Making a (Counterfactual) Difference One Rationale at a Time
Year: 2022

Abstract:
Rationales, snippets of extracted text that explain an inference, have emerged as a popular framework for interpretable natural language processing (NLP). Rationale models typically consist of two cooperating modules: a selector and a classifier with the goal of maximizing the mutual information (MMI) between the "selected" text and the document label. Despite their promises, MMI-based methods often pick up on spurious text patterns and result in models with nonsensical behaviors. In this work, we investigate whether counterfactual data augmentation (CDA), without human assistance, can improve the performance of the selector by lowering the mutual information between spurious signals and the document label. Our counterfactuals are produced in an unsupervised fashion using class-dependent generative models. From an information theoretic lens, we derive properties of the unaugmented dataset for which our CDA approach would succeed. The effectiveness of CDA is empirically evaluated by comparing against several baselines including an improved MMI-based rationale schema [19] on two multi-aspect datasets. Our results show that CDA produces rationales that better capture the signal of interest.

heading: 1 Introduction
text: Research in neural model interpretability has been cast as important and received significant recent attention [21]. Within the field of natural language processing (NLP), rationales have been a popular method for providing interpretability in the form of extracted subsets of text [10]. Rationale models typically consist of two cooperating modules where one module, the "rationale selector", selects the rationale from a source document, and the other module, the "classifier", acts on only the selected rationale without seeing the rest of the document. There is interpretability through sparsity and exclusivity.
A common approach for training these rationale models is based on the maximum mutual information criteria (MMI) [7]. With the MMI criteria, rationale selectors seek the subset of text that carries the most information about the target label. Often, sparsity and coherency constraints are used to keep the rationales interpretable. Within many datasets, however, spurious patterns and co-varying aspects can cause the rationale selector to pick up on patterns that do not capture a desired relationship between input text and target labels. As a result, the rationalized model can have undesirable behaviour like predicting a hotel is very clean because it is in a convenient location. Nonsensical rationales or
35th Conference on Neural Information Processing Systems (NeurIPS 2021).
explanations might decrease trust in the model, and in some cases, suggest the model might generalize poorly [26].
In this work, we propose a general counterfactual data augmentation (CDA) [22] approach to aid rationale models trained with MMI. We show that theoretically our CDA approach can effectively improve the performance of rationale selectors by lowering the mutual information between spurious signals and aspects of interest. Empirically, we show that models trained on our CDA datasets learn higher quality rationales than those trained on the original dataset when both use the same MMI criteria. More importantly, the most significant advantage of our CDA approach is that it does not require human intervention. We use rationales from an initial, noisy model and replace them with new text that changes the target label using a generative neural model. In this way, our CDA approach is completely hands off and does not need input from human experts or crowd workers.
We first show that in the extremely ideal scenario where the initial rationale selector is perfect, our CDA approach can eliminate the mutual information between spurious signals and the target label. Next, we show in the common, realistic scenario where the rationale selector is noisy and imperfect, our CDA approach can still yield gains. Finally, the effectiveness of our CDA approach is compared against several baselines including an improved MMI-based rationale schema [19] on two common multi-aspect review datasets, TripAdvisor [29] and RateBeer [24]. Multi-aspect datasets are our main focus as they are guaranteed to contain spurious patterns and co-varying aspects; we primarily used MMI-based baselines because the fundamental goal our CDA approach is to reduce the mutual information between spurious signals and aspects of interest.

heading: 2 Counterfactual Data Augmentation and Multi-Aspect Datasets

heading: 2.1 Definitions and Notations
text: We use upper-case letters to denote random variables, X and Y , and lower-case letters to denote samples from these variables, x and y. I(X,Y ) marks the mutual information between X and Y . Mutual information is defined as the reduction in uncertainty of a random variable due to knowledge in another random variable, I(X,Y ) = H(Y ) − H(Y |X), where H(X) and H(Y |X) are the Shannon entropy and conditional entropy respectively [8].

