Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction

• URL: http://arxiv.org/abs/2110.08144v1
• Date: Fri, 15 Oct 2021 15:19:11 GMT
• Title: Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction
• Authors: Bhushan Kotnis, Kiril Gashteovski, Carolin Lawrence, Daniel O\~noro Rubio, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert
• Abstract summary: We propose a neural multilingual OpenIE system that iteratively extracts triples by conditioning extractions on different elements of the triple. MiLIE outperforms SOTA systems on multiple languages ranging from Chinese to Galician thanks to it's ability of combining multiple extraction pathways.
• Score: 11.344977846840747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we investigate a simple hypothesis for the Open Information Extraction (OpenIE) task, that it may be easier to extract some elements of an triple if the extraction is conditioned on prior extractions which may be easier to extract. We successfully exploit this and propose a neural multilingual OpenIE system that iteratively extracts triples by conditioning extractions on different elements of the triple leading to a rich set of extractions. The iterative nature of MiLIE also allows for seamlessly integrating rule based extraction systems with a neural end-to-end system leading to improved performance. MiLIE outperforms SOTA systems on multiple languages ranging from Chinese to Galician thanks to it's ability of combining multiple extraction pathways. Our analysis confirms that it is indeed true that certain elements of an extraction are easier to extract than others. Finally, we introduce OpenIE evaluation datasets for two low resource languages namely Japanese and Galician.

Related papers

• Extracting and Transferring Abilities For Building Multi-lingual Ability-enhanced Large Language Models [104.96990850774566]
  We propose a Multi-lingual Ability Extraction and Transfer approach, named as MAET. Our key idea is to decompose and extract language-agnostic ability-related weights from large language models. Experiment results show MAET can effectively and efficiently extract and transfer the advanced abilities, and outperform training-based baseline methods.
  arXiv  Detail & Related papers  (2024-10-10T11:23:18Z)
• Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction [44.716502690026026]
  Relation triple extraction, which outputs a set of triples from long sentences, plays a vital role in knowledge acquisition. Large language models can accurately extract triples from simple sentences through few-shot learning or fine-tuning when given appropriate instructions. In this paper, we design an evaluation-filtering framework that integrates large language models with small models for relational triple extraction tasks.
  arXiv  Detail & Related papers  (2024-04-15T09:03:05Z)
• Adaptive Ordered Information Extraction with Deep Reinforcement Learning [19.76901412962578]
  This paper proposes a novel adaptive ordered IE paradigm to find the optimal element extraction order for different instances. We also propose an reinforcement learning based framework to generate optimal extraction order for each instance dynamically.
  arXiv  Detail & Related papers  (2023-06-19T08:58:56Z)
• Enriching Relation Extraction with OpenIE [70.52564277675056]
  Relation extraction (RE) is a sub-discipline of information extraction (IE) In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models.
  arXiv  Detail & Related papers  (2022-12-19T11:26:23Z)
• Towards Relation Extraction From Speech [56.36416922396724]
  We propose a new listening information extraction task, i.e., speech relation extraction. We construct the training dataset for speech relation extraction via text-to-speech systems, and we construct the testing dataset via crowd-sourcing with native English speakers. We conduct comprehensive experiments to distinguish the challenges in speech relation extraction, which may shed light on future explorations.
  arXiv  Detail & Related papers  (2022-10-17T05:53:49Z)
• UniREx: A Unified Learning Framework for Language Model Rationale Extraction [30.39545674859148]
  We propose UniREx, a unified and highly flexible learning framework for rationale extraction. UniREx enables end-to-end customization of the rationale extractor training process. Our best UniREx configurations achieve a superior balance of the five desiderata.
  arXiv  Detail & Related papers  (2021-12-16T11:39:21Z)
• A Simple but Effective Bidirectional Extraction Framework for Relational Triple Extraction [0.9926500244448218]
  Tagging based relational triple extraction methods are attracting growing research attention recently. Most of these methods take a unidirectional extraction framework that first extracts all subjects and then extracts objects and relations simultaneously based on the subjects extracted. This framework has an obvious deficiency that it is too sensitive to the extraction results of subjects. We propose a bidirectional extraction framework based method that extracts triples based on the entity pairs extracted from two complementary directions.
  arXiv  Detail & Related papers  (2021-12-09T14:17:33Z)
• Contrastive Triple Extraction with Generative Transformer [72.21467482853232]
  We introduce a novel model, contrastive triple extraction with a generative transformer. Specifically, we introduce a single shared transformer module for encoder-decoder-based generation. To generate faithful results, we propose a novel triplet contrastive training object.
  arXiv  Detail & Related papers  (2020-09-14T05:29:24Z)
• IMoJIE: Iterative Memory-Based Joint Open Information Extraction [37.487044478970965]
  We present IMoJIE, an extension to CopyAttention, which produces the next extraction conditioned on all previously extracteds. IMoJIE outperforms CopyAttention by about 18 F1 pts, and a BERT-based strong baseline by 2 F1 pts.
  arXiv  Detail & Related papers  (2020-05-17T07:04:08Z)
• At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization [110.54963847339775]
  We show that unnecessity and redundancy issues exist when extracting full sentences. We propose extracting sub-sentential units based on the constituency parsing tree.
  arXiv  Detail & Related papers  (2020-04-06T13:35:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.