Extracting Structured Scholarly Information from the Machine Translation Literature

Eunsol Choi, Matic Horvat, Jonathan May, Kevin Knight, Daniel Marcu

Abstract
Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work. We introduce a structured, queryable representation for experimental results and a baseline system that automatically populates this representation. The representation can answer compositional questions such as: “Which are the best published results reported on the NIST 09 Chinese to English dataset?” and “What are the most important methods for speeding up phrase-based decoding?” Answering such questions usually involves lengthy literature surveys. Current machine reading for academic papers does not usually consider the actual experiments, but mostly focuses on understanding abstracts. We describe annotation work to create an initial hscientific paper; experimental results representationi corpus. The corpus is composed of 67 papers which were manually annotated with a structured representation of experimental results by domain experts. Additionally, we present a baseline algorithm that characterizes the difficulty of the inference task.
Anthology ID:
L16-1067
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
421–425
Language:
URL:
https://aclanthology.org/L16-1067
DOI:
Bibkey:
Cite (ACL):
Eunsol Choi, Matic Horvat, Jonathan May, Kevin Knight, and Daniel Marcu. 2016. Extracting Structured Scholarly Information from the Machine Translation Literature. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 421–425, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Extracting Structured Scholarly Information from the Machine Translation Literature (Choi et al., LREC 2016)
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https://preview.aclanthology.org/nschneid-patch-1/L16-1067.pdf
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