Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 8: Accept
review_content: Summary:
       The authors take two tasks,sentiment analysis and natural language inference, and identify datasets for them which they counterfactually augment it by asking people over the Amazon Mechanical Turk Platform to change either the sentiment (in the case of sentiment analysis) or the nature of relationship in the NLI task by making minimal changes to the text that produce the targeted changes. 

Authors find that popular models trained on either fail on the other dataset while the models trained on both actually generalize much better. This is because the original sample and its counterfactual pair the label changed , has the difference in the text that matters to the change and this pair could reduce spurious correlations that models might find in the data distribution. 

Pros: 
 This is a very interesting experiment and certainly the dataset that will be released would be extremely valuable to the community. The one part (I dont have much NLP background but I do have a causality background) that I like most is that the new text generated are counterfactual in some real sense with respect to a real world generating process - that is people modifying text with changed targets.

 A lot of existing work that claim to do counterfactual changes do not specify assumptions about the generating mechanism. For counterfactuals to be valid they have to be intervention on the actual generating mechanism (or an assumed one) acting on a given unit (latent) that produced the current sample. The paper in that respect (even if it does not explicitly specify relationship between counterfactuals and generating mechanisms) tries to be faithful to a "strict causal notion" by actually asking people to modify the text. 

Cons:
    - I think the authors want to make an explicit connection to counterfactuals as understood in the causality community. Then they shy away from it saying they are inspired by it. May be a formal exposition in the supplement about counterfactuals and generating mechanisms could help readers from other communities (NLP) even it means repeating standard/synthetic examples. Its good to say what exactly in a counterfactual generation process, the "people" in amazon turk were substituting. 

   -  Is the romantic/ horror flips and their absence the only spurious thing in Figure 4 ? 
  -  In figure 6, it appears that BERT is sensitive to the domain - does it mean that it is bad ? - Authors indicate that ideally it must not be so. Because Table 3 results seem to indicate that BERT performs the best in almost all the cases .
 -  Can the authors highlight the best performances in each case in the Tables by a bold face.  It helps easily eye ball the best performing model.
 
review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: This paper addresses the problem of building models for NLP tasks that are robust against spurious correlations in the data by introducing a human-in-the-loop method: annotators are asked to modify data-points minimally in order to change the label.  They refer to this process as counterfactual augmentation.  The authors apply this method to the IMDB sentiment dataset and to SNLI and show (among other things) that many models cannot generalize from the original dataset to the counterfactually-augmented one.

This contribution is timely and addresses a very important problem that needs to be addressed in order to build more robust NLP systems.

Because, however, of a few limitations, I recommend weak acceptance.

My main hesitation comes from a lack of clarity about the main lesson we have learned.  In particular, if the goal is to use this method to augment the data we use to train NLP systems in order to make them more robust, it seems that the time cost of the process will be prohibitive.  On the other hand, perhaps these methods could be used to identify the kind of spurious correlations that models tend to rely on, which could then be used in a more automated data augmentation process.  If that's the goal, however, a more detailed error analysis would need to be included.

A few small comments:

* There was some analysis of the augmented IMDB dataset, but none of the SNLI dataset.  I would love to see a more detailed investigation of what annotators usually did.  For instance, a reason that hypothesis-only models do well is that certain words are very predictive of certain labels (e.g. "not" and contradiction).  Do people leave the negations in when modifying such examples for entailment or neutrality, thus breaking the simple correspondence?  That's a very simple kind of question; more generally, I'd like to see more analysis of the new dataset.

* The BiLSTM they use is very small (embedding and hidden dimension 50).  Given that BERT is most robust against their manipulation, it would be good to see a more powerful recurrent model for comparison.  It would be easy to use ELMo here, if the main question is about Transformers vs recurrent models.


Some very minor / typographic comments:

* abstract: "with revise" should be "with revising"
* first paragraph page 2: some references to causality literature and definition of spuriousness as common cause
* page 2, "We show that..." I'd break this into two sentences to make it easier to parse.
* Table 3: I would make two columns for each model with accuracy on original versus revised.  With the current table, one has to compare cells in the top half of the table to those in the bottom half of the table, which is quite difficult to do.
review_title: Official Blind Review #4

final_score: rating: 1: Reject
review_content: The authors propose a new way to augment textual datasets for the task of sentiment analysis, in order to help the learning methods to generalize better by concentrating on learning the different that makes a difference. The main idea of the paper is to augment existing datasets with minimally counteractual versions of them, that change the sentiment of the documents. In this way, all spurious factors will naturally cancel out. The authors use the newly created datasets and show that indeed, the retrained algorithms on the augmented datasets generalize much better.

The main contribution of the paper is the introduction of the idea of counterfactual datasets for sentiment analysis.

Overall, I find the idea of the paper quite interesting and I’m excited to use the datasets they have created. However, I think the relative novelty of the paper does not meet ICLR standards, and it’s better suited as a whitepaper attached to an open dataset release.
review_title: Official Blind Review #2

final_score: rating: 8: Accept
review_content: This paper seeks to separate "causal" features from ones with spurious correlations in the context of natural language machine learning tasks. The proposed approach is to ask human annotators to alter examples in a minimal way that changes the label. Thereby the humans separate out the causal features (those changed) from the spurious or irrelevant features (those left unchanged).

Experiments show that classifiers trained on the original data perform poorly on the altered data and vice versa, but (unsurprisingly) training on the union of the two datasets results in a classifier that performs well in both cases. Furthermore, training an SVM on the original results in irrelevant attributes (such as movie genre) being weighted, whereas these weights are largely removed when training on the union of the datasets. This suggests that the augmented training data results in weighting the "right" features more. 

Overall, I think this paper should be accepted because it makes several interesting contributions: It proposes an interesting approach, shows intriguing experimental results, and produces an interesting dataset (size ~2k) that may be useful for future testing.

The main limitation of the paper is that the evidence is largely circumstantial. The method has intuitive appeal and the experimental results are suggestive, but the experiments do not conclusively show that the method achieves something that ordinary machine learning does not.

My suggestion for a further experiment would be to apply the movie review classifiers to, say, book reviews -- something where the task is fundamentally the same but the context is different. If the classifier trained on the union of the original and altered datasets performs better than a classifier trained on only on dataset, then that is strong evidence that this approach yields better extrapolation.

review_title: Official Blind Review #3

Meta-review: This paper introduces the idea of a counterfactually augmented dataset, in which each example is paired with a manually constructed example with a different label that makes the minimal possible edit to the original example that makes that label correct. The paper justifies the value of these datasets as an aid in both understanding and building classifiers that are robust to spurious features, and releases two small examples.

On my reading, this paper presents a very substantially new idea that is relevant to a major ongoing debate in the applied machine learning literature: How do we build models that learn some intended behavior, where the primary evidence we have of that behavior comes in the form of datasets with spurious correlations/artifacts.

One reviewer argued for rejection on the grounds that dataset papers are not appropriate for publication at a main conference. I don't find that argument compelling, and I'm also not sure that it's accurate to call this paper primarily a dataset paper. We could not reach a complete consensus after further discussion. The other reviews raised some additional concerns about the paper, but the revised manuscript appears to have address them to the extent possible.
Decision: Accept (Spotlight)