TY - GEN
T1 - VaryLATEX: Learning Paper Variants That Meet Constraints
AU - Acher, Mathieu
AU - Temple, Paul
AU - Jézéquel, Jean-Marc
AU - Galindo Duarte, José Angel
AU - Martinez, Jabier
AU - Ziadi, Tewfik
N1 - Funding Information:
This work benefited from the support of the project ANR-17-CE25-0010-01 VaryVary. This work was supported, in part, by the European Commission (FEDER), by the Spanish government under BELi (TIN2015-70560-R) project, by the Andalusian government under the COPAS (TIC-1867) project and by the ITEA3 15010 REVaMP2 project: FUI the Île-de-France region and BPI in France.
Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/2/7
Y1 - 2018/2/7
N2 - How to submit a research paper, a technical report, a grant proposal, or a curriculum vitae that respect imposed constraints such as formatting instructions and page limits? It is a challenging task, especially when coping with time pressure. In this work, we present VaryL
ATEX, a solution based on variability, constraint programming, and machine learning techniques for documents written in L
ATEX to meet constraints and deliver on time. Users simply have to annotate L
ATEX source files with variability information, e.g., (de)activating portions of text, tuning figures' sizes, or tweaking line spacing. Then, a fully automated procedure learns constraints among Boolean and numerical values for avoiding non-acceptable paper variants, and finally, users can further configure their papers (e.g., aesthetic considerations) or pick a (random) paper variant that meets constraints, e.g., page limits. We describe our implementation and report the results of two experiences with VaryL
ATEX.
AB - How to submit a research paper, a technical report, a grant proposal, or a curriculum vitae that respect imposed constraints such as formatting instructions and page limits? It is a challenging task, especially when coping with time pressure. In this work, we present VaryL
ATEX, a solution based on variability, constraint programming, and machine learning techniques for documents written in L
ATEX to meet constraints and deliver on time. Users simply have to annotate L
ATEX source files with variability information, e.g., (de)activating portions of text, tuning figures' sizes, or tweaking line spacing. Then, a fully automated procedure learns constraints among Boolean and numerical values for avoiding non-acceptable paper variants, and finally, users can further configure their papers (e.g., aesthetic considerations) or pick a (random) paper variant that meets constraints, e.g., page limits. We describe our implementation and report the results of two experiences with VaryL
ATEX.
KW - Constraint programming
KW - Generators
KW - L TEX
KW - Machine learning
KW - Technical writing
KW - Variability modelling
UR - https://hal.inria.fr/hal-01659161
UR - http://www.scopus.com/inward/record.url?scp=85044354894&partnerID=8YFLogxK
U2 - 10.1145/3168365.3168372
DO - 10.1145/3168365.3168372
M3 - Conference contribution
T3 - ACM International Conference Proceeding Series
SP - 83
EP - 88
BT - Proceedings - VaMoS 2018
A2 - Lochau, Malte
A2 - Capilla, Rafael
ER -