Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: This work introduces a model for incorporating language explanation feedback for domain adaptation with limited labeled data from the target domain. To get this feedback, users are provided with a heat-map of feature attribution scores which they may use to provide feedback on down-weighting / up-weighting attribution scores of certain features, along with labels. Crucially, this paper proposes an approach to convert specific feedback provided on one example, into an abstract template which can then be instantiated on other examples, thus improving coverage of the explanation to many more examples. Finally, this feedback is used to construct a loss which essentially guides the model to up-weight/ down-weight certain feature attribution scores, and this loss along with log-likelihood on the small number of collected labels is used to adapt the model. From experiments, we see that this approach does better than various versions of fine-tuning, hence demonstrating the efficacy of explanations.
main_review: This is a well written paper with a neat idea of using explanations to finetune models to a new domain. The main insight is that these explanations have “limited regularization strength as it is only enforced on the very few examples associated with human feedback”. However, there are a few things that could be further improved / further elaborated upon: 

- What is the role of compositionality in explanations here? Is it just for improving precision in the kinds of examples that are matched? The other place where it could be potentially helpful is in modeling feature interaction, but looking at Table-5(b), it doesn’t seem to help significantly

- In Section 4.2, how are time costs estimated? How many examples is the fine-tune (C_sample) model finetuned on (and how much more data is this compared to C_strict?)

- Looking at Table-3 and Table-4, how is finetuning on C_sample (more data) worse than finetuning on C_strict (lesser data). For e.g. finetuning on C_sample results in 40.7 F1 on HatEval → GHC, and finetuning on C_strict gives 45.1 F1 on the same. Similarly for Stormfront → GHC, we see 49 F1 with C_strict but only 45.0 with C_sample. It appears that finetuning on more data decreases performance.

- Time: How long does it take to finetune with the R_soft loss compared to just straightforward finetuning? It seems like computing the losses in Equation-2 could be slow. 

- Hyperparameters: How large is the development set? Typically for domain adaptation, a large labeled development set from the target domain is not practical, and thus hyperparameter tuning is often challenging.

- Related Work: Line 68 claims that REMOTE is the first work to use feature-level supervision from explanations. However, this is also considered in the generalized expectation criteria (Mann et al 2010), where they introduce expectations on the label conditioned on certain features. (e.g. the bigram not bad appearing decreasing probability that a review is negative).   
limitations_and_societal_impact: N/A
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: The paper deals with the problem of spurious patterns generally learned by a language model. They address this problem by refining the model using compositional explanations. First, they obtain annotation from human workers and then obtain first-order logic from them that map and explain internal features of the model. These updated explanations are used to update the model. They show their results in hate speech and sentiment classification datasets.
main_review: 1. While updating a model with explanations is a neat idea, this reminds me of several works on critique-based explanation generation works from the domain of conversational recommender systems.
2. Quite interesting analysis on annotation time, I wonder how so few explanations could generalize over the full dataset? Is it because of the regularization? 
3. Quite solid results otherwise and solid ablations showing the effectiveness of the regularization and how the performance changes with the number of explanations available. 
limitations_and_societal_impact: NA
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper presents an idea of using the logical forms of explanations to help refine pre-trained language models. 
main_review: - First of all, I understand the high-level ideas but had some trouble understanding some technical details. For example, it is not clear to me
  - How to generate refinement advice and how reliable is the generated advice?
  - How to make sure the interaction module and compositionality module can do what we expect them to do? Particularly, for the compositionality module, is it sufficient to use some algebraic operations to replace the logical operations? 
  - Which specific algorithm was used to generate the explanation heat-map, given [13] is a survey paper and contains a dozen of algorithms
- Line 169, how reliable the semantic parser is, especially on some out-of-domain data? Will the noisy outputs from the semantic parser have a big impact on the final results?
- The results reported in this work are not directly comparable, as they are evaluated under different conditions. Some ablation studies will be great to demonstrate the benefit of the proposed algorithms.
- Regarding the experiment setup, I don't understand why it makes sense to do evaluations on a sampled subset, instead of directly comparing with "fine-tune(C_all)"?

