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Arxiv Doc - Entity Retrieval - Hard negative mining - NLP@Google - Two-Tower neural network - Unsupervised negative mining -

sl:arxiv_author : Daniel Gillick

[1909.10506] Learning Dense Representations for Entity Retrieval

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> We show that it is feasible to perform **entity
linking by training a dual encoder (two-tower)
model that encodes mentions and entities in
the same dense vector space**, where candidate
entities are retrieved by approximate nearest
neighbor search. Unlike prior work, **this setup
does not rely on an alias table followed by a
re-ranker, and is thus the first fully learned entity
retrieval model**.

Contributions:

> -  a dual encoder architecture for
learning entity and mention encodings suitable for
retrieval. A key feature of the architecture is that it
employs a modular **hierarchy of sub-encoders that
capture different aspects of mentions and entities**
> - a simple, fully unsupervised **hard negative
mining** strategy that produces massive gains
in retrieval performance, compared to using only
random negatives
> - high
quality candidate entities very efficiently using approximate nearest neighbor search
> - outperforms discrete retrieval
baselines like an alias table or BM25

> strong retrieval
performance across all 5.7 million Wikipedia entities in
around 3ms per mention

> since we are using a two-tower or dual
encoder architecture, **our model cannot use any kind of attention over
both mentions and entities at once**, nor feature-wise
comparisons as done by Francis-Landau et al. (2016).
This is a fairly severe constraint – for example, **we cannot
directly compare the mention span to the entity title**
– but it permits retrieval with nearest neighbor search
for the entire context against a single, all encompassing
representation for each entity

2021-05-01 About