Appendix Figure 5a).

Original: [Figure]
Revised: [Figure]

Original: 20 10a cc u r a cy model =
Revised: 20 10a cc u r a cy model =

Original: SVM
Revised: SVM

Original: [Figure]
Revised: [Figure]

Original: model =
Revised: model =

Original: Bi-LSTM w/ SA
Revised: Bi-LSTM w/ SA

Original: [Figure]
Revised: [Figure]

Original:  = Longformer
Revised: model = Longformer

Original: Dataset
Revised: Dataset

Original: In-sample test
Revised: In-sample test

Original: CRD
Revised: CRD

Original: Amazon
Revised: Amazon

Original: Semeval
Revised: Semeval

Original: Yelp
Revised: Yelp

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Revised: drop by 16 .

Original:  For NLI, while the in-sample accuracyof BERT ﬁne-tuned on an SNLI subsample drops by ≈ 20% when rationales are replaced with noise, out-of-domain accuracy  goes down by 21 . 3 – 31 .
Revised: 9% as noise is inserted on rationales but goes down by 17 . 3% and 14 . 6% , respectively when noise is inserted in non-rationales . For Longformer, in-sample accuracy drops by 14% and accuracy on Yelp goes down by 26 .

Original: 5% on various datasets (Table 1).
Revised: 4% compared to a drop of 17 .

Original: On the other hand, if non-rationales are replaced with noise, in-sample
Revised: 3% and gain of 3 . 9% , respectively,

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: {+( 3 . 4 k ) 63 .
+}
Revised: {+( 3 . 4 k ) 63 .
+}

Original: Accuracy on Yelp Reviews
Revised: Accuracy on Yelp Reviews

Original: CAD [Table]
Revised: CAD ( 3 . 4 k ) 85.

Original: Orig [Table]
Revised: Orig. & Hu et al.

Original: Orig [Table]
Revised: Orig. & Li et al.

Original: [Table] [Table]
Revised: Orig. & Sudhakar et al. Orig. & Madaan et al.

Original: Orig
Revised: Orig.

Original: [Table]{+( 3 . 4 k ) 
+}
Revised: {+( 3 . 4 k ) 
+}

Original: & Sudhakar
Revised: .72 .84 .when

Original: [Table]
Revised: noise is inserted in non-rationales .

Original: [Table] Madaan [Table] [Table]
Revised: Similar patterns are observed across datasets and models (see Figure 3a, Appendix Table 6, and Appendix Figure 5a).

Original: Orig
Revised: For NLI, the in-sample accuracy of BERT ﬁne-tuned on an SNLI subsample drops by ≈ 20% when rationales are replaced with noise, and out-of-domain accuracy goes down by 21 . 3 – 31 .

Original: [Table]
Revised: 5% on various datasets (Table 10).

Original:  accuracy goes down by 6 . 2% but out-of-domain accuracy drops by only 2 . 3 – 5 . 5% .
Revised: Whereas, if non-rationales are replaced with noise, in-sample accuracy goes down by 6 . 2% but out-of-domain accuracy drops by only 2 . 3 – 5 . 5% .

Original: These results support our hypothesis that spans marked by humans as causing a label are truly indicative of causal variables.
Revised: These results support our hypothesis that spans marked by humans as causing a label are analogous to causal variables.

Original: Interestingly, in our NLI experiments, for various models the drops in both in-sample and out-ofdomain accuracy are greater in magnitude when noise is injected in rationales versus when it is injected in non-rationales .
Revised: Interestingly, in our NLI experiments, for various models the drops in both in-sample and out-ofdomain accuracy are greater in magnitude when noise is injected in rationales versus when it is injected in non-rationales .

Original: This is opposite to what we observe in sentiment analysis.
Revised: This is opposite to what we observe in sentiment analysis.

Original: We conjec- ture that these results are due to the fact that in our experiment design for NLI, we only keep those premise-hypothesis pairs that contain at least 10 tokens marked as rationales so we can observe the difference in accuracy as the amount of noise increases.
Revised: We conjecture that these results are due to the fact that in our experiment design for NLI, we only keep those premise-hypothesis pairs that contain at least 10 tokens marked as rationales so we can observe the difference in accuracy as the amount of noise increases.

Original: A consequence of this selection is that many pairs selected have many more tokens marked as rationales than non-rationales , whereas, in sentiment analysis this is the opposite.
Revised: A consequence of this selection is that many pairs selected have many more tokens marked as rationales than non-rationales , whereas, in sentiment analysis this is the opposite.

Original: Hence, in NLI when some percentage of rationales are replaced by noise, this corresponds to many more edited tokens than when a corresponding percentage of non-rationales are noised.
Revised: Hence, in NLI when some percentage of rationales are replaced by noise, this corresponds to many more edited tokens than when a corresponding percentage of non-rationales are noised.

