Venue: ICLR 2021 Conference

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: This paper introduces a library that preprocesses tabular data called Automunge.

For software packages I feel that one of two criteria must be met: The software implements a scientifically novel algorithm, framework, model, etc.; or the software package is so complex that a well-designed implementation in itself is of scientific significance.

Whereas Automunge seems like a useful library, I am not convinced that it falls in either of these two categories. As such, I am not sure it justifies a publication at a machine learning conference. I suggest the authors target a different venue (e.g., PyCon, SysML, etc.) or elaborate on the scientific impact of their software (e.g., show experimentally that this framework allows practitioners to train better performing models).

Pros

* The library seems useful

Cons

* No significant novelty
* Little relevance to the scientific machine learning community
* Not very clearly written
review_title: Useful software but insufficient scientific contribution

final_score: rating: 4: Ok but not good enough - rejection
review_content: The submitted paper describes a very nice featurization library, AutoMunge, that converts NLP into features suitable for NNs.

It's clear that the authors of the library have put a lot of thought into its construction, and it looks very useful.

However, ICLR is about /learning/ representations, not about feature engineering. So this is off-topic for the conference. To make it on-topic, the authors could, e.g., compare using standard word representation techniques vs AutoMunge on a set of NLP tasks using some popular modern NLP architecture (perhaps BERT?). That would be a really interesting paper.

review_title: Very nice featurization library --- not a topic for ICLR

final_score: rating: 6: Marginally above acceptance threshold
review_content: This package aims at automating some repetitive tasks that data analysts (especially within the NLP domain) deal with. I confirm that tasks like feature selection and string encoding are beneficial for applied researchers with textual data. I can see a wide interest in the community.
On the negative side, the paper lacks a literature review and comparison with any existing and relevant work. I expected to see performance plots for different tasks (whether just for this package or with comparison to some baseline alternatives.

As a minor comment, I don't think having "String theory" in the title is a good idea because this keyword is already taken to refer to another scientific topic ( a sub-field of physics).

review_title: The authors present a python package to process data in the form of strings.

Meta-review: 
This paper presents "Automunge" a python library for pre-processing tabular data. 
The authors develop a useful library that can be used by practicioners for data engineering in NNs applications. 
The reviewers raised a common concern regarding the lack of focus on the actual usefulness of the librabry in improving the 
performance of the models that is applied on. A common concern was the lack of performance plots compared to other alternatives. 
In the response the authors have done a rather thorough job of addressing the reviewers comments and
adding material in the supplementary. However, given the current presentation, the manuscript needs a considerable amount of  rewriting to incorporate the suggested changes into the main paper. As it is, I don't think ICLR is the right venue for the manuscript.  It might reach its audience better in venues like SysMl or PyCon also suggested by a reviewer. 

Decision: Reject