Abstract
In this paper, we present a feature fusion decoder for argument extraction in Open Information Extraction (Open IE), where we challenge argument extraction as a predicate-dependent task. Therefore, we create a predicate-specific embedding layer to allow the argument extraction module fully shares the predicate information and the contextualized information of the given sentence, after using a pre-trained BERT model to achieve the predicates. After that, we propose a decoder in argument extraction that leverages both token features and span features to extract arguments with two steps as argument boundary identification by token features and argument role labeling by span features. Experimental results show that the proposed decoder significantly enhances the extraction performance. Our approach establishes a new state-of-the-art result on two benchmarks as OIE2016 and Re-OIE2016.
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Notes
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The only difference is the confidence score for training data chosen by different baselines, please check Sect. 3.1 for details.
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Note that results reported in [15] contradicts our results. That is because the author changed the matching function of evaluation scripts. While this changes the absolute performance numbers of the different systems, it does not change the relative performance of any of the tested systems.
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Li, Y., Yang, Y., Hu, Q., Chen, C., He, L. (2021). An Argument Extraction Decoder in Open Information Extraction. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_21
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