heading: 2.2 Problem Formulation
text: This work follows the same rationale concept introduced in [19]. Specifically, one neural module extracts text from a document and another neural module classifies the extracted text. Later it has been shown that the rationalization criteria aims "to maximize the mutual information between selected features and the response variable" [7] defined as:
max G
I(XM ;Y ) subject to M ∼ G(X) (1)
where M denotes a binary rationale mask over the input produced by a rationale selector G. That is, the goal of the rationale selector is to select the subset of features in X that are most informative of the label Y under some constraints defined by the selector G. In this work, our selector constraints affect the size and coherency of the rationales.
Here we consider a multi-aspect dataset, D with features X and labels Y: X is a set of features, or a sequence of words in NLP, and Y is a vector, possibly one dimensional, of numerical scores. In a multi-aspect dataset, a single document can discuss multiple attributes of a single object. For example, a single beverage review might discuss its appearance, taste, and smell. We assume some subset of the features, X1, belong to the target aspect label, Y1, while other features, X2, are spurious or non-causal. These features could belong to other aspects or be artifacts of the dataset. In the following, for simplicity, we will use <X , Y1> or <X1, X2, Y1> interchangeably based on the context.
Our goal is to estimate or model the score for aspect Y1 by using the function f given only X1, as follows: Y1 = f(X1). There is one such model corresponding to each < X1, Y1 > pair. In this work, we use multi-aspect datasets, and model each aspect individually. This simulates the more common case where a dataset has a single output label of interest and all signals in the dataset that do not pertain to that label are considered spurious or belonging to other, not-estimated aspects.
When predicting Y1, our ideal model would focus onX1 and ignore all other features,X2. Specifically, for a given sample <x, y>, our selector would select a subset of x such that x1 = xM . A rationale selector might fail to extract x1 effectively for two primary reasons: first, Chen et al. [7] showed it is intractable to find a solution to Eqn 1 and thus they derive a variational approximation with Monte Carlo based gradient estimates. Second, datasets can contain artifacts such that spurious patterns might contain significant mutual information with the target label. Along the same lines but even more concerning, other aspects in a dataset might be highly correlated with the target label. For example, beers that smell good usually taste good as well.
Since the goal of the selector is to select features that maximize I(XM , Y1), we can assist the selector in finding X1 by lowering the mutual information between the other spurious features and the desired label, I(X2, Y1). Drawing on ideas from [22], we do this through a general counterfactual data augmentation (CDA) scheme where, in the counterfactual dataset, superscript c, we flip the label of the document from Y1 to Y c1 and replace the text selected by the rationale selector, X1, with an inference, Xc1 , generated by a class conditioned masked language model (MLM) using Y c 1 and X2 described as: Y c1 ← 1− Y1; Xc1 ← arg max
X1
p(X1|1− Y1, X2) (2)
In our generated counterfactual dataset < Xc1 , X2, Y c 1 >, X c 1 is the newly generated counterfactual, X2 is the original spurious feature set, and we assign Y c = 1− Y1. We will show, in the augmented dataset which is the concatenation of datasets < X1, X2, Y1 > and < Xc1 , X2, 1 − Y1 >, we are lowering the mutual information I(X2, Y1). We will use superscript a for the augmented dataset: Da = <Xa1 ,X a 2 , Y a 1 >.
2.3 Lowering I(X2, Y1), an idyllic case
Take a dataset of beer reviews where each document contains a description of the taste and smell of a beer as well as a numerical score for only the smell aspect. The task is to estimate the smell score while using the smell text as the rationale. Figure 1 demonstrates our process. An example of a concise document in this dataset might be This beer smells great. It tastes terrific. In this document x1 is the phrase smells great and x2 is tastes terrific. Both have positive sentiment but our only label for this document is y1 = 1 for the smell sentiment. Our counterfactual document could be This beer smells awful. It tastes ter-
rific and our label becomes y1 = 0. We can see that in the augmented dataset the phrase tastes terrific maps to both positive and negative labels and therefore p(Y1|X2) = p(Y1). Finally, I(X2, Y1) is 0 and I(X1, Y1) is unchanged.
With perfect knowledge of the ground truth rationales and the process Xc1 ← p(X1|1− Y1, X2), we can craft counterfactual documents and therefore a counterfactual dataset that perfectly eliminates I(X2, Y1) while preserving I(X1, Y1). However, the challenge is that ground truth rationales are not provided in the training data. Therefore, it is important for us to show that even when rationales selected by the initial rationale selector are noisy and imperfect, we can still lower I(X2, Y1) in the augmented dataset and benefit subsequent models trained with MMI.

