Semanlink - Few-shot learning

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 A propos

Advances in few-shot learning: reproducing results in PyTorch

Tags:

2018-12-02 A propos

Advances in few-shot learning: a guided tour – Towards Data Science

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2018-12-02 A propos

[1703.03129] Learning to Remember Rare Events

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2018-10-23 A propos

Propriétés

sl:creationDate : 2018-12-02
sl:creationTime : 2018-12-02T10:17:02Z
rdf:type : sl:Tag
skos:prefLabel : Few-shot learning