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Subword Encoding in Lattice LSTM for Chinese Word Segmentation Jie Yang, Yue Zhang, Shuailong LiangarXiv:1810.12594v1 [cs.CL] 30 Oct 2018Singapore University of Technology and Design jieynlp@gmail.com,
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01
Tokenize the input sentence into subword units using a tokenizer like BPE or WordPiece.
02
Convert the subword units into their corresponding numerical IDs using a pre-trained vocabulary.
03
Construct a lattice by creating paths connecting the subword units based on their order in the input sentence.
04
Fill out the lattice by assigning scores to each path based on the likelihood of the subword units occurring together.

Who needs subword encoding in lattice?

01
NLP researchers and practitioners who work with languages that have complex morphology and rich word formation processes.
02
Machine translation systems that need to handle rare or out-of-vocabulary words efficiently.
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Speech recognition systems that benefit from subword units to improve the accuracy of transcription.
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Subword encoding in lattice refers to a method of breaking down words into smaller units or subwords to improve the efficiency and accuracy of language models, particularly in natural language processing.
Individuals or organizations engaged in language processing tasks that utilize subword encoding techniques are typically required to file subword encoding in lattice.
To fill out subword encoding in lattice, one must follow the specific guidelines provided by the framework, which usually includes defining a vocabulary of subwords, providing encoding rules, and submitting the encoded data in the prescribed format.
The purpose of subword encoding in lattice is to enhance the model's ability to understand and generate language by efficiently representing words, capturing morphological variations, and reducing the vocabulary size.
Information reported on subword encoding in lattice typically includes the defined subword vocabulary, encoding rules, and any relevant data pertaining to how the encoding is applied in language models.
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