Original: To comparethe value of human feedback to automatic feature attribution methods such as attention (Bahdanau et al., 2015), we conduct the same set of experiments assuming tokens attended to (or not) by an attention based classiﬁer (BiLSTM with Self-Attention) or identiﬁed as highly inﬂuential by a gradient based feature attribution method (salience scores) as new rationales (or non-rationales ).
Revised: To compare human feedback to automatic feature attribution methods such as attention (Bahdanau et al., 2015) and gradient based saliency methods (Li et al., 2016), we conduct the same set of experiments assuming tokens attended to (or not) by an attention based classiﬁer (BiLSTM with Self-Attention) or identiﬁed as highly inﬂuential by a gradient based feature attribution method (salience scores) as new rationales (or non-rationales ).

Original: In this case, unlike our ﬁndings with human feedback, we observe markedly different behavior than predicted by our analysis of the toy causal model (See Figures 2b, 2c, 3b, and 3c, as well as Appendix Tables 4, 5, 7, and 8).
Revised: In this case, unlike our ﬁndings with human feedback, we observe markedly different behavior than predicted by our analysis of the toy causal model (See Figures 2b, 2c, 3b, and 3c; and Appendix Tables 4, 5, 7, and 8).

Original: In some cases, non-causal features may generalize to  a new domain.
Revised: While we might not expect spurious signals to be as reliable out of domain, that does not mean that they will always fail. For example, while the associations between genre and sentiment learned from a dataset of book reviews might not hold in a dataset of kitchen appliances, but nevertheless hold in a dataset of audiobook reviews.

Original: In such settings, even though noising non-causal features would lead to models relying more on causal features, it may not result in better out of domain performance.
Revised: In such settings, even though noising non-causal features would lead to models relying more on causal features, this may not result in better out-of-domain performance.

Original: We also look at whether we really need to go through the process of collecting CAD (or humanannotated rationales) at all or if automated methods for generating “counterfactuals” might obtain similar gains in out-of-domain performance.
Revised: We also look at whether we really need to go through the process of collecting CAD (or humanannotated rationales) at all or if automated methods for generating “counterfactuals” might obtain similar gains in out-of-domain performance, as the former could be an expensive process.

Original: We experiment with state-of-the-art style transfer methods to convert Positive reviews into Negative and vice versa.
Revised: We experiment with state-of-the-art style transfer methods to convert Positive reviews into Negative and vice versa.

Original: Ideally, we would expect these methods to preserve a document’s “content” while modifying the attributes that relate to sentiment (if they obtain perfect disentanglement in the feature space).
Revised: Ideally, we would expect these methods to preserve a document’s “content” while modifying the attributes that relate to sentiment (if they obtain perfect disentanglement in the feature space).

Original: Sentiment classiﬁers trained on original and sentiment-ﬂipped reviews generated using style transfer methods often give better out-of-domain performance compared to training only on original data of same size (Table 2).
Revised: Sentiment classiﬁers trained on original and sentiment-ﬂipped reviews generated using style transfer methods often give better out-of-domain performance compared to training only on original data of same size (Table 2).

Original: However, models trained on CAD perform even better across all datasets, hinting at the value of human feedback.
Revised: However, models trained on CAD perform even better across all datasets, hinting at the value of human feedback.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: While prior work offers promising clues to the beneﬁts of CAD generated through human-in-theloop mechanisms, previous work lacked formal frameworks for thinking about the technique, or comparisons to plausible alternatives.
Revised: While prior work offers promising clues to the beneﬁts of CAD generated through human-in-theloop mechanisms, previous work lacked formal frameworks for thinking about the technique, or comparisons to plausible alternatives.

Original: In this paper, through simple analysis on toy linear Gaussian models followed by a large-scale empirical investigation on sentiment analysis and NLI tasks, we formalize CAD and make strides towards understanding its practical efﬁcacy.
Revised: In this paper, through simple analysis on toy linear Gaussian models followed by a large-scale empirical investigation on sentiment analysis and NLI tasks, we formalize CAD and take some initial steps towards understanding its practical efﬁcacy.

Original: Our analysis suggests that data corrupted by adding noise to rationale spans (analogous to adding noise to causal features) will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain.
Revised: Our analysis suggests that data corrupted by adding noise to rationale spans (analogous to adding noise to causal features) will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain.

Original: Our empirical study focuses on sentiment analysis and NLI and our ﬁndings remain consistent across datasets and models.
Revised: Our empirical study focuses on sentiment analysis and NLI and our ﬁndings remain consistent across datasets and models.

Original: Furthermore, the two tasks are subjectively very different as sentiment analysis requires a strong consideration of expressions of opinion than stated facts, whereas NLI is the opposite.
Revised: Furthermore, the two tasks are subjectively very different as sentiment analysis requires a strong consideration of expressions of opinion than stated facts, whereas NLI is the opposite.

Revised: We also show that models trained on the augmentation of original data and revised data generated by style transfer methods had better out-of-domain generalization in some cases compared to models trained on original data alone, but performed worse than models trained on CAD.

