Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: Summary: This paper introduces the task of using deep learning for auto-completion in UI design. The basic idea is that given a partially completed tree (representing the design state of the UI), the goal is to predict or "autocomplete" the final tree. The authors propose a transformer-based solution to the task, considering three variants: a vanilla approach where the tree is flattened to a sequence, a pointer-network style approach, and a recursive transformer. Preliminary experiments indicate that the recursive model performs best and that the task is reasonable difficulty.

Assessment: Overall, this is a borderline paper, as the task is interesting and novel, but the presentation is lacking in technical detail and there is a lack of novelty on the modeling side.

In particular, the authors spend a bulk of the paper describing the three different baselines they implement. However, despite the fact that most of the paper is dedicated to the explanation of these baselines. There is not sufficient detail to reproduce the models based on the paper alone. Indeed, without referencing the original Pointer Network and (and especially the) Transformer papers, it would not be possible to understand this paper at all. Further technical background and detail would drastically improve the paper. Moreover, it seems strange that significant space was used to give equations describing simple embedding lookups (i.e., matrix multiplications with one-hot vectors), but the basic technical foundations of Transformers were not adequately explained.  In addition, only the transformer baselines were considered, and it would seem natural to consider LSTM-based baselines, or some other related techniques.  In general, the space that was used to explain the Transformer baselines---which are essentially straightforward ways to adapt transformers to this task---could have been used to give more detail on the dataset. For example, one question is how often a single partial tree has multiple possible completions in the data. 

A major issue---mainly due to the lack of technical details and the lack of promise to provide code/data (unless I missed this)---is that the paper does not appear to be reproducible. Given the intent to have this be a new benchmark, ensuring reproducibility seems critical.

Reasons to accept:
- Interesting new application of GNNs

Reasons to reject:
- Incremental modeling contribution
- Lack of sufficient technical detail on models and dataset
- Does not appear to be reproducible 


review_title: Official Blind Review #2

final_score: rating: 1: Reject
review_content: This paper proposes an autocompletion model for UI layout based on adaptations of Transformers for tree structures and evaluates the models based on a few metrics on a public UI dataset.

I like the area of research the authors are looking into and I think it's an important application. However, the paper doesn't answer key questions about both the application and the models:

1) There is no clear rationale on why we need a new model based on Transformers for this task. What was wrong with LSTMs/GRUs as they've been used extensively for recursive problems including operations on trees? Similarly, I'd have expected baselines that included those models in the evaluation section showing the differences in performance between the newly proposed Transformer model for trees and previously used methods.

2) The evaluation metrics used while borrowed from the language or IR fields doesn't seem to translate to UI design. UI layout is about visual and functional representation of an application so if one is seeking to evaluate different models, they need to relate to those.
review_title: Official Blind Review #1

final_score: rating: 3: Weak Reject
review_content: The paper presents an auto completion for UI layout design. The authors formulate the problem as partial tree completion, and investigate a range of variations of layout decoders based on Transformer.

The paper proposes two models: Pointer and Recursive Transformer. The paper designs three sets of metrics to measure the quality of layout prediction based on the literature and the domain specifics of user interface interaction.

The writing quality is readable. The presentation is nice. The task of auto completion for UI layout design is relatively new.

The paper misses the key baseline in Bayesian optimisation using tree structure [1] which can perform the prediction under the tree-structure dependencies.

[1] Jenatton, Rodolphe, et al. "Bayesian optimization with tree-structured dependencies." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.

NB: the reviewer has low confidence in evaluating this paper.


review_title: Official Blind Review #3

Meta-review: The paper introduces an interesting application of GNNs, but the reviewers find that the contribution is too limited and the motivation is too weak.
Decision: Reject