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    <abstract>Building multi-modal language models has been a trend in the recent years, where additional modalities such as image, video, speech, etc. are jointly learned along with natural languages (i.e., textual information). Despite the success of these multi-modal language models with different modalities, there is no existing solution for neural network architectures and natural languages. Providing neural architectural information as a new modality allows us to provide fast architecture-2-text and text-2-architecture retrieval/generation services on the cloud with a single inference. Such solution is valuable in terms of helping beginner and intermediate ML users to come up with better neural architectures or AutoML approaches with a simple text query. In this paper, we propose ArchBERT, a bi-modal model for joint learning and understanding of neural architectures and natural languages, which opens up new avenues for research in this area. We also introduce a pre-training strategy named Masked Architecture Modeling (MAM) for a more generalized joint learning. Moreover, we introduce and publicly release two new bi-modal datasets for training and validating our methods. The ArchBERT’s performance is verified through a set of numerical experiments on different downstream tasks such as architecture-oriented reasoning, question answering, and captioning (summarization). Datasets, codes, and demos are available as supplementary materials.</abstract>
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%0 Conference Proceedings
%T ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages
%A Akbari, Mohammad
%A Ranjbar Alvar, Saeed
%A Kamranian, Behnam
%A Banitalebi-Dehkordi, Amin
%A Zhang, Yong
%Y Jiang, Jing
%Y Reitter, David
%Y Deng, Shumin
%S Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F akbari-etal-2023-archbert
%X Building multi-modal language models has been a trend in the recent years, where additional modalities such as image, video, speech, etc. are jointly learned along with natural languages (i.e., textual information). Despite the success of these multi-modal language models with different modalities, there is no existing solution for neural network architectures and natural languages. Providing neural architectural information as a new modality allows us to provide fast architecture-2-text and text-2-architecture retrieval/generation services on the cloud with a single inference. Such solution is valuable in terms of helping beginner and intermediate ML users to come up with better neural architectures or AutoML approaches with a simple text query. In this paper, we propose ArchBERT, a bi-modal model for joint learning and understanding of neural architectures and natural languages, which opens up new avenues for research in this area. We also introduce a pre-training strategy named Masked Architecture Modeling (MAM) for a more generalized joint learning. Moreover, we introduce and publicly release two new bi-modal datasets for training and validating our methods. The ArchBERT’s performance is verified through a set of numerical experiments on different downstream tasks such as architecture-oriented reasoning, question answering, and captioning (summarization). Datasets, codes, and demos are available as supplementary materials.
%R 10.18653/v1/2023.conll-1.7
%U https://aclanthology.org/2023.conll-1.7
%U https://doi.org/10.18653/v1/2023.conll-1.7
%P 87-107

Markdown (Informal)

[ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages](https://aclanthology.org/2023.conll-1.7) (Akbari et al., CoNLL 2023)

• ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages (Akbari et al., CoNLL 2023)

ACL

• Mohammad Akbari, Saeed Ranjbar Alvar, Behnam Kamranian, Amin Banitalebi-Dehkordi, and Yong Zhang. 2023. ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages. In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), pages 87–107, Singapore. Association for Computational Linguistics.