Semanlink - Unsupervised machine learning AND NLP@Facebook

elvis sur Twitter : "NEW: Meta AI introduces OPT-IML, a large language model (175B) fine-tuned on 2000 NLP tasks. Uses instruction-tuning to improve zero-shot and few-shot generalization abilities...."

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2022-12-23 About

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

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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

Timo Schick sur Twitter : "PEER, a language model trained to incrementally write texts & collaborate w/ humans ..."

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2022-08-25 About

[2208.03299] Few-shot Learning with Retrieval Augmented Language Model

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2022-08-08 About

Devendra Singh Sachan sur Twitter : "...Unsupervised Passage Re-ranker (UPR), an approach to re-rank retrieved passages for information retrieval tasks."

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2022-04-18 About

Sentence Embedding Fine-tuning for the French Language | by La Javaness R&D | Feb, 2022 | Medium

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2022-03-31 About

Jason Weston sur Twitter : "SeeKeR: An open source search-augmented language model"

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2022-03-25 About

[2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

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2022-01-19 About

[2112.09118] Towards Unsupervised Dense Information Retrieval with Contrastive Learning

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2021-12-21 About

[1906.04980] Unsupervised Question Answering by Cloze Translation

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2021-12-08 About

facebookresearch/UnsupervisedQA: Unsupervised Question answering via Cloze Translation

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2021-12-07 About

Sahajtomar/french_semantic · Hugging Face

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2021-10-14 About

[1901.04085] Passage Re-ranking with BERT

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2021-03-26 About

Hugging Face sur Twitter : "Transformers release of the Retrieval-Augmented Generation model in collaboration with @facebookai!"

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2021-02-23 About

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

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> 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

Guillaume Lample sur Twitter : "Last year, we showed that you can outperform a 24-layer transformer in language modeling with just...

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