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

• URL: http://arxiv.org/abs/2010.04249v1
• Date: Thu, 8 Oct 2020 20:26:34 GMT
• Title: Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks
• Authors: Ansel MacLaughlin, Jwala Dhamala, Anoop Kumar, Sriram Venkatapathy, Ragav Venkatesan, Rahul Gupta
• Abstract summary: Neural Architecture Search (NAS) methods have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks. In this work, we explore the applicability of a SOTA NAS algorithm, Efficient Neural Architecture Search (ENAS) to two sentence pair tasks.
• Score: 14.963150544536203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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