Semanlink - [1802.05930] Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing

[1802.05930] Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing

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sl:arxiv_author :
sl:arxiv_firstAuthor : K M Annervaz
sl:arxiv_num : 1802.05930
sl:arxiv_published : 2018-02-16T13:38:00Z
sl:arxiv_summary : Machine Learning has been the quintessential solution for many AI problems, but learning is still heavily dependent on the specific training data. Some learning models can be incorporated with a prior knowledge in the Bayesian set up, but these learning models do not have the ability to access any organised world knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. Our aim is to develop a deep learning model that can extract relevant prior support facts from knowledge graphs depending on the task using attention mechanism. We introduce a convolution-based model for learning representations of knowledge graph entity and relation clusters in order to reduce the attention space. We show that the proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. Using this method we show significant improvement in performance for text classification with News20, DBPedia datasets and natural language inference with Stanford Natural Language Inference (SNLI) dataset. We also demonstrate that a deep learning model can be trained well with substantially less amount of labeled training data, when it has access to organised world knowledge in the form of knowledge graph.@en
sl:arxiv_title : Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing@en
sl:arxiv_updated : 2018-05-21T03:44:48Z
sl:bookmarkOf : https://arxiv.org/abs/1802.05930
sl:creationDate : 2020-10-02
sl:creationTime : 2020-10-02T01:01:15Z

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Bookmark of: https://arxiv.org/abs/1802.05930

Documents with similar tags (experimental)

[1911.03681] E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT

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> way of **injecting factual knowledge about entities into the pretrained BERT model**.

(Feeding entity vectors
into BERT as if they
were wordpiece vectors without additional encoder
pretraining)

>
> **We align [Wikipedia2Vec](tag:wikipedia2vec) entity vectors (Yamada et al., 2016) with BERT's native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors**. The resulting entity-enhanced version of BERT (called E-BERT) is similar in spirit to [ERNIE](tag:ernie) (Zhang et al., 2019) and [KnowBert](tag:knowbert) (Peters et al., 2019), but it **requires no expensive further pretraining of the BERT encoder**.
>
> Our vector space alignment strategy is inspired by
cross-lingual word vector alignment

Related work on Entity-enhanced BERT:

> ([ERNIE](doc:2019/08/_1905_07129_ernie_enhanced_la) and [Knowbert](doc:2020/05/1909_04164_knowledge_enhanced)) are based on the design principle
that BERT be adapted to entity vectors. They introduce
new encoder layers to feed pretrained entity
vectors into the Transformer, and they require additional
pretraining to integrate the new parameters.
In contrast, E-BERT’s design principle is that entity
vectors be adapted to BERT.
>
> Two other knowledge-enhanced MLMs are [KEPLER](doc:2020/11/1911_06136_kepler_a_unified_)
(Wang et al., 2019c) and K-Adapter (Wang
et al., 2020)... Their factual knowledge
does not stem from entity vectors – instead, they
are trained in a multi-task setting on relation classification
and knowledge base completion.

Not to be cofounded with [[2009.02835] E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce](doc:2020/12/2009_02835_e_bert_a_phrase_a)

2021-01-12 A propos

[1911.06136] KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation

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