arXiv Open Access 2018

On learning an interpreted language with recurrent models

Denis Paperno

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Abstrak

Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified datasets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic structure and compositionality. We find LSTM and GRU networks to generalise to compositional interpretation well, but only in the most favorable learning settings, with a well-paced curriculum, extensive training data, and left-to-right (but not right-to-left) composition.

Topik & Kata Kunci

cs.CL

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Denis Paperno

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Paperno, D. (2018). On learning an interpreted language with recurrent models. https://arxiv.org/abs/1809.04128

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Tahun Terbit: 2018
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