Simulink is a leading modelling language and data-flow environment for Model-Driven Engineering, prevalent in both industrial and educational contexts. Accordingly, there are many standalone publicly-available tools for analyzing and using Simulink models for various purposes. However, Simulink's model format has evolved to a new proprietary format, rendering many of these tools useless. To combat this, we devise an approach, SLX2MDL, that applies transformation rules based on Simulink syntax to transform the new SLX format models to models conforming to the legacy MDL syntax. The resulting approach enables backwards compatibility with existing tools, including previous versions of Simulink itself. Our 4-phase process includes analysis and extraction, merging and transformation of the common elements, transformation of the specialized Stateflow elements, and output production. We position this problem within the literature by comparing and contrasting similar, but insufficient, related approaches. We evaluate and validate SLX2MDL by applying it to 543 standard and publicly available models from an established and curated corpus. Our evaluation demonstrates 100% validity and correctness on these models based on functional equivalence. Further, we evaluate our approach's performance and find it consistent and scalable as model size and complexity increases.
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What is Learned in Visually Grounded Neural Syntax Acquisition
Visual features are a promising signal for learning bootstrap textual models. However, blackbox learning models make it difficult to isolate the specific contribution of visual components. In this analysis, we consider the case study of the Visually Grounded Neural Syntax Learner (Shi et al., 2019), a recent approach for learning syntax from a visual training signal. By constructing simplified versions of the model, we isolate the core factors that yield the model’s strong performance. Contrary to what the model might be capable of learning, we find significantly less expressive versions produce similar predictions and perform just as well, or even better. We also find that a simple lexical signal of noun concreteness plays the main role in the model’s predictions as opposed to more complex syntactic reasoning.
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- Award ID(s):
- 1656998
- PAR ID:
- 10197947
- Date Published:
- Journal Name:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Page Range / eLocation ID:
- 2615 to 2635
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2020.acl-main.234
