What is recorded here?
Adapted byte-pair compression ideas to learn character-sequence merges within words, representing rare and unseen words as reusable subword sequences in neural translation.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The adaptation merges characters or character sequences, not necessarily raw bytes. It inherits Gage's compression algorithm and earlier subword ideas. A finite alphabet and learned representation do not guarantee correct translation of every unseen string.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Rico Sennrich; Barry Haddow; Alexandra Birch
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Character-merge subword units for open-vocabulary neural translation
Adapted byte-pair compression ideas to learn character-sequence merges within words, representing rare and unseen words as reusable subword sequences in neural translation.
Scientific scope
The adaptation merges characters or character sequences, not necessarily raw bytes. It inherits Gage's compression algorithm and earlier subword ideas. A finite alphabet and learned representation do not guarantee correct translation of every unseen string.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Rico Sennrich; Barry Haddow; Alexandra Birch
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.