Semanlink - [1906.03158] Matching the Blanks: Distributional Similarity for Relation Learning

[1906.03158] Matching the Blanks: Distributional Similarity for Relation Learning

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Au sujet de ce document

sl:arxiv_author :
sl:arxiv_firstAuthor : Livio Baldini Soares
sl:arxiv_num : 1906.03158
sl:arxiv_published : 2019-06-07T15:26:50Z
sl:arxiv_summary : General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph. However, both of these approaches are limited in their ability to generalize. In this paper, we build on extensions of Harris' distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text. We show that these representations significantly outperform previous work on exemplar based relation extraction (FewRel) even without using any of that task's training data. We also show that models initialized with our task agnostic representations, and then tuned on supervised relation extraction datasets, significantly outperform the previous methods on SemEval 2010 Task 8, KBP37, and TACRED.@en
sl:arxiv_title : Matching the Blanks: Distributional Similarity for Relation Learning@en
sl:arxiv_updated : 2019-06-07T15:26:50Z
sl:bookmarkOf : https://arxiv.org/abs/1906.03158
sl:creationDate : 2021-05-13
sl:creationTime : 2021-05-13T00:39:03Z

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Bookmark of: https://arxiv.org/abs/1906.03158

Linked From

[2102.07043] Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

Tags:

> a method for constructing **a virtual KB (VKB) trained entirely from text**

Open Predicate Query Language (OPQL): constructing a virtual knowledge base (VKB) that supports KB reasoning & open-domain QA, tackling the incompleteness of knowledge bases by constructing a virtual KB only from text

> OPQL constructs
a VKB by **encoding and indexing a set of
relation mentions** in a way that naturally enables
reasoning and can be trained without any structured
supervision.

> can be used
as an **external memory integrated into a language
model**

cf. this earlier paper [[2002.10640] Differentiable Reasoning over a Virtual Knowledge Base](doc:2020/07/2002_10640_differentiable_rea). But does not require an initial structured KB for distant
supervision.

> The key idea in constructing the OPQL VKB is to use a
dual-encoder pre-training process, similar to 
[[1906.03158] Matching the Blanks: Distributional Similarity for Relation Learning](doc:2021/05/1906_03158_matching_the_blank)

Related work section refers to [[1909.04164] Knowledge Enhanced Contextual Word Representations](doc:2020/05/1909_04164_knowledge_enhanced). Also refers to [[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](doc:2020/07/2007_00849_facts_as_experts_) (some authors in common)

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DIRT Discovery of inference rules from text (2001)

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> a method for constructing **a virtual KB (VKB) trained entirely from text**

> OPQL constructs
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> can be used
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> We show that it is feasible to perform **entity
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>
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>
> we include an additional
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>
> This combination results in a
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