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arXiv · 2010.04249

Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks

Ansel MacLaughlin·Jwala Dhamala·Anoop Kumar·Sriram Venkatapathy·Ragav Venkatesan·Rahul Gupta
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Abstract

Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification. In this work, we explore the applicability of a SOTA NAS algorithm, Efficient Neural Architecture Search (ENAS) (Pham et al., 2018) to two sentence pair tasks, paraphrase detection and semantic textual similarity. We use ENAS to perform a micro-level search and learn a task-optimized RNN cell architecture as a drop-in replacement for an LSTM. We explore the effectiveness of ENAS through experiments on three datasets (MRPC, SICK, STS-B), with two different models (ESIM, BiLSTM-Max), and two sets of embeddings (Glove, BERT). In contrast to prior work applying ENAS to NLP tasks, our results are mixed -- we find that ENAS architectures sometimes, but not always, outperform LSTMs and perform similarly to random architecture search.

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Ansel MacLaughlin, Jwala Dhamala, Anoop Kumar, Sriram Venkatapathy, Ragav Venkatesan, Rahul Gupta. 2020-10-08. Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks. https://arxiv.org/abs/2010.04249

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