  State of the art neural methods for open information extraction (OpenIE)
usually extract triplets (or tuples) iteratively in an autoregressive or
predicate-based manner in order not to produce duplicates. In this work, we
propose a different approach to the problem that can be equally or more
successful. Namely, we present a novel single-pass method for OpenIE inspired
by object detection algorithms from computer vision. We use an order-agnostic
loss based on bipartite matching that forces unique predictions and a
Transformer-based encoder-only architecture for sequence labeling. The proposed
approach is faster and shows superior or similar performance in comparison with
state of the art models on standard benchmarks in terms of both quality metrics
and inference time. Our model sets the new state of the art performance of
67.7% F1 on CaRB evaluated as OIE2016 while being 3.35x faster at inference
than previous state of the art. We also evaluate the multilingual version of
our model in the zero-shot setting for two languages and introduce a strategy
for generating synthetic multilingual data to fine-tune the model for each
specific language. In this setting, we show performance improvement 15% on
multilingual Re-OIE2016, reaching 75% F1 for both Portuguese and Spanish
languages. Code and models are available at
https://github.com/sberbank-ai/DetIE.