heading: 2.4 Dealing with a Noisy Initial Selector
text: Here we are working on the augmented dataset: Da = <Xa1 , X a 2 , Y a>. To completely eliminate I(Xa2 , Y a 1 ), our CDA approach requires a perfect rationale selector. We were originally motivated by improving a poor rationale selector, so here we track what happens to both I(Xa2 , Y a 1 ) and I(X a 1 , Y a 1 ) when the rationale selector is not perfect. Our new goal is to reduce I(Xa2 , Y a 1 ) more than we reduce
I(Xa1 , Y a 1 ). We analyze the procedure in the worst case scenario in order to determine a lower bound on our algorithm’s benefits under some assumptions.
In an extremely erroneous case, we say that for a given <x1, x2, y1>, our initial rationale selector mistakenly selects x2 when aiming for x1. When creating the corresponding counterfactual document, we still have yc1 = 1 − y1, but we modify the document according to xc2 ← arg maxX2 p(x2|1− y1, x1) instead of the process defined by Eqn 2. Going back to our concise example, this would be the counterfactual document This beer tastes good. It also smells bad. In this extremely erroneous case, we have decreased I(X1, Y1) while I(X2, Y1) remains unchanged. Thus, we define the worst case scenario as reducing I(X1, Y1) at some error rate α and keeping I(X2, Y1) constant at the same rate.
If we say that this error happens to all samples with a rate α, we can analyze conditions that must be present in the original dataset so that our CDA approach is beneficial. Let’s first define ∆Ia as the change in mutual information from the original to the augmented dataset.
∆IaXi,Yj = I(Xi, Yj)− I(X a i , Y a j ) (3)
In order for our CDA approach to be beneficial, we need to decrease I(X2, Y1) more than I(X1, Y1). That is:
∆IaX2,Y1 −∆I a X1,Y1 > 0 (4)
For samples in the original dataset where the initial selector results in an error, X1 would map to 1− Y1 for the corresponding counterfactuals. Furthermore, the augmented dataset would map X1 to both Y1 and 1− Y1 for these original erroneous samples and corresponding counterfactuals, and thus, X1 is no longer informative of Y1 on such samples. That is, in the augmented dataset and with proportion α, p(Y a1 |Xa1 ) = p(Y a1 ). For the remaining 1 − α portion of non-error samples, CDA can successfully capture p(Y1|X1) in the original dataset and p(Y c1 |Xc1) in the counterfactual dataset. To quantify the conditional distributions of the augmented dataset, we make two assumptions: first, we assume that we can successfully generate the counterfactuals, and thus, we can assume p(Y c1 |Xc1) = p(Y1|X1); second, we assume that the erroneous samples happen randomly across the original dataset, and the marginal distributions of the original and augmented datasets are the same. Based on these two assumptions, we have:
p(Y a1 |Xa1 ) = αp(Y1) + (1− α)p(Y1|X1) (5) For X2, the success and failure cases are reversed.
p(Y a1 |Xa2 ) = (1− α)p(Y1) + αp(Y1|X2) (6) We can now expand Eqn 4 using the definition I(X,Y ) = H(Y )−H(Y |X).
0 < −H(Y1|X2) +H(Y a1 |Xa2 ) +H(Y1|X1)−H(Y a1 |Xa1 ) (7) Using the definition, H(Y |X) = −E log p(Y |X), we can expand this further to
0 < −E log p(Y1|X1) + E log p(Y a1 |Xa1 ) + E log p(Y1|X2)− E log p(Y a1 |Xa2 ) (8)
Eqn 8 describes the conditions that must be met in our original dataset in order to yield gains from our CDA procedure for some error rate α. It is impossible to calculate this relation directly because it will require exact knowledge of our ground-truth rationales. In order to shed some light on this, Figure 2 shows the efficacy of the CDA approach when we approximate X1 and X2 with binary variables, p(Y1|X1) = 34 , p(X1) = p(X2) = p(Y1) = 1 2 . When X1 and X2 are equally informative of Y1, the benefits of CDA decrease linearly with the error rate, and intuitively, our error rate must be less than 50% to see gains. When X2 is more informative than X1, we have a higher error budget to see any benefit, and when X1 is more informative than X2, our error budget is smaller. According to this analysis, we have a higher error budget when spurious signals offer the same or more information about the target label. When the spurious signals carry much less information, the initial selector must have a low error rate in order to benefit from the CDA approach.