Original: In future work, we will look at how these ﬁndings generalize to other domains, including computer vision, and investigate the surprisingly low susceptibility of pre-trained transformers to spurious associations. 
Revised: In future work, we will look at how these ﬁndings generalize to other domains, including computer vision, and investigate the surprisingly low susceptibility of pre-trained transformers to spurious associations. A CKNOWLEDGEMENTS

Revised: The authors are grateful to NVIDIA for providing GPUs to conduct the experiments, Salesforce

Revised: Research and Facebook AI for their ﬁnancial support, and Sanket Mehta, Sina Fazelpour and Tejas

Revised: Khot for our discussions and their valuable feedback.

Original: A OLS E STIMATION U NDER N OISY M EASUREMENT A. C AUSAL SETTING
Revised: A OLS E STIMATION U NDER N OISY M EASUREMENT A. C AUSAL SETTING

Original: Let the Gaussian SCM be deﬁned as follows where the noise term for variable x is deﬁned as u x :
Revised: Let the Gaussian SCM be deﬁned as follows where the noise term for variable x is deﬁned as u x :

Original: [Equation]
Revised: [Equation]

Original: Then if we were to solve the linear regression problem y = x 1 β 1 + x 2 β 2 + β
Revised: Then if we were to solve the linear regression problem y = x 1 β 1 + x 2 β 2 + β

Original: [Equation]
Revised: [Equation]

Original: then using Eq.
Revised: then using Eq.

Original: 1 we obtain the following values for β ols 0 , β ols 1 and β ols 2 :
Revised: 1 we obtain the following values for β ols 0 , β ols 1 and β ols 2 :

Original: [Equation]
Revised: [Equation]

Original: However
Revised: However

Original: [Equation]
Revised: [Equation]

Original: if the setting is slightly different
Revised: if the setting is slightly different

Original: [Equation]
Revised: [Equation]

Original: and we observe a noisy version of x
Revised: and we observe a noisy version of x

Original: [Equation]
Revised: [Equation]

Original: given by (cid:102) x 1 :
Revised: given by (cid:102) x 1 :

Original: [Equation]
Revised: [Equation]

Original: (cid:15) x 1 ⊥⊥ ( x 1 , x 2 , y ) ,
Revised: (cid:15) x 1 ⊥⊥ ( x 1 , x 2 , y ) ,

Original: [Equation]

Original: [Equation]
Revised: [Equation]

Original: Plugging these values into Eq.
Revised: Plugging these values into Eq.

Original: 1 we get the OLS estimates (cid:100) β ols 1 and (cid:100) β
Revised: 1 we get the OLS estimates (cid:100) β ols 1 and (cid:100) β

Original: ols 2 in the presence of observation noise on X 1 :
Revised: ols 2 in the presence of observation noise on X 1 :

Original: [Equation]
Revised: [Equation]

Original: As we can see λ c > 0 and λ c ∝ σ 2 (cid:15) x 1 .
Revised: As we can see λ c > 0 and λ c ∝ σ 2 (cid:15) x 1 .

Original: This shows us that as σ 2 (cid:15) x 1 increases, | (cid:100)
Revised: This shows us that as σ 2 (cid:15) x 1 increases, | (cid:100)

Original: β ols 1 | (magnitude of the coefﬁcient for X 1 ) decreases and | (cid:100)
Revised: β ols 1 | (magnitude of the coefﬁcient for X 1 ) decreases and | (cid:100)

Original: β
Revised: β

Original: ols 2 | (magnitude of the coefﬁcient for X 2 ) increases.
Revised: ols 2 | (magnitude of the coefﬁcient for X 2 ) increases.

Original: lim
Revised: lim

Original: [Equation]
Revised: [Equation]

Original: (cid:15)x

Original: [Equation]

Original: (cid:100)
Revised: (cid:100)

Original: [Equation]

Original: ols

Original: [Equation]
Revised: [Equation]

Original: , and
Revised: , and

Original: [Equation]
Revised: [Equation]

Original: A. A NTICAUSAL SETTING
Revised: A. A NTICAUSAL SETTING

Original: Once again we assume that each variable V is a linear function of its parents
Revised: Once again we assume that each variable V is a linear function of its parents

Original: Pa ( V ) .
Revised: Pa ( V ) .

Original: The noise terms are assumed to be Gaussian and are jointly independent.
Revised: The noise terms are assumed to be Gaussian and are jointly independent.

Original: [Equation]
Revised: [Equation]

Original: If we were to solve the linear regression problem y = x 1 β 1 + x 2 β 2 + β 0 , then using Eq.
Revised: If we were to solve the linear regression problem y = x 1 β 1 + x 2 β 2 + β 0 , then using Eq.