I would love to update my reviews if all the listed confusion can be addressed.
limitations_and_societal_impact: It would be great to discuss how the proposed ideas can address the inherent biases in pre-trained language models.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: see main review
main_review: This paper describes a procedure for improving the fine-tuning of deep network
by eliciting fine-grained natural langauge explanations of model decisions from
human annotators. On a set of unlabeled examples, annotators identify a set of
predicative or relational features that provide evidence for a specific label,
and describe the relationship between these features and labels in language.
Descriptions are then parsed into logical rules, which are applied to a large
corpus of unlabeled data. Finally, the model is fine-tuned on the
(semi-automatically) labeled data with an "attribution loss" that encourages
off-the-shelf feature attribution methods to pick out the labeled features for
the correct label (i.e. for the model to be right for the right reason).
Given a fixed annotation budget, this method outperforms direct labeling of a
(larger) dataset. It also outperforms different learning-from-explanations
techniques trained on the same annotations. Additional experiments 

STRENGTHS

- Intuitive approach

- Careful & thorough experimentation

WEAKNESSES

- Writing is difficult to understand in places

- A few details missing needed to interpret numerical comparisons

This paper takes three well-studied techniques (learning from human-generated
feature attributions, learning from noisy labels, and semantic parsing),
and combines them in an interesting and evidently effective way. I think it's a
good fit for NeurIPS.

PRESENTATION

- Given how much there is going on here, I guess it's inevitable that a lot of
  important details wind up in the appendix. Things that really need to go in
  the main paper: (1) identity / compensation of annotators, (2) just a little
  bit of detail about how the semantic parser works.

- Conversely, I think 3.3 can be simplified a lot. Is it really necessary to
  describe everything in terms of modules? If every explanation maps to a
  logical form, then this whole section is (1) if the LF premise applies to a
  sentence, produce its label, (2) otherwise, soften the LF by replacing its
  predicates with more general ones, assign these generalized predicates lower
  scores, and use a probabilistic logic. I think most reviewers will have at
  least passing familiarity with this machinery, and it's not necessary to
  reinvent the wheel.

- This paper has numerous grammatical issues (more than I can list here) and 
  would really benefit from careful editing.

- The whole point of the 9th page was to include the societal impact section in
  the main paper; please don't bury it in the appendix if you think it's
  relevant (and if you don't think it's relevant, simply don't include it).

- Some examples and failure cases (even if those have to go in the appendix)
  would be really helpful.


QUANTITATIVE EVALUATION

- I'm not sure how to interpret error bars in tables 3/4/5. Are they standard
  errors of the mean? Standard deviations? 95% CIs? What has actually been
  randomized?  The appendix mentions a seed: does this control model weights
  (i.e.  we're retraining both the upstream and downstream models in each
  experiment) or just the order in which data is presented (as we fine-tune the
  downstream model)?

- What does bolding mean? Some non-bold numbers are identical (e.g. distillation
  / source / HatEval in Tab 4 matches Remote+IG but doesn't get bolded) or
  within error bars (L2 reg / target / StormFront). I suspect that many of these
  differences are not statistically significant, especially after doing a
  multiple hypothesis correction. This certainly isn't a deal-breaker (REMOTE
  seems to be pretty clearly best overall, especially when considering source
  scores and FPRD), but the paper must do some kind of testing (and ideally bold
  all the numbers that are indistinguishable from the best result).

limitations_and_societal_impact: see main review
review_title: null

Meta-review: The paper introduces an approach to incorporating natural language explanations into model fine-tuning. Annotators are presented with a heat-map of feature attribution scores on a target unlabeled corpus and they are asked to refine the importance scores and describe interaction of features in language. Description are then parsed into logical rules, which provide abstract templates that can be instantiated on other examples, thus improving coverage. Then the model is then regularized to align with the pseudo-labeled attribution data. 

--

The paper tackles the important problem of efficiently exploiting costly human explanation on few examples by generalizing such explanations "compositionally" to other examples: to do so, the method effectively uses known techniques in semantic parsing, pseudo-labeling and feature attribution methods. All reviewers and myself agree that the method is interesting and novel, the experimentation is "careful & thorough" and the results confirm the effectiveness of the pseudo-labeling approach. I suggest the authors to incorporate all the precious reviewers' feedback. Following reviewers' feedback, extra care should be put into proofreading the paper for grammar and spelling errors (some I can think of right now are in Fig. 2 caption "annotaters", "rulesand", ...).

I recommend this paper for acceptance.
Decision: Accept (Spotlight)