Semanlink - Knowledge Graphs in Natural Language Processing @ ACL 2020

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sl:bookmarkOf : https://towardsdatascience.com/knowledge-graphs-in-natural-language-processing-acl-2020-ebb1f0a6e0b1
sl:creationDate : 2020-07-12
sl:creationTime : 2020-07-12T01:12:20Z

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Bookmark of: https://towardsdatascience.com/knowledge-graphs-in-natural-language-processing-acl-2020-ebb1f0a6e0b1

Documents with similar tags (experimental)

Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge (ACL Anthology 2020)

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Aim to leverage both contextual representation of input text (deep LMs)  and  knowledge derived
from curated KBs ([Wordnet](tag:wordnet)) to improve [slot tagging](tag:slot_tagging) in the presence of [out-of-vocab](tag:oov) words ([few-shot scenario](tag:few_shot_learning))

Method:

1. retrieve potentially relevant KB entities and
encode them into distributed representations that
describe global graph-structured information
2. BERT encoder
layer to capture context-aware representations of
the sequence and attend to the KB embeddings
using multi-level graph attention
3. integrate
BERT embeddings and the KB embeddings
to predict the slot type

Contributions:

1. feasibility of applying lexical ontology
to facilitate recognizing OOV words. First to consider the large-scale background
knowledge for enhancing context-aware
slot tagging models.
2. a knowledge integration mechanism that uses multi-level graph
attention to model explicit lexical relations.
3.experiments on two benchmark datasets

> our method makes a notable difference in a
scenario where samples are linguistically diverse,
and large vocab exists.

(Better improvements when using RNN than BERT, because BERT already contains a lot of background knowledge)

2020-07-04 About