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- W3202047860 abstract "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." @default.
- W3202047860 created "2021-10-11" @default.
- W3202047860 creator A5074570184 @default.
- W3202047860 date "2021-10-06" @default.
- W3202047860 modified "2023-09-25" @default.
- W3202047860 title "On learning an interpreted language with recurrent models." @default.
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