arXiv
Open Access
2020
Discrete Word Embedding for Logical Natural Language Understanding
Masataro Asai
Zilu Tang
Abstrak
We propose an unsupervised neural model for learning a discrete embedding of words. Unlike existing discrete embeddings, our binary embedding supports vector arithmetic operations similar to continuous embeddings. Our embedding represents each word as a set of propositional statements describing a transition rule in classical/STRIPS planning formalism. This makes the embedding directly compatible with symbolic, state of the art classical planning solvers.
Penulis (2)
M
Masataro Asai
Z
Zilu Tang
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2020
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