Original: 1 we get the OLS estimates β ols 1 and β ols 2 :
Revised: 1 we get the OLS estimates β ols 1 and β ols 2 :

Original: [Equation]
Revised: [Equation]

Original: However, if the setting is slightly different
Revised: However, if the setting is slightly different

Original: [Equation]
Revised: [Equation]

Original: and we observe a noisy version of x 1 , given by (cid:102) x 1 :
Revised: and we observe a noisy version of x 1 , given by (cid:102) x 1 :

Original: [Equation]
Revised: [Equation]

Original: [Equation]
Revised: [Equation]

Original: Since (cid:15) x 1 ⊥⊥ x 2 , y , in order to obtain expressions for the OLS estimates (cid:100) β ols 1 , (cid:100) β ols 2 in the presence of observation noise, in Eq.
Revised: Since (cid:15) x 1 ⊥⊥ x 2 , y , in order to obtain expressions for the OLS estimates (cid:100) β ols 1 , (cid:100) β ols 2 in the presence of observation noise, in Eq.

Original: 15 we only need to replace σ 2 u x 1 with σ 2 u (cid:102) x 1 , which is given by:
Revised: 15 we only need to replace σ 2 u x 1 with σ 2 u (cid:102) x 1 , which is given by:

Original: [Equation]
Revised: [Equation]

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: where λ x 1 ac > 0 and λ x 1 ac ∝ σ 2 (cid:15) x 1 .
Revised: where λ x 1 ac > 0 and λ x 1 ac ∝ σ 2 (cid:15) x 1 .

Original: Thus, as σ 2 (cid:15) x 1 increases, | (cid:100) β ols 1 | decreases.
Revised: Thus, as σ 2 (cid:15) x 1 increases, | (cid:100) β ols 1 | decreases.

Original: The asymptotic OLS estimates in the presence of inﬁnite observational noise can be seen to be:
Revised: The asymptotic OLS estimates in the presence of inﬁnite observational noise can be seen to be:

Original: [Equation]
Revised: [Equation]

Original: = 0 , where as
Revised: = 0 , where as

Original: [Equation]
Revised: [Equation]

Original: Similarly, if we observe a noisy version of X 2 , given by (cid:102) X 2 :
Revised: Similarly, if we observe a noisy version of X 2 , given by (cid:102) X 2 :

Original: [Equation]
Revised: [Equation]

Original: Since (cid:15) x 2 ⊥⊥ x 1 , y , in order to obtain expressions for the OLS estimates (cid:100) β ols 1 , (cid:100) β ols 2 in the presence of observation noise on non-causal features, in Eq.
Revised: Since (cid:15) x 2 ⊥⊥ x 1 , y , in order to obtain expressions for the OLS estimates (cid:100) β ols 1 , (cid:100) β ols 2 in the presence of observation noise on non-causal features, in Eq.

Original: 15 we only need to replace σ 2 u x 2 with σ 2 u (cid:102) x 2 , which is given by:
Revised: 15 we only need to replace σ 2 u x 2 with σ 2 u (cid:102) x 2 , which is given by:

Original: [Equation]
Revised: [Equation]

Original: where λ x 2 ac > 0 and λ x 2 ac ∝ σ 2 (cid:15) x 2 .
Revised: where λ x 2 ac > 0 and λ x 2 ac ∝ σ 2 (cid:15) x 2 .

Original: Thus, as σ 2 (cid:15) x 2 increases, | (cid:100) β ols 1 | increases.
Revised: Thus, as σ 2 (cid:15) x 2 increases, | (cid:100) β ols 1 | increases.

Original: The asymptotic OLS estimates in the presence of inﬁnite observational noise can be seen to be:
Revised: The asymptotic OLS estimates in the presence of inﬁnite observational noise can be seen to be:

Original: [Equation]
Revised: [Equation]

Original: , where as
Revised: , where as

Original: [Equation]
Revised: [Equation]

Original: B M ODEL I MPLEMENTATION D ETAILS FOR S ECTION
Revised: B M ODEL I MPLEMENTATION D ETAILS FOR S ECTION

Original: Standard Methods We use scikit-learn (Pedregosa et al., 2011) implementations of SVMs and Na¨ıve Bayes for sentiment analysis.
Revised: Standard Methods We use scikit-learn (Pedregosa et al., 2011) implementations of SVMs and Na¨ıve Bayes for sentiment analysis.

Original: We train these models on TF-IDF bag of words feature representations of the reviews (Jones, 1972).
Revised: We train these models on TF-IDF bag of words feature representations of the reviews (Jones, 1972).

Original: We identify parameters for both classiﬁers using grid search conducted over the validation set.
Revised: We identify parameters for both classiﬁers using grid search conducted over the validation set.

Original: BiLSTM We restrict the vocabulary to the most frequent 20 k tokens, replacing out-of-vocabulary tokens by UNK .
Revised: BiLSTM We restrict the vocabulary to the most frequent 20 k tokens, replacing out-of-vocabulary tokens by UNK .

Original: We ﬁx the maximum input length at 330 tokens when training on reviews from
Revised: We ﬁx the maximum input length at 330 tokens when training on reviews from

Original: Kaushik et al.
Revised: Kaushik et al.

Original: (2020) and 2678 when doing so on Zaidan et al.
Revised: (2020) and 2678 when doing so on Zaidan et al.

Original: (2007), and pad smaller reviews.
Revised: (2007), and pad smaller reviews.