heading: 3 Architecture and Implementation

heading: 3.1 Rationale Framework
text: The original rationale framework, as viewed in this work, was introduced by [19]. It was not our goal to change the core rationalization algorithm, so we re-implemented the algorithm and updated some details. At a high level, our rationale framework is the same in that we use one network to select the rationale and another network to classify the text. The rationale framework can be visualized by the blue portion of Figure 4.
The original implementation [19] used RNNs for both networks, REINFORCE [30] for dealing with the discontinuity introduced by the binary rationale mask, and variable percentage rationales. In this work, we use transformers for both networks because of their effectiveness over RNNs in NLP [28], the simpler straight-through method [3] [5] instead of REINFORCE, and we use fixed percentage rationales because it eliminates sparsity related hyperparameters. Our fixed percentage rationales differ from Chen et al. [7] in that we use the top-K tokens during training and inference whereas Chen et al. [7] use an iterative re-sampling approach with Gumbel-softmax reparameterization [15].
The classifier is trained only to make quality predictions against the labels while using the rationale. This is the cross-entropy between the labels and the classifier’s prediction, Ly. We follow [19] by using a coherency regularizer for the rationale selector: Lr = λrT ∑ 1...T |mt −mt−1| where T is the total number of tokens in a document, m is the rationale mask, and λr is the hyper-parameter used to tune coherency. This encourages the rationales to be contiguous. For the datasets evaluated in this work, coherency is a useful inductive bias. The loss for the rationale selector Ls is the coherency regularizer and the cross-entropy between the labels and the classifier’s prediction. This is Ls = Lr + Ly .

heading: 3.2 Counterfactual Predictor
text: rationale framework in the counterfactual generation process because our goal is to lower the mutual
information between the spurious signals and the target label. An off-the-shelf style transfer method might focus on signals other than that selected by our initial rationale selector.
Figure 4 shows the CF Predictor’s training flow. At its core, the CF Predictor leverages classdependent Masked-language models (MLM) [9]. We replace the original document’s rationale with an inference from a MLM and leave the rest of the document unchanged. The MLMs are trained to produce documents with the desired class through reinforcement learning (RL) [12], and they are trained to produce realistic documents through adversarial learning [11]. We use straight-through [3] to propagate gradients through token selection during training. For a dataset with binary labels, we train two separate MLMs: one for generating class-0 documents and another for generating class-1 documents [31]. The loss for the counterfactual predictor, LCFP is defined as:
LCFP = λRLLRL − λALA (9) where LRL is the classifier loss after passing the counterfactual through the rationale selector and the classifier. It is the cross-entropy between the desired label, all ones or zeros, and the predicted label. LA is the loss component from adversarial training. Following [11] and [32], our discriminator, D, seeks to distinguish generated documents from the originals, so the discriminator’s loss, LD, is 1λALA where LA is the cross-entropy between real-fake labels and the prediction given by the discriminator. λA and λRL balance the adversarial and classification losses. We include 1λA when training the discriminator to hamstring it relative to the predictor. We found that this generally helped us avoid mode collapse commonly seen when adversarially training generative models. For LA, we mask the contribution of original documents without the desired label. When training the class-1 counterfactual predictor, we show the discriminator real documents X with Y1 = 1 and counterfactual documents, Xc, where the original documents’ labels were Y1 = 0.
During training, the counterfactual is produced in one step. All words not included in the selected rationale from the original document, XM , are kept in the counterfactual document. The kept tokens are X\XM . All words in the selected rationale are replaced by the CF Predictor using one prediction from the MLMs in a greedy fashion. After training, when producing the counterfactual dataset, the counterfactual documents still keep all non-rationale tokens. We now replace the rationale tokens from left to right using the output of the CF Predictor MLMs. A counterfactual token at position t is decoded according to the following process.
xc1,t = arg max x1,t p(x1,t|x2, 1− y1, xc1,0...t) (10)
We found this to be a good trade-off between greedy decoding and a more expensive beam search. Greedy decoding could generate frequent, repeated tokens while beam search could be an unnecessary expense for generating documents that reflect the target distribution, but do not necessarily need to pass the bar of human readers.
Figure 5 illustrates an original document and its counterfactual generated by our CF predictor. Notice here the initial selector was successful in identifying the smell aspect. The inclusion of the original and its counterfactual document in the augmented dataset successfully decreases the mutual information between the non-smell text and the smell label.

