Semanlink - Arxiv Doc ; Neural networks AND NLP@Facebook

[1904.04458] Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition

Tags:

Knowledge Augmented
Language Model (KALM)

a language
model with access to information available in a
KB, no assumptions
about the availability of additional components
(such as Named Entity Taggers) or annotations

> While classes of
named entities (e.g., person or location) occur frequently,
each individual name (e.g, Atherton or
Zhouzhuang) may be observed infrequently even
in a very large corpus of text. As a result language
models learn to represent accurately only the most
popular named entities

> knowing that Alice is a name
used to refer to a person should give ample information
about the context in which the word may
occur (e.g., Bob visited Alice).

> ---

> extends a traditional **RNN LM**

> we enhance a traditional LM with a
gating mechanism that controls whether a particular
word is modeled as a general word or as a reference
to an entity
>
> We train the model end-to-end
with only the traditional predictive language modeling
perplexity objective
>
> KALM is trained end-to-end using
a predictive objective on large corpus of text.

> To the best of our knowledge, KALM is the first
unsupervised neural NER approach.

> KALM extends a traditional, RNN-based neural
LM.

2022-08-31 About

[2112.09118] Towards Unsupervised Dense Information Retrieval with Contrastive Learning

Tags:

2021-12-21 About

[2004.04906] Dense Passage Retrieval for Open-Domain Question Answering

Tags:

2021-06-03 About

[1901.04085] Passage Re-ranking with BERT

Tags:

2021-03-26 About

[2010.02194] Self-training Improves Pre-training for Natural Language Understanding

Tags:

2021-03-12 About

[2012.04584] Distilling Knowledge from Reader to Retriever for Question Answering

Tags:

> a method to train an information retrieval module for downstream tasks, **without using pairs of queries and documents as annotations**.

Uses two models (standard pipeline for open-domain QA):

- the first one retrieves documents from a large source of knowledge (the retriever)
- the second one processes the support documents to solve the task (the reader).

> First the retriever selects support passages in a large knowledge
source. Then these passages are processed by the reader, along with the question, to generate an
answer

Inspired by knowledge distillation: the reader model is the teacher and the retriever is the student.

> More precisely, we use a sequence-to-sequence model as the reader, and use
the attention activations over the input documents as synthetic labels to train the retriever. 
> (**train the retriever by learning to approximate the attention score of the reader**)

Refers to:

- [REALM: Retrieval-Augmented Language Model Pre-Training](doc:2020/12/2002_08909_realm_retrieval_a)
- [Dehghani: Neural Ranking Models with Weak Supervision](doc:?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1704.08803)

2020-12-11 About

[1911.03814] Scalable Zero-shot Entity Linking with Dense Entity Retrieval

Tags:

2020-05-02 About

[1911.00172] Generalization through Memorization: Nearest Neighbor Language Models

Tags:

2019-12-20 About

[1912.01412] Deep Learning for Symbolic Mathematics

Tags:

2019-12-09 About

[1905.11852] EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction

Tags:

2019-12-05 About

[1911.01464] Emerging Cross-lingual Structure in Pretrained Language Models

Tags:

2019-11-06 About

[1909.01066] Language Models as Knowledge Bases?

Tags:

2019-09-05 About

[1907.05242] Large Memory Layers with Product Keys

Tags:

> **a structured memory which can be easily integrated into a neural network.** The memory is very large by design and therefore significantly increases the capacity of the architecture, by up to a billion parameters with a negligible computational overhead. Its design and access pattern is based on **product keys**, which enable fast and exact nearest neighbor search. The ability to increase the number of parameters while keeping the same computational budget lets the overall system strike a better trade-off between prediction accuracy and computation efficiency both at training and test time.

> a key-value memory layer that can increase model capacity for a negligible computational cost. A 12-layer transformer with a memory outperforms a 24-layer transformer, and is 2x faster!

[Implementation](/doc/2019/08/product_key_memory_pkm_minima)

TODO: compare with [[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](doc:2020/07/2007_00849_facts_as_experts_)

2019-07-13 About

[1503.08895] End-To-End Memory Networks

Tags:

2018-10-23 About

[1712.09405] Advances in Pre-Training Distributed Word Representations

Tags:

2017-12-29 About

[1710.04087] Word Translation Without Parallel Data

Tags:

2017-10-14 About

[1607.01759] Bag of Tricks for Efficient Text Classification

Tags:

2017-09-10 About