Original: Each token is represented by a randomly-initialized 300 -dimensional embedding.
Revised: Each token is represented by a randomly-initialized 300 -dimensional embedding.

Original: Our model consists of a bidirectional LSTM (hidden dimension 128 ) with recurrent dropout (probability 0 . 5 ) and self attention following the embedding layer.
Revised: Our model consists of a bidirectional LSTM (hidden dimension 128 ) with recurrent dropout (probability 0 . 5 ) and self attention following the embedding layer.

Original: We use the self attention implementation discussed in Lin et al.
Revised: We use the self attention implementation discussed in Lin et al.

Original: (2017) with hyperparameter values d = 64 and r = 64 .
Revised: (2017) with hyperparameter values d = 64 and r = 64 .

Original: To generate output, we feed this (ﬁxed-length) representation through a fully-connected hidden layer (hidden dimension 32 ), and then a fully-connected output layer with softmax activation.
Revised: To generate output, we feed this (ﬁxed-length) representation through a fully-connected hidden layer (hidden dimension 32 ), and then a fully-connected output layer with softmax activation.

Original: We train all models for a maximum of 20 epochs using Adam (Kingma & Ba, 2015), with a learning rate of 1e − 4 and a batch size of 16 .
Revised: We train all models for a maximum of 20 epochs using Adam (Kingma & Ba, 2015), with a learning rate of 1e − 4 and a batch size of 16 .

Original: We apply early stopping when validation loss does not decrease for 5 epochs.
Revised: We apply early stopping when validation loss does not decrease for 5 epochs.

Original: Pretrained Transformers We use off-the-shelf uncased BERT Base and Longformer Base models (Wolf et al., 2019), ﬁne-tuning for each task.
Revised: Pretrained Transformers We use off-the-shelf uncased BERT Base and Longformer Base models (Wolf et al., 2019), ﬁne-tuning for each task.

Original: We used BERT for experiments on the smaller IMDb dataset used by Kaushik et al.
Revised: We used BERT for experiments on the smaller IMDb dataset used by Kaushik et al.

Original: (2020) (with a maximum review length of 330 tokens) and Longformer for the dataset presented by Zaidan et al.
Revised: (2020) (with a maximum review length of 330 tokens) and Longformer for the dataset presented by Zaidan et al.

Original: (2007) (with maximum review length of 2678).
Revised: (2007) (with maximum review length of 2678).

Original: To account for BERT’s sub-word tokenization, we set the maximum token length is set at 350 for sentiment analysis and 50 for NLI.
Revised: To account for BERT’s sub-word tokenization, we set the maximum token length is set at 350 for sentiment analysis and 50 for NLI.

Original: In case of Longformer, that is 3072 .
Revised: In case of Longformer, that is 3072 .

Original: 3 We ﬁne-tune BERT up to 20 epochs with same early stopping criteria as for BiLSTM, using the BERT Adam optimizer with a batch size of 16 (to ﬁt on a 16 GB Tesla V- 100 GPU).
Revised: 3 We ﬁne-tune BERT up to 20 epochs with same early stopping criteria as for BiLSTM, using the BERT Adam optimizer with a batch size of 16 (to ﬁt on a 16 GB Tesla V- 100 GPU).

Original: We found learning rates of 5e − 5 and 1e − 5 to work best for sentiment analysis and NLI respectively.
Revised: We found learning rates of 5e − 5 and 1e − 5 to work best for sentiment analysis and NLI respectively.

Original: We ﬁne-tune Longformer for 10 epochs with early stopping, using a batch size of 8 (to ﬁt on 64 GB of GPU memory).
Revised: We ﬁne-tune Longformer for 10 epochs with early stopping, using a batch size of 8 (to ﬁt on 64 GB of GPU memory).

Original: Style Transfer Methods For Hu et al.
Revised: Style Transfer Methods For Hu et al.

Original: (2017), 4 Sudhakar et al.
Revised: (2017), 4 Sudhakar et al.

Original: (2019), 5 and Madaan et al.
Revised: (2019), 5 and Madaan et al.

Original: (2020),we found the default hyperparameters used by the authors to work best on our task.
Revised: (2020),we found the default hyperparameters used by the authors to work best on our task.

Original: In case of Li et al.
Revised: In case of Li et al.

Original: (2018), 7 we followed the training schedule presented in the paper.
Revised: (2018), 7 we followed the training schedule presented in the paper.

Original: However, since the paper does not present results on IMDb reviews, we experimented with multiple values of the salience ratio , and used a salience ratio of 5 . 5 for our downstream task based on transfer accuracy and BLEU scores achieved on the validation set.
Revised: However, since the paper does not present results on IMDb reviews, we experimented with multiple values of the salience ratio , and used a salience ratio of 5 . 5 for our downstream task based on transfer accuracy and BLEU scores achieved on the validation set.