heading: 4 Experiments

heading: 4.1 Datasets
text: We conduct experiments using datasets from two sources. This first source contains reviews compiled by Wang et al. [29] from TripAdvisor.com. We use the training, dev, and test sets curated by Bao et al. [2] and used for rationalization by Chang et al. [5]. The label is binary, and we focus on the location aspect. There are 198 test samples with human-annotated rationales.
The second source consists of reviews collected by McAuley et al. [24] from RateBeer. Each review is a paragraph of text with five numerical scores in the range of 1 to 5 for the appearance, smell, palate, taste, and overall aspects of the beer. We focus on the appearance, smell, and palate aspects. We created training and dev datasets for each aspect that follow the source distribution. Following [5], we binarize this data so that all reviews with a score ≥ 3 are class 1 and all reviews with a score ≤ 2 are class 0. We then balance the dataset between classes. Additionally, we use procedurally decorrelated datasets created by Lei et al. [19], and binarize these datasets as well. We now have two
datasets for each RateBeer aspect: a "correlated" set and a "decorrelated" set. Appendix Section A.2 shows correlation matrices before and after decorrelating the data as well as additional dataset details. In the correlated datasets, the correlations and presumably the mutual information between aspects are much higher than in the decorrelated datasets. This makes the rationalization task more difficult. Additionally, McAuley et al. [24] provides about 1,000 holdout samples where the aspect specific text is annotated by human experts.

heading: 4.2 Baselines
text: Three MMI-based baselines are used. First, "MMI" is the original Rationalization scheme [19] that has been updated and re-implemented to use the most recent NLP models as described in section 3.1. This model is trained on the original, unaugmented dataset.
Second, Factual Data Augmentation (FDA) is used to verify that our gains are not only due to data augmentation but due to the CDA procedure. FDA augments the original dataset with new samples generated by the CF predictor models, but instead of flipping the label and passing it to the 1− y1 component, we pass the sample to the component of the CF predictor with the same label as the original document during inference. The new samples are produced using the following process:
yc1 ← y1; xc1 ← arg max x1 p(X1|y1, x2) (11)
The third baseline, simple substitution using antonyms (ANT), does not use neural models to augment counterfactual data. The counterfactual is generated by replacing words in the rationale with antonyms from WordNet [25] [4]. Note that we only accept antonyms that are in the vocabulary used by the models and antonyms that have the same part of speech as labeled by NLTK [4].

