Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper propose a novel method on training VQA system that can achieve competitive performance both in-distribution and out-of-distribution. Specifically, the proposed system leverage a recent causality-based QA model to estimate the OOD distribution (via counterfactual reasoning), thus building two sub-modules to handle ID and OOD settings respectively. The two modules are then blended via knowledge distillation to train a single student model that can better handle the inductive bias. The authors perform extensive experiments on VQA and text-only question answering, with multiple baseline models and show the proposed training paradigm can consistently improve the overall performance (measured by harmonic mean over ID and OOD settings).

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Thanks for the response from the authors. I've updated my rating after reading the rebuttal as well as the comments from other reviewers.
main_review: 1. Originality
- The main idea of the paper can be summarized as "proportionally distilling two teacher models into a single one to handle both ID and OOD setting" in question answering, which is quite neat and novel. The authors cleverly leveraged a recent study that approximates the OOD performance via causal QA and counterfactual reasoning. 
- The other parts of the paradigm are quite intuitive and straightforward, including estimating the relative weights based on cross-entropy or predicted probability, and to transfer knowledge to a new model via distillation. The combination of these techniques are shown to be effective for building a more robust QA system.

2. Quality
- The proposed system is technically sound, and authors have done extensive experiments on VQA and reading comprehension to validate its effectiveness, and important ablation studies are provided.
- From the experiments the proposed system consistently show improvements over different baseline models.

3. Clarity
- In general, the paper is well-organized and quite easy to follow, but there still exists some minor issues in the writing. For example, in L151 and L165 the authors claim "setting the weights disproportionate to the matching scores". My guess here (based on the equations) is that the authors meant to say "inversely proportional" rather than "disproportionate". Otherwise I could not follow the logic.

4. Significance
- This paper achieves quite significant improvements over baselines and the proposed system seems compatible with a wide range of systems.
limitations_and_societal_impact: * My major concern about this work is the lack of evidence about the approximation of OOD setting, in other words how accurate/reliable the OOD-teacher is. As the authors mention in L64, since the OOD distribution is unseen in training the teacher model is actually an approximation using counterfactual reasoning. Therefore the quality of the approximation is important. In particular, in L294 Q6, the authors also mention that using ID-Prediction as ID-Knowledge is a bad idea, showing the importance of oracle when available. I would expect that a more careful (hopefully quantitative) analysis on the OOD approximation would be beneficial. Since the VQA setting does not have the OOD annotations, probably a proxy can be used, or using reading comprehension (SQuAD) as the testbed to measure the quality of "approximated OOD" vs. ground-truth OOD.

* As for the novelty, the most important component in this system (i.e., approximated OOD-teacher) is from a recent work, and other techniques (model weighing and knowledge distillation) are not new as well. Admittedly combining multiple existing ideas holistically is still a contribution but individual components are not novel inventions.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This paper proposed Introspective Distillation (IntroD) to achieve good OOD generalizability without sacrificing ID performance. Their training paradigm has three key components (1) factual reasoning ID teader model and counterfactual reasoning OOD model to capture ID and OOD inductive bias, (2) intersection between two inductive biases by comparing the predictions between ID teacher and OOD teader, and (3) knowledge distillation for a strong student model. They conduct experiments on visual QA and extractive QA tasks. On VQA-CP v2 and VQA v2, built upon four counterfactual teacher models (i.e., RUBi, LMH, CSS and CF-VQA0, their introD have shown large ID improvements, leading to considerably large improvements in harmonic mean of ID and OOD accuracies. They also conduct detailed ablation studies to identify the best introspection and distillation strategy. For Extractive QA experiments, they follow the experimental setting from prior work (Ko et al., 2020) where the dataset is divided into subsets based on the position of answers. IntroD has shown its effectiveness on the extractive QA experiments as well.  
main_review: **Originality:**
The ID performance deterioration by debiasing has been pointed out, and this work addresses the important problem with a simple yet effective approach. By combining prior counterfactual models with their proposed introD, they significantly improve the ID performance while maintaining or improving the original OOD performance. 

**Quality:**
I think the proposed approach is technically sound and is well-motivated and the rigorous experimental results support its effectiveness. The detailed analysis helps us to understand the best training strategy to address the aforementioned issue. Probably more experiments on extractive QA. For example, testing on another extractive QA dataset having a similar positioning bias, or studying another annotation bias seen in extractive QA (e.g., lexical overlap) would make this paper even stronger. 

**Clarity:**
I think this paper is generally well-written and easy to read. 

**Significance:**
I think this paper addresses an important issue and provides a simple yet effective solution which can be combined with current or new debiasing techniques. The experimental results show its effectiveness on two tasks, namely VQA and extractive QA. As mentioned above, the experimental results on extractive QA may be weak, and more experiments on that task would be helpful. Position issue is one of the core issues in extractive QA, but there are several major annotation biases in the task, and I wonder if the proposed approach would be useful or not for those (more complex) inductive biases. 
limitations_and_societal_impact: The authors acknowledge that the OOD performance is proportional to OOD-teachers’ debiasing ability and future work would study how to establish a stronger OOD-teacher. 
I cannot find any major discussions on the **negative** social implications of this work in the submitted manuscript.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: The paper claims to provide a better balance of in-domain and out-of-distribution settings. Normally, algorithms optimized for one hurt the other. The paper proposes to use a weighting mechanism to to balance a model's reliance on in-domain world (modeled as causal factual world) and out-of distribution world (modeled as causal counterfactual world). Experiments on VQA-CP and SQUAD datasets show that the proposed method does indeed help improve ID performance of several algorithms optimized of OOD accuracy while maintaining/slightly improving its OOD performance. 
main_review: **Strengths** 


**[S1]** Interesting and well-motivated method: The proposed weighting scheme by dividing the training samples into belonging to "ID world" and "OOD world" is well-motivated and interesting. Despite its simplicity, it is quite effective (S2).

