Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: This paper explores how saliency explanations can be used to improve the performance of knowledge-graph-augmented commonsense reasoning (CSR) models, by investigating coarse to fine KB components (an entire graph to its nodes and paths). The paper first explores how KG influences the models in making correct predictions using oracle models, which have access to true saliency explanations. Base on that, the paper proposes SALKG, a framework for KG-augmented CSR that learns from coarse and/or fine saliency explanations that are automatically created. The proposed models achieve better performance compared to the baselines used in the paper.
main_review: The paper carefully constructed and performed detailed experiments and analyses on KG-augmented models. The detailed answers to three questions for the oracle KG-augmented models are sound and contribute to understanding the role of KG for commonsense reasoning. The proposed SALKG model is sound too, and improves the performance over the baseline models compared in the paper.  The paper is well structured and easy to follow. The methods and ideas are clearly presented. 

My main concern is that the SALKG method proposed in this work is not novel enough for the conference. In addition, the entire study is rather incremental. Using attention scores to obtain explanation over the input components is not new. 

The paper should provide more comparison between the proposed models (SALKG) to other strong baselines listed in the leaderboards (e.g., on CSQA and OBQA) to render a more comprehensive understanding, in addition to using in-house splits of CSQA. This limits the significance of the paper and raises some concerns; e.g., if the observations made on the proposed model can transfer to the state-of-the-art models, although it may not be straightforward to construct saliency explanation for all types of existing SOTA models.

According to Table 5 and Table 6, random explanations can achieve pretty good results compared to the baselines and can sometimes be better than heuristic explanations, which somehow shows that the saliency explanation inputs are less influential. Can the authors explain more on this?

The authors may consider providing some quantitative analysis on how good the generated explanations are in addition to the case study.

The paper is in general well written, but still contains typos or grammatical errors, e.g., on page 4, “There are many existing saliency computation method which calculate the importance”. (“method” should be “methods”). 

limitations_and_societal_impact: No concerns
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: Motivated by that the knowledge graph used in KG-augmented models is useless sometimes, the authors propose two ensemble models by using coarse/fine saliency explanations. Specifically, the authors first get saliency explanations through F_KG, F_NO-KG and saliency methods. Then, the authors propose oracle model using coarse/fine/hybrid explanations as extra inputs, and achieves superb performance than its baselines. Finally, based on oracle models, the authors develop the SALKG framework that uses explanations as supervision for distillation learning. SALKG achieves SOTA performance on CommonsenseQA and competitive performance on OBQA. It is the first to supervise KG-augmented models with KG explanations.
main_review: Pros:
- Well-written introduction of the basic concepts and structure of KG-augmented models.
- The idea of learning from KG explanations can be applied to other KG-augmented methods and probably generalizes to other tasks such as reading comprehension, and thus would be useful to a wider community. 
- The coarse saliency explanations indicate when KG helps the model and are gained from the differences between the confidence probabilities of F_KG and F_NO-KG. The fine saliency explanations show whether a unit in KG is useful and are derived from existing effective saliency method. The design of two kinds of saliency explanations is reasonable and effective. 
- Thorough ablation studies to explain the behavior of the model, which contain the discussion of two kind of explanations, the saliency methods and the loss function.

