Exploiting Duality in Open Information Extraction with Predicate Prompt
Zhen Chen Jingping Liu Deqing Yang Yanghua Xiao Huimin Xu Zongyu Wang Rui Xie Yunsen Xian
Open information extraction (OpenIE) aims to extract the schema-free triplets
in the form of (\emph{subject}, \emph{predicate}, \emph{object}) from a given
sentence. Compared with general information extraction (IE), OpenIE poses more
challenges for the IE models, {especially when multiple complicated triplets
exist in a sentence. To extract these complicated triplets more effectively, in
this paper we propose a novel generative OpenIE model, namely \emph{DualOIE},
which achieves a dual task at the same time as extracting some triplets from
the sentence, i.e., converting the triplets into the sentence.} Such dual task
encourages the model to correctly recognize the structure of the given sentence
and thus is helpful to extract all potential triplets from the sentence.
Specifically, DualOIE extracts the triplets in two steps: 1) first extracting a
sequence of all potential predicates, 2) then using the predicate sequence as a
prompt to induce the generation of triplets. Our experiments on two benchmarks
and our dataset constructed from Meituan demonstrate that DualOIE achieves the
best performance among the state-of-the-art baselines. Furthermore, the online
A/B test on Meituan platform shows that 0.93\% improvement of QV-CTR and 0.56\%
improvement of UV-CTR have been obtained when the triplets extracted by DualOIE
were leveraged in Meituan's search system.