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
this https URL

本文提出了一种基于计算机视觉中的物体检测算法启发的一种新型单遍 OpenIE 方法，并使用基于二分图匹配的无序损失和基于 Transformer 的编码器体系结构进行序列标记。 该方法更快速并在标准基准测试中表现出优异的性能。我们还在两种语言的零 - shot 设置中对其进行了多语言版本的评估，并在多语言 Re-OIE2016 上展示了性能提高 15％，使两种语言的 F1 值均达到 75％以上。