Demo Data from EDBT 2026 / reviewers in the wild / expert
Raphael Valenti
dblp:276/6096
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Language models and text generation · 100% | |
| Network and information security
1 paper |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › decoding
large language model decoding |
0.4 | 1 | 2020 | Solving Historical Dictionary Codes with a Neural Language Model · EMNLP (1) 2020 |
Natural language and speech › Language models and text generation
neural language model |
0.4 | 1 | 2020 | Solving Historical Dictionary Codes with a Neural Language Model · EMNLP (1) 2020 |
Methods — techniques the papers use, named apart from their topics
neural language model · 0.9decoding lattice search · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Solving Historical Dictionary Codes with a Neural Language ModelabstractWe solve difficult word-based substitution codes by constructing a decoding lattice and searching that lattice with a neural language model. We apply our method to a set of enciphered letters exchanged between US Army General James Wilkinson and agents of the Spanish Crown in the late 1700s and early 1800s, obtained from the US Library of Congress. We are able to decipher 75.1% of the cipher-word tokens correctly. Christopher Chu, Raphael Valenti, Kevin Knight |
EMNLP (1) | 2 |