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Building multi-modal language models has been a trend in the recent years,where additional modalities such as image, video, speech, etc. are jointlylearned along with natural languages (i.e., textual information). Despite thesuccess of these multi-modal language models with different modalities, thereis no existing solution for neural network architectures and natural languages.Providing neural architectural information as a new modality allows us toprovide fast architecture-2-text and text-2-architecture retrieval/generationservices on the cloud with a single inference. Such solution is valuable interms of helping beginner and intermediate ML users to come up with betterneural architectures or AutoML approaches with a simple text query. In thispaper, we propose ArchBERT, a bi-modal model for joint learning andunderstanding of neural architectures and natural languages, which opens up newavenues for research in this area. We also introduce a pre-training strategynamed Masked Architecture Modeling (MAM) for a more generalized joint learning.Moreover, we introduce and publicly release two new bi-modal datasets fortraining and validating our methods. The ArchBERT's performance is verifiedthrough a set of numerical experiments on different downstream tasks such asarchitecture-oriented reasoning, question answering, and captioning(summarization). Datasets, codes, and demos are available supplementarymaterials.

ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages