Are Neighbors Enough? Multi-Head Neural n-gram can be Alternative to Self-attention

Mengsay Loem; Sho Takase; Masahiro Kaneko; Naoaki Okazaki

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arXiv · 2207.13354

Are Neighbors Enough? Multi-Head Neural n-gram can be Alternative to Self-attention

Mengsay Loem·Sho Takase·Masahiro Kaneko·Naoaki Okazaki

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Abstract

Impressive performance of Transformer has been attributed to self-attention, where dependencies between entire input in a sequence are considered at every position. In this work, we reform the neural $n$-gram model, which focuses on only several surrounding representations of each position, with the multi-head mechanism as in Vaswani et al.(2017). Through experiments on sequence-to-sequence tasks, we show that replacing self-attention in Transformer with multi-head neural $n$-gram can achieve comparable or better performance than Transformer. From various analyses on our proposed method, we find that multi-head neural $n$-gram is complementary to self-attention, and their combinations can further improve performance of vanilla Transformer.

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Mengsay Loem, Sho Takase, Masahiro Kaneko, Naoaki Okazaki. 2022-07-27. Are Neighbors Enough? Multi-Head Neural n-gram can be Alternative to Self-attention. https://arxiv.org/abs/2207.13354

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