Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: 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.

main_review: 1. An important problem to solve and the proposed solution is interesting.
2. The trade-off between lowering the MMI and preserving the fidelity of the rationales is nicely dealt with.
3. Did you use Eraser (April 2020) for evaluating rationales? The precision and F1 metrics look the same but it will be important to confirm that. Maybe you could use that benchmark to show results for two datasets.
4. Check typos such as the incorrect orientation of inverted commas (Line 279, 285, etc.)
limitations_and_societal_impact: NA
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: 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.
main_review: - I think overall this paper is well motivated and presents an interesting framework that could be extended to various problems (since it does not require human annotations).
- I particularly like the choice of datasets here, along with the extensive analysis as it relates to the error rate associated with the rationale extractor and how it may impact the mutual information between a causal feature and the label and the non causal features and the label.
- I think the results are quite convincing on each dataset (more on Tripadvisor than Beer Review), however, I would have liked to see experiments with more than one rationale extraction framework, especially since multiple methods have been proposed since Lei et al. Additionally, since the counterfactuals are generated based on sampling, I would have liked to see multiple runs of the experiments and means and standard deviations reported in the tables rather than just a single number. This would especially help with the Beer Review results since in many cases certain baselines achieve comparable performance to the proposed method. There are some other outstanding questions as well that the authors haven’t answered. Lastly, the authors say they set the rationale percentage to 10% for all datasets, but don’t offer a justification for this number. What happens if it is 20% or 30%? I imagine the percentage tokens that are relevant for a particular dataset would be dependent on the dataset to some extent (see [1]).
- The paper’s presentation needs to be improved, especially as it relates to Section 2. The authors also appear to use causal terms such as “spurious” to mean something different than what they are. For instance, in Lines 68 and 69, they say “We assume some subset of the features, X_1, belong to the target aspect label, Y_1, while other features, X_2, are spurious.” This sentence implies one of two things: (i) either this is a very strong assumption where all features are confounded; or (ii) the authors meant to say non-causal instead of spurious. Currently I am assuming that it is the latter.
- The paper is lacking a comprehensive qualitative analysis. I think the paper would be considerably better if the authors perform one on generated rationales from various methods (including an error analysis on the same).

[1] Jain, Sarthak, Sarah Wiegreffe, Yuval Pinter, and Byron C. Wallace. "Learning to Faithfully Rationalize by Construction." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4459-4473. 2020.

Additional comments:
- In the last paragraph of Page 2 you talk about two reasons why a rationale selector may not work well. There is in fact a third reason too, where the rationale selector (in end to end models) may in fact end up performing the classification task and the classification layers end up as dummy layers. For instance, if a model just extracts a period as the rationale for positive sentiment and any non-period token as rationale for negative sentiment.
- For multiple papers, Arxiv versions have been cited even though the papers have been published at peer reviewed conferences/journals. Please update these references.

Other presentation comments:
- Lines 21 and 22 (‘rationale selector’ and ‘classifier’): In Latex, the opening quotes are ` instead of ‘. Same for Lines 276, 279, and possibly others that I might have missed.
- Use \citet when using an intext citation. For instance, in lines 259 and 261, the paper says “reviews collected by [25]” and “rationalization by [5]” respectively. Instead it should read “reviews collected by Wang et al. [25]” and “rationalization by Chang et al. [5]”
- Line 279: “establish our gains” -> “establish that our gains”


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I thank the authors for taking the time to respond to my review in such detail. I have updated my score to recommend an acceptance but at the same time I would urge the authors to include the additional experiments I have recommended in my review. I think those will make this a very thorough and influential empirical study. 
limitations_and_societal_impact: I believe that the broader impact section could use some more work, especially as it relates to the limitations of current work.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: 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.

main_review: While both rationalization and counterfactual augmentation have been performed before, this paper provides first known implementation to correcting for spurious correlation via counterfactuals. The authors provide relatively well researched section for related work although primarily focuses on MMI based rationalization (https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00367/98620/Aligning-Faithful-Interpretations-with-their) . In addition, comparisons to other baselines like Invariant Rationalization has not been made (It is unclear why that is). 

The basic idea behind the paper is reasonably clear from the first reading, although some details can be improved. For example, in line 146, it is unclear what $X^{er}_1$ is. In addition, equations 5 and 6 are not immediately obvious from preceding discussion and can use a few more lines of derivation. Also it seems the derivation assumes that predicted label is binary (the counterfactual label is 1 - Y). What if the tasks is multi class -- how will the derivation generalize ? The details for experiments are reasonably sufficient that I can reproduce the results (assuming the code is also provided). 

