Papers › To be Continuous, or to be Discrete, Those are Bits of Questions
To be Continuous, or to be Discrete, Those are Bits of Questions
Yiran Wang, Masao Utiyama
Recently, binary representation has been proposed as a novel representation that lies between continuous and discrete representations. It exhibits considerable information-preserving capability when being used to replace continuous input vectors. In this paper, we investigate the feasibility of further introducing it to the output side, aiming to allow models to output binary labels instead. To preserve the structural information on the output side along with label information, we extend the previous contrastive hashing method as structured contrastive hashing. More specifically, we upgrade CKY from label-level to bit-level, define a new similarity function with span marginal probabilities, and introduce a novel contrastive loss function with a carefully designed instance selection strategy. Our model achieves competitive performance on various structured prediction tasks, and demonstrates that binary representation can be considered a novel representation that further bridges the gap between the continuous nature of deep learning and the discrete intrinsic property of natural languages.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Constituency Parsing | CTB5 | Hashing + Bert | F1 score | 92.33 | #4 of 9 | Archive leaderboard | report |
| Constituency Parsing | Penn Treebank | Hashing + XLNet | F1 score | 96.43 | #1 of 27 | Archive leaderboard | report |
| Constituency Parsing | Penn Treebank | Hashing + Bert | F1 score | 96.03 | #7 of 27 | Archive leaderboard | report |
| Nested Named Entity Recognition | ACE 2004 | Hashing | F1 | 87.93 | #9 of 24 | Archive leaderboard | report |
| Nested Named Entity Recognition | ACE 2005 | Hashing | F1 | 85.90 | #10 of 25 | Archive leaderboard | report |
| Nested Named Entity Recognition | GENIA | Hashing | F1 | 80.54 | #7 of 26 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections