Venue: AKBC 2019 Conference

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: This work provided a comprehensive review of important works in semantic parsing. It starts with the rule-based systems in the early days. Then it described the introduction of statistical methods to learn from natural language and logical form pairs. Finally, it summarized the recent advances in weakly supervised semantic parsing or learning semantic parsing from denotations and the rise of seq2seq models. It also briefly compared different learning strategies (MML, RL, Max-Margin).  

The paper is well written and easy to follow. The survey covers most of the important works in the field. It provides a good summary of the development of the field as well as the most recent advances. I support the acceptance of this paper. 

Some minor comments: 

"the ATIS domain is : What states border Texas :λx.state(x)borders(x, texas)." 
Is this example from ATIS? It seems more like a GeoQuery example. 

Regarding Reinforcement Learning (section 7.2), there is a recent work (Liang et al, 2018) that is quite relevant. It introduced a principled RL method for semantic parsing, and compared it with other objectives like MML. It also introduced a systematic exploration strategy to address the exploration problem mentioned in this section. Might be worth discussing here. 

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing, Liang, Chen and Norouzi, Mohammad and Berant, Jonathan and Le, Quoc V and Lao, Ni, Advances in Neural Information Processing Systems, 2018
review_title: Nice review of the development and recent advances in semantic parsing 

final_score: rating: 7: Good paper, accept
review_content: Summary:
This paper conducts a thorough survey on semantic parsing, as the title suggested. This paper introduces the formal definition of the semantic parsing, categorized them, describes the development of the system from 1970s to very recent in (fairly) chronological order.

Quality & clarity:
The survey is very thorough and self-contained, and the descriptions are all very clear and well-written.

Originality & significance:
Since it is the survey paper, it’s hard to say it has originality. 

General comment:
Although the survey is very thorough, the paper does not have an original contribution, which conference paper should have.


* Update
I had a misunderstanding about the policy regarding survey paper. I agree with other reviewers that the paper is a well-written survey. Therefore I vote for the acceptance.
review_title: Good survey paper, but no originality

final_score: rating: 7: Good paper, accept
review_content: The survey paper examines the various components of semantic parsing and discusses previous work. The semantic parsing models are categorized into different types according to the supervision forms and the modeling techniques. Overall, the survey is easy to follow and includes sufficient references. The following points can be improved:

* Training a semantic parser involves NL, MR, context, data, model, and learning algorithms. A summarization and examples of popular datasets are helpful.
* Sec 3.1  Rule based systems can be expanded. The current section is too brief.
* Sec 2.1 Language for Meaning Representation and Sec 2.2 Grammar should be merged. 
* P9: "Machine translation techniques" is not a method instead of `` 5. Alternate forms of supervision"
* Sec 8 is too brief now. More discussions on future work are welcome.

minor:
*  Combinatory categorial Grammar ->  Combinatory Categorial Grammar
review_title: easy to follow and includes sufficient references

Meta-review: metareview: This is a very nice survey of the history and current state of semantic parsing.  It does a good job covering a very broad field, hitting the right key points along the way.  If there were one thing I would recommend improving, it would be to try to categorize what the open questions are.  There is a short section on "future work", though it simply provides more references to very recent work.  It would be nice to see more detailed thoughts from the authors thoughts on what is missing and what is next, after having read through this vast literature.

Note to PCs: I don't know what to recommend as far as oral vs. poster.  Neither one seems to fit a survey paper very well.  I'm saying poster assuming that only the top few percent of papers will be oral presentations, and while I think this is a good survey, I don't think it is in the top few percent of papers.
confidence: 5: The area chair is absolutely certain
Decision: Accept (Poster)