Original: For all style transfer methods, we experimented with multiple sequence lengths, and found that models worked best on sentence level (versus review-level) data, with sequence length of 30 , truncating longer sentences in the process.
Revised: For all style transfer methods, we experimented with multiple sequence lengths, and found that models worked best on sentence level (versus review-level) data, with sequence length of 30 , truncating longer sentences in the process.

Original: For each review, we passed individual sentences through each model and reconstructed whole reviews by joining the resulting sentiment-ﬂipped sentences.
Revised: For each review, we passed individual sentences through each model and reconstructed whole reviews by joining the resulting sentiment-ﬂipped sentences.

Original: C F ULL R ESULTS C ORRESPONDING TO N OISE I NJECTION
Revised: C F ULL R ESULTS C ORRESPONDING TO N OISE I NJECTION

Original: [Table]
Revised: [Table]

Original: BiLSTM with Self Attention
Revised: BiLSTM with Self Attention

Original: In-
Revised: In-

Original: [Table]
Revised: [Table]

Original: test 81 .77 .
Revised: test 81 .77 .

Original: CRD 49 .53 .56 .56 .

Revised: CRD 49 .53 .56 .56 .

Original: Amazon 65 .66 .66 .66 .

Revised: Amazon 65 .66 .66 .66 .

Original: Semeval 59 .58 .

Revised: Semeval 59 .58 .

Original: Yelp 71 .74 .

Revised: Yelp 71 .74 .

Original: BERT
Revised: BERT

Original: In-sample test 87 .88 . 86 .
Revised: In-sample test 87 .88 . 86 .

Original: CRD 82 .92 .

Revised: CRD 82 .92 .

Original: Amazon 76 .78 .78 .79 .75 .71 .67 .65 .65 .63 .61 .
Revised: Amazon 76 .78 .78 .79 .75 .71 .67 .65 .65 .63 .61 .

Original: Semeval 76 .

Revised: Semeval 76 .

Original: Yelp 83 .

Revised: Yelp 83 .

Original: [Table]
Revised: [Table]

Original: Yelp 71 .

Revised: Yelp 71 .

Original: BERT
Revised: BERT

Original: In
Revised: In

Original: [Table]
Revised: [Table]

Original: sample test 87 .
Revised: test 87 .

Original: CRD 82 .91 .
Revised: CRD 82 .91 .

Original: Amazon 76 .77 .79 .78 .79 .79 .79 .79 .79 .78 .79 .
Revised: Amazon 76 .77 .79 .78 .79 .79 .79 .79 .79 .78 .79 .

Original: Semeval 76 .71 .73 .73 .

Revised: Semeval 76 .71 .73 .73 .

Original: Yelp 

Revised: Yelp

Original: Dataset
Revised: Dataset

Original: [Table]
Revised: [Table]

Original: SVM

Original: In
Revised: In

Original: [Table]
Revised: [Table]

Original: test 87 .
Revised: test 87 .

Original: CRD 51 .50 .

Revised: CRD 51 .50 .

Original: Amazon 73 .73 .75 .

Revised: Amazon 73 .73 .75 .

Original: Semeval 62 .62 .63 .63 .

Revised: Semeval 62 .62 .63 .63 .

Original: Yelp 79 .

Revised: Yelp 79 .

Original: BiLSTM with Self Attention
Revised: BiLSTM with Self Attention

Original: In
Revised: In-

Original: [Table]
Revised: [Table]

Revised: test 81 .77 .

Original: CRD 49 .53 .52 .52 .
Revised: CRD 49 .53 .52 .52 .

Original: Amazon 65 .63 .65 .
Revised: Amazon 65 .63 .65 .

Original: Semeval 59 .54 .58 .

Revised: Semeval 59 .54 .58 .

Original: Yelp 71 .
Revised: Yelp 71 .

Original: BERT
Revised: BERT

Original: In
Revised: In

Original: [Table]
Revised: [Table]

Revised: test 87 .86 .86 .85 .

Original: CRD 82 .92 .92 .92 . 86 .
Revised: CRD 82 .92 .92 .92 . 86 .

Original: Amazon 76 .79 .78 .77 .69 .67 .

Revised: Amazon 76 .79 .78 .77 .69 .67 .

Original: Semeval 76 .76 .

Revised: Semeval 76 .76 .

Original: Yelp 83 .85 . 

Revised: Yelp 83 .85 .

Original: [Table]
Revised: [Table]

Original: [Table]

Original: [Table]

Original: [Table]

Original: [Table]

Original: [Table] [Table] [Table] [Table]
Revised: Yelp 71 .

