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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 10: AAAI-22 Technical Tracks 10

DetIE: Multilingual Open Information Extraction Inspired by Object Detection

February 1, 2023

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Authors

Michael Vasilkovsky

Skolkovo Institute of Science and Technology, Moscow, Russia Neuromation OU, Tallinn, Estonia


Anton Alekseev

St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences, St. Petersburg, Russia St. Petersburg State University, St. Petersburg, Russia


Valentin Malykh

Huawei Noah’s Ark lab, Moscow, Russia St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences, St. Petersburg, Russia Kazan Federal University, Kazan, Russia ISP RAS Research Center for Trusted Artificial Intelligence, Moscow, Russia


Ilya Shenbin

St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences, St. Petersburg, Russia


Elena Tutubalina

HSE University, Moscow, Russia Kazan Federal University, Kazan, Russia Sber AI, Moscow, Russia


Dmitriy Salikhov

Sber AI, Moscow, Russia


Mikhail Stepnov

Sber AI, Moscow, Russia


Andrey Chertok

Sber AI, Moscow, Russia Artificial Intelligence Research Institute, Moscow, Russia


Sergey Nikolenko

St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences, St. Petersburg, Russia ISP RAS Research Center for Trusted Artificial Intelligence, Moscow, Russia Neuromation OU, Tallinn, Estonia


DOI:

10.1609/aaai.v36i10.21393


Abstract:

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 of 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.

Topics: AAAI

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Michael Vasilkovsky||Anton Alekseev||Valentin Malykh||Ilya Shenbin||Elena Tutubalina||Dmitriy Salikhov||Mikhail Stepnov||Andrey Chertok||Sergey Nikolenko DetIE: Multilingual Open Information Extraction Inspired by Object Detection Proceedings of the AAAI Conference on Artificial Intelligence (2022) 11412-11420.

Michael Vasilkovsky||Anton Alekseev||Valentin Malykh||Ilya Shenbin||Elena Tutubalina||Dmitriy Salikhov||Mikhail Stepnov||Andrey Chertok||Sergey Nikolenko DetIE: Multilingual Open Information Extraction Inspired by Object Detection AAAI 2022, 11412-11420.

Michael Vasilkovsky||Anton Alekseev||Valentin Malykh||Ilya Shenbin||Elena Tutubalina||Dmitriy Salikhov||Mikhail Stepnov||Andrey Chertok||Sergey Nikolenko (2022). DetIE: Multilingual Open Information Extraction Inspired by Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 11412-11420.

Michael Vasilkovsky||Anton Alekseev||Valentin Malykh||Ilya Shenbin||Elena Tutubalina||Dmitriy Salikhov||Mikhail Stepnov||Andrey Chertok||Sergey Nikolenko. DetIE: Multilingual Open Information Extraction Inspired by Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.11412-11420.

Michael Vasilkovsky||Anton Alekseev||Valentin Malykh||Ilya Shenbin||Elena Tutubalina||Dmitriy Salikhov||Mikhail Stepnov||Andrey Chertok||Sergey Nikolenko. 2022. DetIE: Multilingual Open Information Extraction Inspired by Object Detection. "Proceedings of the AAAI Conference on Artificial Intelligence". 11412-11420.

Michael Vasilkovsky||Anton Alekseev||Valentin Malykh||Ilya Shenbin||Elena Tutubalina||Dmitriy Salikhov||Mikhail Stepnov||Andrey Chertok||Sergey Nikolenko. (2022) "DetIE: Multilingual Open Information Extraction Inspired by Object Detection", Proceedings of the AAAI Conference on Artificial Intelligence, p.11412-11420

Michael Vasilkovsky||Anton Alekseev||Valentin Malykh||Ilya Shenbin||Elena Tutubalina||Dmitriy Salikhov||Mikhail Stepnov||Andrey Chertok||Sergey Nikolenko, "DetIE: Multilingual Open Information Extraction Inspired by Object Detection", AAAI, p.11412-11420, 2022.

Michael Vasilkovsky||Anton Alekseev||Valentin Malykh||Ilya Shenbin||Elena Tutubalina||Dmitriy Salikhov||Mikhail Stepnov||Andrey Chertok||Sergey Nikolenko. "DetIE: Multilingual Open Information Extraction Inspired by Object Detection". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.11412-11420.

Michael Vasilkovsky||Anton Alekseev||Valentin Malykh||Ilya Shenbin||Elena Tutubalina||Dmitriy Salikhov||Mikhail Stepnov||Andrey Chertok||Sergey Nikolenko. "DetIE: Multilingual Open Information Extraction Inspired by Object Detection". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 11412-11420.

Michael Vasilkovsky||Anton Alekseev||Valentin Malykh||Ilya Shenbin||Elena Tutubalina||Dmitriy Salikhov||Mikhail Stepnov||Andrey Chertok||Sergey Nikolenko. DetIE: Multilingual Open Information Extraction Inspired by Object Detection. AAAI[Internet]. 2022[cited 2023]; 11412-11420.


ISSN: 2374-3468


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