heading: 4.3 Experiment Settings and Assumptions
text: We train the rationale selector and the classifier together, early stop based on the selector cost, freeze the selector, and finally fine-tune the classifier on the original dataset. For all of the data sets and models, we use the dev set for early stopping (more details in Appendix Section A.3). Our MLM transformers [28] were pretrained on unlabeled data from the TripAdvisor and RateBeer datasets separately. For the TripAdvisor dataset, we pretrain on all data that does not appear in the location aspect’s train, dev, or annotated dataset from [5]. For the RateBeer datasets, we pretrain on all data that does not appear in any train, dev, or annotated dataset from [19] and the correlated datasets. We used a masking rate of 10% and masked tokens were treated as described in [9]. For the rationale models, the transformers are initialized from models pre-trained with random masking. We found
Human annotations are underlined. CDA rationales are in blue. MMI rationales are in red. Overlaps between CDA and MMI are in magenta.
that contiguous masking was better for pre-training the CF predictor. This contiguous masking is similar to that from Joshi et al. [16], but masked tokens are treated the same as in Devlin et al. [9] and there is only one contiguous span. The rationale selector and classifier are initialized from the random-masking transformers. For the rationale selectors, following [5], we set the rationale percentage to 10% for all datasets. The CF predictor components and the GAN discriminator are initialized from the contiguous-masking transformers. These models all have a vocabulary with 215 tokens, 8 layers, 8 attention heads, and a hidden dimension of 256. Appendix Section A.4 shows our server configurations and more details on our experiment setup.
Models are selected and reported based on the best performance on the dev set across a grid search. All methods are evaluated using the same grid search when training the rationale model. The model is selected before fine-tuning the classifier with a frozen rationale selector. The initial selector used to train the counterfactual predictors was the selected MMI model with a fine-tuned classifier. The parameters and checkpoints for the CF Predictor models are tuned and chosen to maximize the accuracy of the training documents’ predicted label as compared to the target label (measured by the original rationale model) and to maximize the entropy in the inserted counterfactual tokens. The CF Predictor model is chosen from a grid search, using only the training dataset, across λA and λRL.
For the TripAdvisor dataset, we repeat the experiment three times: training an initial rationale selector, training a counterfactual predictor, generating a counterfactual dataset, and training a new rationale model. Additionally, we train two additional rationale models with different random seeds and the selected hyperparameters. Consequently, there are nine rationale models for each of the four methods, and we report the mean and standard deviation across those nine models. In the RateBeer datasets, we repeated the same experiments two times and then trained two additional models with different random seeds and the selected hyperparameters for each rationale model. As a result, we have a total of six runs for each method for the RateBeer datasets. All models are in Tensorflow [1]. Our software is publicly released 1.

heading: 4.4 Results
text: We evaluate the rationale models by the precision (Rat. Prec.) as compared to human annotations. This token-level metric is taken as the mean across samples in the annotated set. We also report the accuracy of the classifier on the development set (Dev. Acc.).
As shown in Tables 1, 2, and 3, our CDA approach outperforms all baselines on 67 experiments. As expected, CDA’s performance over other methods is most pronounced on the correlated RateBeer datasets compared to on the decorrelated ones. Only on the decorrelated appearance dataset, CDA performs second to MMI. This may be because on the decorrelated appearance dataset the spurious aspects have very low mutual information with the target label, appearance, and therefore the error margin for the initial selector is very tight and CDA is expected to yield low gains as discussed in Section 2.4.
1github.com/mlplyler/CFs_for_Rationales
The baseline augmentation schemes (FDA, ANT) produced mixed results. The FDA scheme should not have changed the mutual information between the spurious aspects and the target label. Whenever it performed worse than MMI alone, it most likely introduced noise into the dataset. While the ANT scheme might be effective in lowering the mutual information between the spurious aspects and the target label, the generated counterfactual does not likely model p(x1|1− y1, x2), so we expect it to also reduce I(X1, Y1).
Empirically, we have demonstrated that models trained on the CDA augmented data tend to outperform models trained on the original datasets. This lends credence to the idea that our scheme is indeed lowering the mutual information between the spurious aspects and the target label. The dataset and method pairs with very high variance are dragged down by degenerated runs where the rationale selector has little or no skill. These runs were seen when varying the random seed with the selected hyperparameters. Removing the degenerate runs can bring the mean
rationale precision of the high variance experiments closer to CDA, but we believe these degenerate models seen primarily in the baselines align well with our theory that CDA allows the rationale selector to more easily identify X1.
Case Study: Figure 6 presents a case study comparing the rationales selected by MMI and our proposed CDA approach in the TripAdvisor and Correlated Beer datasets respectively. As shown in Figure 6, our CDA models can select the text that aligns better with human annotations and they successfully avoid selecting sentiment-carrying text that is not relevant to the aspect of interest.