In terms of significance, the spurious correlation problem is of course an issue in rationalization literature with not many solutions. the authors provide a novel strategy to solve the same and the results in terms of rationale accuracy is better compared to standard training. Is the improvement because the model is better at not picking up the spurious correlation -- I don't know. An interesting experiment would be to see if the model trained with MMI pick up on rationales from other aspect more than the specific aspect in the correlated setting. 

The biggest issue with the paper appears to be that the experiments are mostly run on two datasets, both belonging to sentiment analysis domain only. While results show improvements in these two datasets, it is unclear if they will generalize beyond these datasets or beyond sentiment analysis domain. 
limitations_and_societal_impact: Please include a bit more talk about how you believe the method would generalize beyond simple sentiment analysis (for example, NLI, etc)
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: 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.

main_review: Strengths
-	The proposed idea is intuitive and interesting.
-	Experimental results show improvements over baselines (but also see point 2 under weaknesses).

Weaknesses
-	Some parts of the paper are difficult to follow, see also Typos etc below.
-	Ideally other baselines would also be included, such as the other works discussed in related work [29, 5, 6]. 

After the Authors' Response
My weakness points after been addressed in the authors' response. Consequently I raised my score.
- All unclear parts have been answered
- The authors' explained why the chosen baseline makes the most sense. It would be great if this is added to the final version of the paper.

Questions
-	Do you think there is a way to test beforehand whether I(X_1, Y_1) would be lowered more than I(X_2, Y_1) beforehand?
-	Out of curiosity, did you consider first using Aug and then CF.CDA? Especially for the correlated palate result it could be interesting to see if now CF.CDA can improve.
-	Did both CDA and MMI have the same lambda_RL (Eq 9) value? From Figure 6 it seems the biggest difference between CDA and MMI is that MMI has more discontinuous phrase/tokens.

Typos, representation etc.
-	Line 69: Is X_2 defined as all features of X not in X_1? Stating this explicitly would be great.
-	Line 88: What ideas exactly do you take from [19] and how does your approach differ?
-	Eq 2: Does this mean Y is a value in [0, 1] for two possible labels? Can this be extended to more labels? This should be clarified. 
-	262: What are the possible Y values for TripAdvisor’s location aspect?
-	The definitions and usage of the various variables is sometimes difficult to follow. E.g. What exactly is the definition of X_2? (see also first point above). When does X_M become X_1? Sometimes the augmented data has a superscript, sometimes it does not. In line 131 the meaning of x_1 and x_2 are reverse, which can get confusing - maybe x’_1 and x’_2 would make it easier to follow together with a table that explains the meaning of different variables?
-	Section 2.3: Before line 116 mentioned the change when adding the counterfactual example, it would be helpful to first state what I(X_2, Y_1) and I(X_1, Y_1) are without it.

Minor points
-	Line 29: How is desired relationship between input text and target labels defined?
-	Line 44: What is meant by the initial rationale selector is perfect? It seems if it were perfect no additional work needs to be done.
-	Line 14, 47: A brief explanation of “multi-aspect” would be helpful 
-	Figure 1: Subscripts s and t should be 1 and 2?
-	184: Delete “the” 
limitations_and_societal_impact: There is a broader impact section which discusses the limitations and dangers adequately.
review_title: null

Meta-review: This paper initially received diverging scores, but after the author response, two reviewers updated their reviews and increased their scores. The reviewers are now unanimous in their assessment that the paper tackles an important problem using an interesting and novel approach, and draws a unique connection between explanations and spurious correlations. It also shows good improvements compared to the baseline. 

There are some additional experiments and analyses requested by the reviewers that I urge the authors to add: more extraction methods, more careful experimental settings (multiple runs, report mean and std), other datasets besides the two (the fact there are several versions of them isn't enough), and qualitative analyses. 

Finally, while not mentioned by the reviewers, one should acknowledge that the evaluation only targets plausibility (by comparing with human explanations) and not faithfulness. This is especially concerning considering the issues with select-predict models raised by Jacovi and Goldberg, "Aligning Faithful Interpretations with their Social Attribution". Therefore, the paper should at the very least discuss these limitations, and more ideally, also evaluate faithfulness (e.g., via sufficiency and comprehensiveness metrics; see Wiegreffe and Marasović, "Teach Me to Explain: A Review of Datasets for Explainable NLP", for a good summary of these concepts). 

Despite these issues, the paper makes valuable contributions and I therefore recommend acceptance. 
Decision: Accept (Poster)