Cons and Questions:
- Intuitively, the fine-based model should be better than coarse-based model. However, SALKG-Coarse outperforms SALKG-Fine in experiments. And the SALKG-Fine performs worse than its original KG-augmented model as shown in Table 6. It seems that there is no analysis of these results. 
- The case studies seem insufficient. SARKG learns the distilled knowledge from saliency explanations. In the words of authors, SARKG is explicitly taught which KG components should be used. However, the case study just gives examples that the knowledge graph is useless or introduces noise. There is no comparison of attention weights learned by SARKG and KG-augmented models.
- The different KG unit types of fine explanation in Table 1, i.e., node and path, which one is more efficient when they server as fine saliency explanations? 
- The training costs of the proposed model is much greater than baselines. 
- Presentation problems:
    (1) Many references have been published, but the authors still used their reference formats in arxiv. 
    (2) In Figure 3, it seems like the arrows indicating the SALKG pipeline is purple rather than blue described in the caption. 
limitations_and_societal_impact: Please refer to the main reviews.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposes a framework, SALKG, for commonsense question answering (i.e., Multiple choice). It employs saliency explanations (i.e., coarse and fine) to regularize the attention mechanism and provide direct supervision for the task. The proposed framework is shown to achieve state-of-the-art or competitive results on three QA benchmarks, namely CSQA, OBQA, and CODAH. The proposed work has highlighted the right research questions and answered quantitatively and to some extent qualitatively. Overall, the paper is well motivated and proposes a technically solid framework for multiple-choice commonsense question answering tasks. I have some concerns regarding the details on using the knowledge graph that I have highlighted in the weaknesses section.
main_review: Strengths:
1. The proposed framework, SALKG, is a technically solid framework for multiple-choice commonsense question answering tasks.

2. The idea of coarse/fine saliency explanations has the potential to provide direct supervision to the model focus on useful KG features.

3. The comprehensive quantitative analyses are conducted on three standard benchmarks.


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Weaknesses:
1. ConceptNet has approximately 34 relation types. But, the most common types of positive relations are: RelatedTo, IsA, PartOf, and AtLocation. how many relation types were considered in this work? and Why? Moreover, ConceptNet has negative relation types (e.g., Antonym, DistinctFrom), were negative relation types also considered? Would these not influence the model in a bad way, if not excluded. In any case, the paper fails to explain any details on this part.

2. How the words/phrases (for saliency explanations) were selected from the text of the question and/or choices. For example, for the question, "What would you put in a teakettle?" how the n-grams for the paths are picked, as provided in the appendix, "(1) teakettle –[is a kind of]–> x <–[is at location]– water , (2) put –[is related to]–> x –[is used for]–> water , and (3) teakettle –[is a kind of]–> x –[is used for]–> water". What words/phrases from the question and the choice are picked and how? the paper fails to provide any details on this part as well. Moreover, was there any kind of text pre-processing (e.g., stemming,  lemmatization) performed? If yes, what are the trade-offs?

3. Concatenating the question with the answer choice(s) would make the text inarticulate. Does it have any effect on the embeddings?

4. The paper claims that the proposed framework achieves significant performance gains. But, are the results statistically significant? Was any statistical significance test performed?


limitations_and_societal_impact: N/A
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The paper explores how saliency explanations can be used to improve the performance of KG-augmented models. The authors compared two types of saliency explanations: coarse (whether the KG is useful) and fine (which nodes or paths in the KG are useful) They first showed that using oracle KG saliency explanations as inputs improves model performance (section 4).  They then introduced a model that jointly learns to predict the saliency explanations, and solves the task by attending to corresponding KG features highlighted by the predicted explanations (SALKG). The results showed that SALKG models yield large performance gains in three popular commonsense QA benchmarks (CSQA, OBQA, CODAH).
main_review: In terms of originality, I'm not sure if the work is innovative. Several papers have used attentional mechanisms in KG-augmented NLP models (Hao et al. 2018 CCM, Wang et al. 2019 KGAT, Song et al. 2020 KGAnet). And the attention in those models is similar to the saliency explanations in the current work. For instance, Hao et al. 2018 proposed a commonsense knowledge-aware conversational model (CCM). It retrieves the relevant knowledge graph from ConceptNet given a post and then uses the static graph attention mechanism to extract the semantic information of the post. It seems that the authors are aware of those studies since they cited some of them in the Related Work section. I was curious to know the difference between the current work and the previous ones. It seems to me that both of them train the model to predict which part of the knowledge graph is relevant and use such predictions to enhance model performance.

The submission is relatively sound. The authors first demonstrate that KG-augmented models have large performance gains when provided explanations in the Oracal setting. Then they showed that the explanation does not need to be given since the model can learn to predict the explanation. I do have some concerns about the robustness of the performance. According to the statistical test results reported in Appendix A.10, some improvements are statistically insignificant (line 759) and others are significant under the criteria p<.1, which is not the conventional cutoff value (0.05) for rejecting a null hypothesis. If the authors could report the exact p-values obtained and confirm that the improvements are still significant with a more conventional cutoff value for significance, that would be great.