Original: BERT
Revised: BERT

Original: [Table] [Table]{+In-sample test 87 .
+}
Revised: {+In-sample test 87 .
+}

Original: [Table]{+CRD 82 .93 .
+}
Revised: {+CRD 82 .93 .
+}

Original: [Table]{+Amazon 76 .82 .81 .79 .77 .77 .77 .75 .69 .69 .73 . Semeval 76 .82 .82 .81 .79 .78 .76 .74 .
 Yelp 83 .88 .88 .88 .
+}
Revised: {+Amazon 76 .82 .81 .79 .77 .77 .77 .75 .69 .69 .73 . Semeval 76 .82 .82 .81 .79 .78 .76 .74 .
 Yelp 83 .88 .88 .88 .
+}

Original: [Table]
Revised: Dataset

Revised: Percent noise

Original: [Table]
Revised: [Table]

Original: Semeval
Revised: SVM

Original: [Table] [Table] [Table] [Table]{+In-sample test 87 .85 .85 .
+}
Revised: {+In-sample test 87 .85 .85 .
+}

Original: [Table]{+CRD 51 .
+}
Revised: {+CRD 51 .
+}

Original: Amazon 73 .73 .73 .

Revised: Amazon 73 .73 .74 .

Original: Semeval [Table]{+62 .62 .
+}
Revised: Semeval {+62 .62 .
+}

Original: Yelp 79 .

Revised: Yelp 79 .

Original: BiLSTM with Self Attention
Revised: BiLSTM with Self Attention

Original: In-sample test 81 [-.
-]
Revised: In-sample test 81 [-.
-].

Original: CRD 49 [-.50 .51 .
-]
Revised: CRD 49 [-.50 .51 .
-]. 49 .

Original: Amazon 65 .61 .52 .62 .63  .67 [-.67 .
-]
Revised: Amazon 65 .65 .64 .62 .63 .65 .67 [-.67 .
-].65 .

Original: Semeval 59 [-.52 .52 .52 .
-]
Revised: Semeval 59 [-.52 .52 .52 .
-].

Original: Yelp 71 .69 .

Revised: Yelp 71 .73 .75 .

Original: BERT
Revised: BERT

Original: In-sample test 87 .

Revised: In-sample test 87 .

Original: CRD 82.91 .92 .

Revised: CRD 82 .92 .

Original: Amazon 76  .79 [-.79 .
-]
Revised: Amazon 76 .77 .79 [-.79 .
-].77 .

Original: Semeval 76 [-.80 .78 .78 .
-]
Revised: Semeval 76 [-.80 .78 .78 .
-].

Original: Yelp 83 [-.86 .85 .
-]
Revised: Yelp 83 [-.86 .85 .
-]. [Table] [Table]

Original: [Table]
Revised: [Table]

Original: Longformer
Revised: Longformer

Original: In
Revised: [Table] [Table] [Table]

Original: [Table]
Revised: [Table]

Original: test 97 .96 .
Revised: [Table] [Table] [Table] [Table]

Original: CRD 93 .93 .

Revised: CRD 93 .93 .

Original: [-Amazon 81 .77 .65 .65 .64 .
-]
Revised: [-Amazon 81 .77 .65 .65 .64 .
-][Table] [Table]

Original:  Semeval 80 .74 .
Revised: [Table] Semeval [Table] [Table]

Original: [-Yelp 88 .85 .77 .74 .
-]
Revised: [-Yelp 88 .85 .77 .74 .
-][Table] [Table] [Table] [Table]

Original: Dataset
Revised: [Table]

Original: Percent noise
Revised: [Table] [Table] [Table] [Table]

Original: [Table]
Revised: [Table]

Original: SVM
Revised: [Table] [Table] [Table] [Table]

Original: [Table]
Revised: [Table]

Original: CRD 46 .46 .

Original: Amazon 68 .67 .
Revised: Amazon 68 .67 .

Original: Semeval 56 .56 .57 .

Revised: Semeval 56 .56 .57 .

Original: Yelp 76 .76 .76 .75 .
Revised: [Table] [Table]

Original: BiLSTM with Self Attention
Revised: BiLSTM with Self Attention

Original: In
Revised: [Table] [Table]

Original: [Table]
Revised: [Table]

Original: CRD 49 .
Revised: CRD 49 .

Original: Amazon  50 .50 .50 .52 .52 .53 .
Revised: Amazon  50 .50 .50 .52 .52 .53 .

Original: Semeval      51 .52 .53 .53 .53 .
Revised: Semeval      51 .52 .53 .53 .53 .

Original: Yelp 50 .50 .52 .52 .52 .55 .
Revised: Yelp 50 .50 .52 .52 .52 .55 .

Original: Longformer
Revised: Longformer

Original: In-sample test 97 .97 .98 .

Revised: In-sample test 97 .97 .98 .

Original: CRD 93 .94 .94 .91 .

Revised: CRD 93 .94 .94 .91 .

Original: Amazon 81 .
Revised: Amazon 81 .

Original: Semeval 80 .

Revised: Semeval 80 .

Original: Yelp 88 .85 .86 . 89 .89 .89 .86 .86 .86 .84 .
Revised: Yelp 88 .85 .86 . 89 .89 .89 .86 .86 .86 .84 .

Original: [Table]
Revised: [Table]

Original: Longformer
Revised: Longformer

Original: In-sample test 97 .97 .
Revised: In-sample test 97 .97 .

Original: CRD 93 .

Revised: CRD 93 .

Original: Amazon 81 .76 .

Revised: Amazon 81 .76 .

