It has been of increasing interest in the field to develop automatic machineries to facilitate the design process. In this paper, we focus on assisting graphical user interface (UI) layout design, a crucial task in app development. Given a partial layout, which a designer has entered, our model learns to complete the layout by predicting the remaining UI elements with a correct position and dimension as well as the hierarchical structures. Such automation will significantly ease the effort of UI designers and developers. While we focus on interface layout prediction, our model can be generally applicable for other layout prediction problems that involve tree structures and 2-dimensional placements. Particularly, we design two versions of Transformer-based tree decoders: Pointer and Recursive Transformer, and experiment with these models on a public dataset. We also propose several metrics for measuring the accuracy of tree prediction and ground these metrics in the domain of user experience. These contribute a new task and methods to deep learning research.
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