heading: 5 Related Work
text: The rationale framework used in this work was introduced in [19] and connected to the MMI criteria by Chen et al. [7]. Much of the follow-up work introduced modifications to the learning algorithm to overcome spurious signals picked up by MMI. Yu et al. [33] sought to limit the signal left in the complement of the rationales. The concept of class-wise rationalization was introduced in Chang et al. [5], and Chang et al. [6] introduced a rationale algorithm that seeks invariant rationales across environments. These works modify the rationale framework and the MMI criteria to help the rationale selector find desirable signals. In our work, we keep the MMI criteria unchanged, but instead seek to diminish the undesirable signals through counterfactual data augmentation.
Data augmentation generally and counterfactual data augmentation (CDA) specifically has been a popular technique in recent natural language processing (NLP) work. Lu et al. [22] introduced counterfactual data augmentation as a general methodology and showed their rule-based scheme
could mitigate gender biases commonly seen in word embeddings and NLP models. Other works used CDA to train better named entity recognition (NER) models in the medical text domain [34]. More work [17] continued the ideas of CDA by working with sentiment analysis and natural language inference tasks. They constructed their CDA datasets using crowd workers and showed models trained on the augmented datasets generalized better out-of-domain. Follow-up work [18] showed how their crowd worker interventions reduce spurious signals when viewed through a simplified causal modeling lens. The causal lens and generative counterfactual data augmentation have also shown recent, positive results in the computer vision domain [27].

heading: 6 Conclusions
text: This work presents a counterfactual data augmentation method for lowering the mutual information between spurious signals and a target label in a dataset. We derive theoretical conditions that indicate when our approach will be beneficial to the dataset. Empirically, we show rationale models trained with MMI on our counterfactually augmented datasets result in rationale selectors with improved behaviours. Furthermore, our analysis shows that the benefits of our scheme are proportional to the error rate of the original rationale selector. This suggests we could perform counterfactual data augmentation iteratively where we achieve better rationale selectors after each iteration that could be used as the initial selector in the next round of data augmentation.
Our method does not make any changes to the core rationale algorithm. There has been more work in the literature on rationale algorithms since Lei et al. [19] and Chen et al. [7], but they were not included in this study because they do not necessarily follow the maximum mutual information criteria on which our theory is based. From a practical standpoint, it is likely our CDA method could connect with other rationalization strategies because the CF Predictor is trained using a frozen rationale model. This could be an important area for future study.

heading: 7 Broader Impact
text: This work focuses on methods for training interpretable NLP models. While interpretability is typically a desired trait, defining exactly what is interpretable is not always clear [21]. Rationale models have been shown to not always be faithful [14] [13], and in the worst case, they can degenerate in such a way where the rationale selector makes the decision instead of the classifier [33]. The degenerated case is likely to produce rationales that are nonsensical to human interpretation.
Section 2.4 presents some theoretical proof on how CDA affects the mutual information between text and the target label with different initial selector error rates. Some conclusions drawn in Section 2.4 rely on a simplified model with binary variables. Furthermore, the simplified model makes the assumption that the generated text follows the same distribution as the original dataset. While this is the goal of the adversarial learning strategy that we implemented, we found that the generated text had lower entropy than the rationales they replaced. This is likely due to both the arg max in Eqn 2 and LRL.
We have demonstrated positive results for improving rationales for sentiment analysis sentiment classifiers. Generalizing the approach to other NLP tasks is less straightforward. First, the naive approach to generalizing to K classes in the classification setting suggests having K CF Predictor models and generating K − 1 counterfactual documents per data sample. This would be a significant increase in computation and resources. Second, for the task of natural language inference, CDA has been successful with human annotators [17]. It might be possible to train a CF Predictor for each type of hypothesis. It is less clear how to train a CF Predictor to modify a premise.
Our method uses data augmentation to lower the mutual information between some signals in documents and a label of interest. When the initial rationale selector aligns with human judgment, models trained on the augmented data tend to better align with human judgement. The method can be thought of as amplifying signals relative to others. We are changing the properties of the dataset. For sensitive tasks, one should check for undesired biases and fairness concerns before and after CDA.
Acknowledgments: We sincerely thank Jascha Swisher and Stephen Shauger for their insights and help. We thank the Laboratory for Analytic Sciences for supporting the work, and we also thank the reviewers for their time and helpful advice.

