TY - GEN
T1 - From Examples to Rules
T2 - 13th International Conference on Language Resources and Evaluation Conference, LREC 2022
AU - Vacareanu, Robert
AU - Valenzuela-Escárcega, Marco A.
AU - Barbosa, George C.G.
AU - Sharp, Rebecca
AU - Hahn-Powell, Gus
AU - Surdeanu, Mihai
N1 - Funding Information: This work was supported by NSF grant #2006583. Marco A. Valenzuela-Escárcega, Gus Hahn-Powell, and Mihai Surdeanu declare a financial interest in lum.ai. This interest has been properly disclosed to the University of Arizona Institutional Review Committee and is managed in accordance with its conflict of interest policies. Publisher Copyright: © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
PY - 2022
Y1 - 2022
N2 - While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift. Rule-based methods, on the other hand, can be more easily modified. However, crafting rules requires expertise in linguistics and the domain of interest, making it infeasible for most users. Here we attempt to combine the advantages of these two directions while mitigating their drawbacks. We adapt recent advances from the adjacent field of program synthesis to information extraction, synthesizing rules from provided examples. We use a transformer-based architecture to guide an enumerative search, and show that this reduces the number of steps that need to be explored before a rule is found. Further, we show that without training the synthesis algorithm on the specific domain, our synthesized rules achieve state-of-the-art performance on the 1-shot scenario of a task that focuses on few-shot learning for relation classification, and competitive performance in the 5-shot scenario.
AB - While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift. Rule-based methods, on the other hand, can be more easily modified. However, crafting rules requires expertise in linguistics and the domain of interest, making it infeasible for most users. Here we attempt to combine the advantages of these two directions while mitigating their drawbacks. We adapt recent advances from the adjacent field of program synthesis to information extraction, synthesizing rules from provided examples. We use a transformer-based architecture to guide an enumerative search, and show that this reduces the number of steps that need to be explored before a rule is found. Further, we show that without training the synthesis algorithm on the specific domain, our synthesized rules achieve state-of-the-art performance on the 1-shot scenario of a task that focuses on few-shot learning for relation classification, and competitive performance in the 5-shot scenario.
KW - rule synthesis
KW - rule-based information extraction
UR - http://www.scopus.com/inward/record.url?scp=85144383712&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85144383712&partnerID=8YFLogxK
M3 - Conference contribution
T3 - 2022 Language Resources and Evaluation Conference, LREC 2022
SP - 6180
EP - 6189
BT - 2022 Language Resources and Evaluation Conference, LREC 2022
A2 - Calzolari, Nicoletta
A2 - Bechet, Frederic
A2 - Blache, Philippe
A2 - Choukri, Khalid
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Goggi, Sara
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Mariani, Joseph
A2 - Mazo, Helene
A2 - Odijk, Jan
A2 - Piperidis, Stelios
PB - European Language Resources Association (ELRA)
Y2 - 20 June 2022 through 25 June 2022
ER -