Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper addresses the degeneration problem occurring in the two-phase models for rationalization. The authors identified that the learning speeds of predictor and generator are out of sync. Thus the predictor overfits to the uninformative pieces selected by the not yet well-trained generator, leading to a sub-optimal model. The authors propose FR that uses a shared encoder to fold the two phases into one such that the learning speeds of two components can be aligned. The authors’ claims are supported empirically and theoretically. 
strengths_and_weaknesses: Strengths:

(1) The observation and the idea presented in the paper is interesting. Folding two phases of training into one enforces that the learning processes of two components are compatible, addressing the degeneration problem.
(2) The claims are well supported by both theoretical analysis and solid empirical results.
(3) The paper is generally well written and easy to follow. The idea and motivation are presented clearly.
(4) The topic discussed in the paper is valuable to the broader community, as it aims to improve the interpretability of NLP models.

Weaknesses:

The paper did not thoroughly discuss the discrepancy when using a unified encoder. The functions of two components are related but not identical. While parts of the encoder can be shared indeed, I cannot see why the unified encoder can work well if there are no other specialized layers in each component’s encoder.
questions: Have you considered adding specialized layers in each component’s encoder such that the encoders are partly shared but not identical?

It may be worthy if you can present some failure cases where the model failed to select reasonable rationales. This can pave ways to gain better insights of the model and be helpful to this line of research.

limitations: Yes, the paper discusses the limitations of their work.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper investigates the rationalization for NLP models. The authors analyze the relationship between generator and predictor in terms of working mechanism and propose folded rationalization that folds two phases of the rationalization into one via a unified encoder. Results show that FR improves over the existing baselines on beer and hotel review datasets.
strengths_and_weaknesses: Pros:
-	The proposed approach is simple but effective for rationalization in NLP.
-	The authors conduct in-depth analysis to show that the predictor in FR is regularized by the generator through the unified encoder.
-	Experiments show that the proposed approach outperforms baselines by a large margin.


Cons:
-	All experiments are conducted on two review datasets. It would be better to show the effectiveness of FR on other tasks.

questions: N/A
limitations: Yes
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper proposes a new method for the task of rationalized classification, where a (latent, discrete) rationale is extracted and then used in a prediction module to render a classification decision. The core idea of this paper is to share the encoders for both the generator (= selector) and the predictor modules. This model is trained using a sparsity and coherence regularizer from prior work. The paper presents some theoretical results on why this rationalizer structure should be effective.  Results on two rationalized classification datasets, BeerAdvocate and Hotel Reviews, show that the proposed approach produces rationales that agree more closely with human-labeled gold standards.
strengths_and_weaknesses: STRENGTHS

- The problem of rationalizing predictions is an important one. Being able to do this without any supervision is a worthy goal.

- The paper compares against results from several pieces of recent prior work and shows strong improvements over these on the automatic evaluation of rationale extraction.

- The proposed method is simple and could be widely adopted for this task as a result.

WEAKNESSES

- I'm not convinced of the correctness of the theory in this work.

Let's start with the definition of uninformativeness. This is defined in terms of the conditional probability of the label given a particular token.  However, I think Definition 1 makes a very specific assumption about the structure of language that really doesn't hold.  What about the word "not"? We can imagine that "not" is uninformative by the definition here; perhaps it occurs in equal measure with positive and negative labels in the dataset. But "not good" and "not bad" might be meaningful collocations that the encoder has to account for.

I believe this case breaks Lemma 1: the behavior of the model is changed by inserting "not". Moreover, I don't see how Lemma 3 holds for cases like this with a well-trained encoder. (I don't see derivations of either lemma from first principles; these are simply asserted, and I disagree with them as they are asserted).

This state of affairs is worse for pre-trained Transformer models, where the holdover from the pre-training objective makes much of the reasoning about the objective for this particular problem moot anyway, as the encoder's behavior reflects the base language modeling objective as well.

Even setting aside collocations, I am also skeptical of Lemma 3 based on the mathematics of the models themselves. I would believe there are some idealized GRUs that this can be true for, but has this been verified on GRUs for real problems? I think the burden of proof is on the authors to show that there isn't some small latent state update (e.g., counting "uninformative" words) going on at each timestep. Otherwise, if the forget gates are nonzero even for some positions in the latent state vector, this lemma won't hold.

As a result, I don't buy the theoretical results in this paper.

- Setting aside the theory, the core contribution of this paper is pretty minor, largely an engineering one of sharing an encoder. And given that most neural modes are overparameterized, I'm not sure how much sharing an encoder actually constrains the representations that get learned even empirically. The model does seem to work better, possibly due to a multitasking effect, but again, I'm not convinced that it's for the reasons the paper states.

- The models used in this paper are very weak. 200-dimensional GRUs on 100-dimensional GloVe embeddings are simply not representative of the state-of-the-art in 2022 -- RoBERTa/ELECTRA/DeBERTa/etc. encoders should be considered. I think the empirical results about multitask learning of the predictor and generator actually depend on decisions like this, and I could imagine the outcomes being different with these models.

- The paper uses word overlap as an automatic metric but does not do any human evaluation of the produced rationales -- I could imagine that rationales from other methods may disagree with human ground truths but still illustrate the prediction in a meaningful way. I think human evaluation would strengthen the empirical claims.
questions: My main questions are around the theoretical questions above, specifically my objections about Definition 1 / Lemma 1 / Lemma 3. Please clarify these points.
limitations: The conclusion and limitations section is quite vague, with claims like "Existing works have proposed many extra modules to solve the degeneration problem, which demonstrate great effectiveness." The limitations listed here are largely conceptual things that haven't been done yet, as opposed to the (in my opinion quite real) limitations of the present technique: what datasets it can apply to, the assumptions it makes about classification, the limited scope of models considered, etc.
review_title: null

Meta-review: metareview: There is consensus between reviewers that this is a worthwhile paper suitable for this venue.  I also apppreciate the extensive back and forths between the authors and the reviewers that seem to have improved the paper during the reviewing period.
confidence: Certain
Decision: Accept