Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations

By Diego Marcheggiani, Ivan Titov. 2016

83 citations recorded by the source corpus.

We present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence assumptions. The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities) relying on the predicted relation. The two components are estimated jointly so as to minimize errors in recovering arguments. We study factorization models inspired by previous work in relation factorization and selectional preference modeling. Our models substantially outperform the generative and agglomerative-clustering counterparts and achieve state-of-the-art performance.

Subfields: Artificial Intelligence

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Citations in the corpus

Curriculum-Meta Learning for Order-Robust Continual Relation Extraction (2021, neutral): “2019), and distantly supervised methods (Yao et al. 2011; Marcheggiani and Titov 2016).”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, background): “, 2018), relation discovery (Yao et al., 2011; Marcheggiani and Titov, 2016), relation clustering (Shinyama and Sekine, 2006; Elsahar et al.”
Language as a Latent Variable: Discrete Generative Models for Sentence Compression (2016, background): “While in natural language processing, there are variants of VAEs on modelling documents (Miao et al., 2016), sentences (Bowman et al., 2015) and discovery of relations (Marcheggiani and Titov, 2016).”
Neural Snowball for Few-Shot Relation Learning (2019, background): “Other work exploits the way to form relation types automatically by clustering semantic patterns of given data (Lin and Pantel, 2001; Shinyama and Sekine, 2006; Yao et al., 2011; Marcheggiani and Titov, 2016; ElSahar et al., 2017).”
Quantifying Similarity between Relations with Fact Distribution (2019, background): “…some redundant relations in KBs. Recently, some efforts are devoted to Open Relation Extraction (Open RE) (Lin and Pantel, 2001; Yao et al., 2011; Marcheggiani and Titov, 2016; ElSahar et al., 2017), aiming to cluster relation patterns into several relation types instead of redundant relation…”
Semantic Parsing with Semi-Supervised Sequential Autoencoders (2016, background): “Discrete autoencoders Very recently there has been some related work on discrete autoencoders for natural language processing (Suster et al., 2016; Marcheggiani and Titov, 2016, i.a. ) This work presents a ﬁrst approach to using effectively discretised sequential information as the latent…”
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, background): “In previous work, it is usually assumed that the sentence always expresses a relation between the two entities (Marcheggiani and Titov, 2016).”