Another question I have is about Line 316 in the main paper. The author mentioned that "for RoBERTa+PathGen on CODAH, RANDOM-Coarse beats all other models". This is a counter-intuitive result. The authors argued that "SALKG’s limited performance may stem from the coarse explanations being unable to consistently discriminate between No-KG and KG, which can happen if No-KG and KG have very similar predictions". It would be great if the author can quantitatively verify that No-KG and KG indeed have very similar predictions in this case so that their explanations are valid. I doubt that is the case since the performance of No-KG and KG are different (see Table 6, PathGen column, row No-KG and row KG).

Finally, in main text 6.4 and Appendix A.5, the authors presented some case studies showing "the positive KGs/paths are useful for predicting the correct answer, and vice versa. " (main text Line 371). I'm worried about the risk of cherry-picking associated with these examples. To make a more convincing argument, the authors may consider presenting random samples to human raters that are naïve to this study and let them rate the usefulness of the KG/path. We can then compare whether the model's judgment aligns with human judgment.

 The submission is clearly written and well organized. If the authors can address the issues I mentioned above and make the arguments and conclusions in the paper more convincing, the results are important since it demonstrates that models can learn to predict the usefulness of knowledge graph (saliency explanation) and leverage such predictions to further improve model performance, although I don't think this observation is new (see my first paragraph above). This approach can be used in common-sense reasoning, question answering, etc.

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Updated: I've read the authors' responses to my review as well as the ones to the other reviewers'. I think their responses address some of my concerns, especially the statistical significance of the main results and the strong performance of random explanation baselines. I think the updated results support the authors' arguments much better. I appreciate the newly added user studies. Although the model's predictions about the saliency explanation did not align with the human annotators, I agree with the authors that this does not contradict the paper's main claim. I appreciate the authors' clarification on the novelty of this paper. However, I still think this work presents a combination of some existing ideas and thus the novelty is limited. Given the improvements and the limitations, I decided to raise my score to 6 but no further. 
limitations_and_societal_impact: There's no foreseeable negative societal impact of this work.
review_title: null

Meta-review: This paper proposes SalKG, a framework for using saliency explanations to learn to better use knowledge graphs (KG) for commonsense reasoning (CSR) tasks. Saliency is considered at two levels: (1) the usefulness of the KG overall called "coarse" and (2) the usefulness of specific nodes and paths in the KG called "fine". To create coarse explanations the authors introduce an ensemble based saliency method for comparing models with and without access to a KG. Fine explanations use off the shelf saliency methods. This leads to SalKG, where explanations for the training data of a CSR task are used to regularize the learning of the attention mechanisms in the CSR architecture. Experiments on the CSQA, OBQA, and CODAH experiments show that the hybrid model significantly outperforms prior work baselines using both BERT and RoBERTa base models.

Many of the reviewers agreed on the strengths of the paper. They found that method is sound and leads to significant results. They generally all agreed that the paper was well written and clear in its presentation. They also remarked on the quality of the analysis, such as the oracle-based approach for analyzing how learning from saliency can lead to improved accuracy.

The reviewers also raised several main concerns. First, some expressed concern that the method was not sufficiently "novel." As the authors clarified in their response, while attention and saliency are both well studied, the significance of their work is using saliency as a training signal for incorporating KGs into CSR.

The other main concern regarded comparison with a wider range of baselines, as well as using the official train/test split for CSQA. In the response, the authors addressed these concerns. First, additional comparisons on the same datasets are included in the appendix. Second, they also submitted a model to the official CSQA test server. Their RoBERTa based model places third among other RoBERTa based models. The authors are encouraged to include these additional results in the final version of the paper.

Ultimately, the strengths outweigh the potential weaknesses. As one reviewer noted, this work has potential application beyond CSR to other tasks like reading comprehension, and it is likely to be of interest to the wider community.
Decision: Accept (Poster)