Review summaries:
In this paper, the authors discuss selecting rationales to improve classifier performance in an extract then classify setting. The motivation behind this paper is that prior work on rationale selection often seeks to identify a subset of tokens in a document that carry the most information about the target label. However, oftentimes such rationale selectors pick up on spurious correlations that are merely correlated with the label due to confounding and do not express the desired relationship with the label. So even though the desired predictor would rely on “the causal variable”, in this case, predictors often end up relying on the “non causal” factors.   To address this issue, the authors suggest assisting the rationale selector by lowering the mutual information between non causal features and the label (since a rationale selector will try to identify features that have the maximum mutual information with the label). To do so, the authors propose a framework in which they first use a rationale extractor (similar to that introduced by Lei et al.) to identify rationales for the label of interest. A class conditional masked language model then makes inference on these tokens (while keeping the other tokens constant) to produce new documents with a counterfactual class label. These replacements are performed by the MLM in one step (in a greedy fashion). A discriminator seeks to distinguish between the original document and the generated counterfactual document. This process is run iteratively until convergence. The core idea here is that when the original dataset will be augmented with the counterfactual documents, it should lead to a lower mutual information between the “non rationales” that remained unchanged during counterfactual generation and the label. As the authors recognize, one drawback of automatically generating counterfactual examples is that we assume access to a perfect rationale selector. However, since the motivation of the paper relies on a poor rationale selector which may also reduce the mutual information between causal features and the label, they conjecture that reducing the mutual information between spurious features and the label more than reducing mutual information between causal features and the label could allow us to improve the model performance. The authors conduct experiments on Beer reviews from RateBeer and Tripadvisor reviews. Compared to three baselines, the proposed method appears to generally perform better on the downstream classification metric, and these gains are attributed to better rationale extraction. I think overall the paper represents a valuable contribution but I have some concerns as I lay out below. I will be happy to update my score post rebuttal.

A rationale in NLP is a subset of an original input text on which a classifier operates. Extracting such a rationale is a popular approach for more interpretability because by definition we know the classifier only had access to this reduced text. However, rationales can be nonsensical and can consist of spurious patterns in the dataset.  To improve the coherence of rationales and to improve generalization, this paper proposes a technique to automatically create counterfactual data. This counterfactual data is created specifically to reduce mutual information between the true target label and irrelevant parts of the original text with regards to the true target label. For example, in “This beer smells great. It tastes terrific.” both sentences have positive sentiment but only the first is relevant when predicting whether the beer has a good smell. To help a model to automatically learn to distinguish, a helpful counterfactual example would be “This beer smells awful. It tastes terrific”.  Contributions  -	A method to automatically generate counterfactual examples to improve rationale extraction. -	An analysis of how good an initial rationale extractor should be to be able to expect performance improvements using the proposed method of generating counterfactual examples. 

This paper addresses an important problem of correcting the spurious rationales that often get learned due to maximizing the mutual information. Instead, it proposes “counterfactual data augmentation” that lowers the mutual information between spurious signals and the document label. The counterfactual factual data augmentation is an unsupervised method. The paper finally shows strong results on two datasets. 

This paper aims to provide a novel rationalization scheme for text classification models in NLP. The authors use the standard setup of a selector and classifier model wherein the selectors selects specific words from the input text and classifier makes a prediction on the selected rationale. Standard issues in such models is that the selector may end up picking on spurious correlations (or "artifacts") when picking the rationale if they are correlated with the predicted quantity, instead of true predictive features. The core contribution of the authors is to augment the dataset in such a manner that the correlation between the artifacts and the label is broken.  The correlation is broken by generating counterfactual examples -- examples where predictive features are *flipped* along with the label but the spurious features are kept the same. Since we don't know a priori which features are predictive vs spurious, the authors hope that the dataset contains slightly less information about the label than the predictive features and provide an analysis in a simple binary feature case, wherein the show when the model may increase the mutual information for predictive features vs rationale error rate of initial selector.   The modeling part use transformer based models all the way. The setup learns the selector and prediction, in addition to a counterfactual generator (which replaces the rationale words with words predictive of the opposite class), and a discriminator to make sure the words are indeed from same distribution as input text.  Experiments are done on two sentiment analysis multi-aspect datasets and the generated rationales are shown to be more accurate than baseline MMI training. Some qualitative analysis seem to indicate better rationales are produced in terms of coherency and correctness.