Recursive Hetero-associative Memories for Translation

نویسندگان

  • Mikel L. Forcada
  • Ramón P. Ñeco
چکیده

This paper presents a modiication of Pollack's RAAM (Re-cursive Auto-Associative Memory), called a Recursive Hetero-Associative Memory (RHAM), and shows that it is capable of learning simple translation tasks, by building a state-space representation of each input string and unfolding it to obtain the corresponding output string. RHAM-based translators are computationally more powerful and easier to train than their corresponding double-RAAM counterparts in the literature.

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تاریخ انتشار 1997