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: The paper proposes a new framework for energy-based constrained text generation. The proposed method implements text generation as gradient based sampling from an energy-based distribution, derived as a linear composition of the distribution defining each constraint. The method description is clear. Paper is well-written and usually easy to follow, although please see comments for suggested revisions. 

Method is evaluated in three different tasks requiring constrained generation and obtains promising results in comparison to previous approaches. Comparison to related work could be more extensive and through. Overall, an interesting idea that should inspire other research directions in the related field. 
strengths_and_weaknesses: Comments:

- line 17-24: discussion and overview of the problem is introduced too generally, and the examples do not directly connect to the figure. A revision can make more clear how the method is directly applicable to each task. 

- line 25- 27: the discussion on supervised learning is too generic. The proposed method does not necessarily solve the annotation scarcity. There also many applications where it is feasible to adapt / finetune models with domain specific annotations

- line 100: token "next" used unnecessary and confusing. 
questions: Q1. Evaluation of results and comparison to baselines is a bit superficial. Did the authors do any analysis to see the differences in characteristics of the outputs generated by different models?

Q2. Table 2: why are the BLEU results so low?
limitations: adequate
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This work proposes a constrained generation decoding plug-in framework that does not require additional fine-tuning and can be applied to any left-to-right LM. The authors used sampling from the EBM model to incorporate constraints, and an energy function is specified by defining relevant constraint functions. One of the main novelties of the paper is that it suggests Langevin dynamics for gradient-based efficient sampling. That allows applying the framework to various constrained tasks. Experiments on three different tasks show that the proposed method outperforms previous works on constrained generation.
strengths_and_weaknesses: Strengths:
- Unified decoding framework for constrained generation
- The method is well-motivated and described carefully
- Shows solid improvements upon over methods

Weaknesses:
- The analysis of the results lacks some interesting aspects (see questions)
questions: Q1: Did I correctly understand that the constraint function must be handcrafted, and it has a significant impact on the performance? Do you have any suggestions on how to propose a new constrain function?

Q2:  Efficiency of the sampling is claimed in the paper. However, there are no insights on the decoding speed/latency and comparison with other methods, if possible

Q3: Is it possible to incorporate several constraints?

limitations: -
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: Many applications of text generation require incorporating different constraints (hard or soft constraints) to control the semantics or style of generated text. The authors propose a decoding framework named Energy-based Constrained Decoding with Langevin Dynamics, which can achieve hard and soft constraints in a unified framework and does not need any task-specific fine-tuning. Experimental results on three tasks of constrained text generation show the effectiveness of proposed framework.
strengths_and_weaknesses: Strengths
- The authors propose a decoding framework to unify hard and soft constrained generation. The framework treats constrained generation as an energy function and is flexible to add any constraints through arbitrary constraint functions.
- The authors propose a new sampling method to achieve efficient search for a single optimal solution.
- Experiments on three constrained generation tasks show that the proposed framework achieves strong performance on both automatic and human evaluation.

Weaknesses
- Consider that 2,000 iterations are required, the author should give the time consumed by the method to generate the text.
- Considering that GPT2 itself may generate many candidates through top-k sampling, the authors should add a baseline, in which sample-and-select is performed on generated text of GPT2 by top-k sampling.

questions: What is the detail of obtaining a right-to-left LM?
limitations: N/A
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes a generative model for constrained text generation. It incorporates hard and soft constraints in text generation and experiments on three tasks, lexically constrained generation, abducting reasoning, and counterfactual reasoning. The approach formulates generation as sampling from an energy-based model. Those constraints are applied as the weighted components for the score function. They introduced Langevin dynamics for efficient gradient-based sampling. 

strengths_and_weaknesses: Strength
The paper presents a novel approach to conditional/constrained text generation. Langevin dynamics has been explored in machine learning and computer vision but it’s novel to NLP and text generation. The approach can model different kind of constraints, hard or soft, in a unified approach. It is grounded with solid foundation and potential use cases. 
The paper is well written and presented. It’s a fairly complicated topic, but examples and figures made the paper easier to follow. 
The approach is validated on three NLG tasks, and the approach beats prior work like DELOREAN. Weakness 
Although the experiments cover three tasks, the scope of comparison is limited. It mostly compares with one baseline, Left-Only, and one model DeLorean. The author of this paper, DeLorean and NeuroLogic are essentially from the same research group, so I am slightly concerned about the credibility of the experimental results. It’s totally possible to compare with prior work using these 3 datasets. The authors can also find some other tasks like sentiment transfer or attribute grounded generation. The FUDGE paper and the Mix and Match paper are good references to look at.


Some missing reference 
The mix and match paper is directly relevant; the FUDGE paper (and many other discriminator based constrained NLG papers) are also worth mentioning and comparing with. 
FUDGE: Controlled Text Generation With Future Discriminators
https://aclanthology.org/2021.naacl-main.276.pdf
Mix and Match: Learning-free Controllable Text Generation using Energy Language Models
https://aclanthology.org/2022.acl-long.31.pdf


questions: I wonder how fast is the proposed algorithm? 

I am not 100% sure about the "sample-and-select". How many samples do you draw for one example? Any more detail about this sampling procedure?
limitations: I am mostly worried about the limited scope of comparison.
review_title: null

Meta-review: metareview: This paper proposes a framework for controlled or constrained text generation where the constraints are encoded with energy-based models (EBMs). Generation proceeds in two steps: first, the discrete words are relaxed into continuous vectors of logits (scores), which enables the use of gradient methods and Langevin dynamics to obtain a sample. Then, to obtain actual words (a discrete output) a LM is used for top-k filtering and the word with the largest score is chosen. The paper demonstrates the applicability of the proposed framework in three different generation tasks, controlled generation, abductive, and counterfactual generation. 

All reviewers agree (and I agree with them) that this is a solid paper which brings Langevin dynamics (a well-known technique mainly used with continuous outputs, e.g. in vision tasks) to text generation. Although the use of this technique with EBMs is certainly not novel, making it work for text is non-trivial. The reviewers suggest several improvements: reporting the runtimes (the proposed method requires many gradient iterations which brings a considerable slow down), comparing against more baselines more explicit, and adding references and discussion related for recent work such as FUDGE and mix-and-match. The author response was satisfactory and promised to add these details to the final version. 

I strongly encourage the authors to incorporate the reviewer's suggestions in their final version.


confidence: Certain
Decision: Accept