Original: Semeval 80 .
Revised: Semeval 80 .

Original: Yelp 88 .85 .79 .83 .79 .

Revised: Yelp 88 .85 .79 .83 .79 .

Original: Dataset
Revised: Dataset

Original: Percent noise in 
Revised: Percent noise in non

Original: [Table]
Revised: [Table]

Original: rationales
Revised: rationales

Original: SVM
Revised: SVM

Original: In
Revised: In

Original: [Table]
Revised: [Table]

Original:  test 87 .
Revised: sample test 87 .

Original: CRD 46 .

Revised: CRD 46 .

Original: Amazon 68 .66 .66 .

Revised: Amazon 68 .66 .66 .

Original: Semeval 56 .56 .56 .55 .56 .
Revised: Semeval 56 .56 .56 .55 .56 .

Original: Yelp 76 .74 .

Revised: Yelp 76 .74 .

Original: BiLSTM with Self Attention
Revised: BiLSTM with Self Attention

Original: In-sample test 80 .

Revised: In-sample test 80 .

Original: CRD 49 .51 .

Revised: CRD 49 .51 .

Original: Amazon 49 .50 .50 .

Revised: Amazon 49 .50 .50 .

Original: Semeval   50 .
Revised: Semeval   50 .

Original: Yelp 50 .52 .52 .56 .
Revised: Yelp 50 .52 .52 .56 .

Original: Longformer
Revised: Longformer

Original: In-sample test 97 .98 .97 . 90 .83 .

Revised: In-sample test 97 .98 .97 . 90 .83 .

Original: CRD 93 .93 .

Revised: CRD 93 .93 .

Original: Amazon 81 .81 .97 . 90 .83 .67 .62 .79 .64 .52 .
Revised: Amazon 81 .81 .97 . 90 .83 .67 .62 .79 .64 .52 .

Original: Semeval 80 .74 .70 .79 .
Revised: Semeval 80 .74 .70 .79 .

Original: Yelp 88 .83 .83 .89 .90 .89 .87 .
Revised: Yelp 88 .83 .83 .89 .90 .89 .87 .

Original: [Table]
Revised: [Table]

Original: [Table]

Original: [Table]

Original: [Table]

Original: [Table]

Original: [Table]

Original: [Table]

Original: Longformer
Revised: Longformer

Original: [Table]
Revised: [Table]

Original: [Table] [Table][Table]
Revised: [Table] [Table]

Original: [Table] [Table][Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: CRD

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table][Table]
Revised: [Table] [Table]

Original: [Table] [Table][Table]
Revised: [Table] [Table]

Original: [Table] [Table][Table]
Revised: [Table] [Table]

Original: [Table]
Revised: [Table]

Original: Semeval
Revised: Semeval

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table] 
Revised: [Table] [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [-CRD 46 .49 .51 .
-]
Revised: [-CRD 46 .49 .51 .
-][Table] [Table] [Table] [Table]

Original: [-Amazon 68 .67 .67 .
-]
Revised: [-Amazon 68 .67 .67 .
-][Table]

Original: [-Semeval 56 .56 .56 .56 .57 .
-]
Revised: [-Semeval 56 .56 .56 .56 .57 .
-][Table] [Table] [Table] [Table]

Original: [-Yelp 76 .75 .75 .75 .
-]
Revised: [-Yelp 76 .75 .75 .75 .
-][Table] [Table] [Table]

Original:  BiLSTMwith Self Attention
Revised: [Table] [Table] BiLSTM

Original: [-In-sample test 80 .
-]
Revised: [-In-sample test 80 .
-][Table] [Table]

Original: CRD 49 [-.54 .51 .
-] Amazon  53 .50 [-.
-]
Revised: CRD 49 [-.54 .51 .
-]. Amazon  49 .50 [-.
-].50 .50 .50 .

Original: Semeval     [-53 .50 .
-] Yelp 50  .

Revised: Semeval     [-53 .50 .
-]50 . Yelp 50 .50 .

Original: Longformer
Revised: Longformer

Original: In-sample test 97 [-.
-]
Revised: In-sample test 97 [-.
-].97 . 97 .97 .98 .

Original: CRD 93  .

Revised: CRD 93 .94 .

Original: Amazon 81 .80  .

Revised: Amazon 81 .80 .75 .

Original: Semeval 80 .78 .78 .78 .79 .

Revised: Semeval 80 .78 .72 .

Original: Yelp 88.88 .87 .88 .88 .89 .87 .
 
Revised: Yelp 88 .
 [Figure] [Figure] [Figure]

Original: [Figure]
Revised: [Figure]

Original: [Figure] 
Revised: [Figure] [Figure] [Figure] [Figure]

Original: [Figure]
Revised: [Table] [Table] [Table] [Table]

Revised: In

Original: [Figure]
Revised: [Table] [Table]

Revised: RH

Original: [Figure]
Revised: [Table]

Revised: MNLI

Original: [Figure]
Revised: [Table]

Revised: MNLI-MM

Original: [Table]
Revised: [Table]