**[S2]** The paper boasts really good results: The paper consistently improves ID performance of several OOD-oriented algorithms without sacrificing its "debiasing" abilities. This is, furthermore, achieved with a well-motivated approach, which is more than can be said for several recent works. 

**[S3]** Clearly written *except for* Section 3.1: The paper, generally, raises, addresses and answers all of the most relevant questions adequately and clearly. It is easy to understand the motivations, implementations, and results of those choices.  The ablations experiments also show a clear picture of different model choices. The only caveat is the details about the ID and OOD teacher which is sorely lacking (even with supplemental materials). More on the below


**Weaknesses**

**[W1]** Very unclear about how the ID and OOD teachers are trained/modeled (aka Section 3.1 + supplementary): I am assuming the paper simply followed the implementation of [23]. If that is the case, then there are no problems and I am familiar with [23]. However, it is super unclear what is happening with regards to ID and OOD teachers, even after reading the supplemental. E.g., the notations used for TIE and NIE are not consistent with the paper -- e.g., compare paper's presentation with Figure 3 from [23] which shows that counterfactual VQA used only Q to predict the answer, whereas the current paper shows links from inputs X which contains both V and Q to answer Y, which is not correct. Please clarify the exact formulation of the ID and OOD teachers in the revised version.

**[W2]** Some concerns about OOD/ID testing setup :

*W2.1* Use of soft/hard weighting for strong vs weak teachers is not very well motivated. Why is Rubi (or by extension, NIE methods) a weak teacher? Why does using hard weighing help "weak models"?

*W2.2* Are ID and OOD performance measured for the same model/data choices? As mentioned in [30], retraining before reporting ID performance can inflate the results on ID evaluation and effectively create two different versions of the model. Upon reading the paper, I don't think that this has been addressed. 

**Overall:** Overall, I think this is a good paper and I currently recommend a weak acceptance. However, it could be slightly higher if the clarity issues were resolved.
limitations_and_societal_impact: Yes
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: This paper studies learning models for visual and text-only question answering (VQA and SQUAD) that do well on both in-distribution test sets, as well as out-of-distribution ones.

The paper takes a knowledge distillation approach for this and designs two "teacher" modules: "ID-teacher" that captures in-domain bias, and "OOD-teacher" in which the in-domain bias is reduced using [23] (Niu et al 2021)'s method. The models are then ensembled and the aggregate probabilities are used to supervise training of a student model.


The paper evaluates this approach on VQA and SQUAD, with a variety of different methods as teachers. The method seems to increase performance on both OOD and ID sets (which is perhaps surprising, since one might hypothesize that the ID teacher ought to do best by itself on ID data, and likewise for the OOD teacher).
main_review: To this reviewer, this paper looks promising overall, however there are a few key concerns that hold me back from wanting to accept it at this moment. If they are addressed, I would be willing to increase my score in the rebuttal.


Strengths: To this reviewer, the pitch of the paper seems interesting, that of using knowledge distillation as a way to effectively ensemble a model that is robust on out-of-distribution data, with one that is robust on in-distribution data. It could be helpful for people working on VQA.

The ablaion study answers a lot of reasonable questions about model performance, e.g. that knowledge distillation from just the OOD teacher isn't as good, and about which ways of ensembling knowledge work best for which models.

Weaknesses: The novelty of this paper is a bit unclear to this reviewer. One argument made in this paper is that prior work on ensemble-based methods (like 10; Clark et al 2019 Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases") explicitly formulates what the language prior is. However, it is not clear to me how the causal approach proposed in this work (which seems to be from [23]; Niu et al 2021) does not explicitly formulate what the language prior is. 

Adding onto this point, to this reviewer, it wasn't super clear how the ID teacher and OOD teacher are trained, or what aspects of [23] (Niu et al 2021) are being used. I looked through the appendix as well but was still confused.

Relatedly, one concern that comes to mind is whether knowlege distillation helps in this dataset, or whether for the "harmonic mean" evaluation on both VQA-v2-CP and VQA-v2, using the proposed weighting method is what is important. In other words, could one ensemble the two teacher models, and directly use that (without knowledge distillation)?
limitations_and_societal_impact: There was no substantive discussion of limitations and societal impact of this work. The paper could be improved by adding such a section.
review_title: null

Meta-review: The reviewers appreciated the author response and recommend to accept the paper after the author response.

I agree this work provides a solid idea and approach to robust question answering. Adding the proposed approach on top of existing methods shows consistent significant improvements on both, visual question answering as well as reading comprehension (additional results for NLI are also added in the author response). 

I recommend acceptance under the expectations that the authors will address the concerns of the reviewers in the camera ready version as discussed in the author response, including but not limited to
1) include clarifications provided in author response and improve discussion of novelty
2) ablation study ensembeling two teachers
3) additional results on NLI
[if any results don't fit in the main paper, please add them to supplement]
Decision: Accept (Poster)