Semanlink - Favoris AND AI@Google

> **Part of what makes LoRA so effective is that ... it’s stackable.**
>
> By contrast, training giant models from scratch not only
throws away the pretraining, but also any iterative
improvements that have been made on top.

> LoRA updates are very cheap to produce (~$100) for the
most popular model sizes.

> Many of these projects are saving time by training on
small, highly curated datasets...
> These
datasets are built using synthetic methods (e.g. filtering
the best responses from an existing model) and
scavenging from other projects

> Directly Competing With Open Source
Is a Losing Proposition

> Paradoxically, the one clear winner in all of this is Meta.
Because the leaked model was theirs ([LLaMA](tag:llama)), they have
effectively garnered an entire planet's worth of free labor.
Since most open source innovation is happening on top of
their architecture, there is nothing stopping them from
directly incorporating it into their products.

2023-05-04 A propos

Aran Komatsuzaki sur Twitter : "JaxPruner: A concise library for sparsity research An open-source JAX-based pruning and sparse training library for machine learning research repo"

Tags:

2023-04-28 A propos

[2303.16839] MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks

Tags:

2023-04-25 A propos

Domain Adaptation with Generative Pseudo-Labeling (GPL) | Pinecone

Tags:

2023-04-09 A propos

Classifying long textual documents (up to 25 000 tokens) using BERT | by Sinequa | (2020)

Tags:

2023-04-07 A propos

Diffusion language models – Sander Dieleman

Tags:

2023-04-06 A propos

Daniel Vila Suero sur Twitter : "Data quality is key for LLMs, but we're building Open Source LLMs with data of "unknown" quality... Introducing Alpaca GarbageCollector..."

Tags:

2023-04-05 A propos

[2304.01982] Rethinking the Role of Token Retrieval in Multi-Vector Retrieval

Tags:

2023-04-05 A propos

[2112.05682] Self-attention Does Not Need O(n^2) Memory

Tags:

2023-02-27 A propos

[2108.08877] Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

Tags:

2023-02-17 A propos

Maarten Grootendorst sur Twitter : "The v0.14 release of BERTopic is here. Fine-tune your topic keywords and labels with models from @OpenAI, @huggingface, @CohereAI, @spacy_io, and @LangChainAI... An overview thread"

Tags:

2023-02-15 A propos

Guiding Frozen Language Models with Learned Soft Prompts – Google AI Blog

Tags:

2023-02-14 A propos

[2203.14465] STaR: Bootstrapping Reasoning With Reasoning

Tags:

2023-02-07 A propos

Google announces ChatGPT rival Bard, with wider availability in ‘coming weeks’ - The Verge

Tags:

2023-02-07 A propos

Ramsri Goutham Golla sur Twitter : "The most practical open-source competitor to @OpenAI 's GPT-3 is Google's Flan-T5 Here are 5 Flan-T5 resources to try out easily, deploy, or fine-tune it! 🧵" / Twitter

Tags:

2023-02-04 A propos

The Flan Collection: Advancing open source methods for instruction tuning – Google AI Blog

Tags:

2023-02-02 A propos

Shayne Longpre sur Twitter : "What’s the best completely public competitor to #ChatGPT? Flan-T5 beats all public models we tested..."

Tags:

2023-02-01 A propos

LaMDA: our breakthrough conversation technology

Tags:

Google LaMDA

2023-01-28 A propos

An empirical analysis of compute-optimal large language model training

Tags:

2023-01-26 A propos

Characterizing Emergent Phenomena in Large Language Models – Google AI Blog

Tags:

2023-01-26 A propos

[2301.08210] Everything is Connected: Graph Neural Networks

Tags:

2023-01-21 A propos

Multilingual Sentence Transformers | Pinecone

Tags:

2023-01-13 A propos

Andrej Karpathy sur Twitter : "Great post (5mo ago) "chinchilla's wild implications" giving context to LLM goldrush shifting from model size to dataset size..."

Tags:

2023-01-05 A propos

ValueError "invalid literal for int() with base 10" in trainer.evaluate (dataset created from pandas) · Issue #228 · huggingface/setfit

Tags:

2022-12-13 A propos

Few-Shot Text Classification (Cloudera 2020)

Tags:

2022-11-24 A propos

[2202.06991] Transformer Memory as a Differentiable Search Index

Tags:

2022-10-25 A propos

Tutorial on Uncertainty Estimation for NLP

Tags:

2022-10-18 A propos

Stephanie Chan sur Twitter : "Transformer inductive biases..."

Tags:

2022-10-14 A propos

Lewis Tunstall sur Twitter : "The SetFit library for few-shot learning with Sentence Transformers now supports *multi-label text classification*..."

Tags:

2022-10-14 A propos

huggingface/setfit: Efficient few-shot learning with Sentence Transformers

Tags:

2022-10-12 A propos

MaartenGr/KeyBERT: Minimal keyword extraction with BERT

Tags:

2022-10-06 A propos

Yi Tay sur Twitter : "Don't retrieve, recite!..."

Tags:

2022-10-06 A propos

[2205.11498] Domain Adaptation for Memory-Efficient Dense Retrieval

Tags:

2022-09-26 A propos

[2209.11055] Efficient Few-Shot Learning Without Prompts

Tags:

2022-09-23 A propos

PromptBERT improving BERT sentence embeddings with prompts - Ethan Kim

Tags:

BERT + Sentence Embeddings

2022-09-16 A propos

[2201.04337] PromptBERT: Improving BERT Sentence Embeddings with Prompts

Tags:

2022-09-16 A propos

Prompt Tuning BERT🎯:CommonLit Readability | Kaggle

Tags:

2022-09-16 A propos

Active Learning for BERT: An Empirical Study - ACL Anthology

Tags:

2022-09-02 A propos

[2106.10199] BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

Tags:

2022-09-01 A propos

On Stability of Few-Sample Transformer Fine-Tuning | Kaggle

Tags:

2022-08-29 A propos

Unsupervised Learning — Sentence-Transformers documentation

Tags:

2022-08-20 A propos

Train and Fine-Tune Sentence Transformers Models

Tags:

Sentence-BERT

2022-08-13 A propos

[2205.00820] Entity-aware Transformers for Entity Search

Tags:

2022-07-12 A propos

[2206.10658] Questions Are All You Need to Train a Dense Passage Retriever

Tags:

> **approach for training dense retrieval models that does not require any labeled training data**. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negative mining and denoising of positive examples.
>
> ART, in contrast, only requires access to unpaired inputs and outputs (e.g. questions and potential answer documents).
>
> It uses a new document-retrieval autoencoding scheme, where
> 1. an input question is used to retrieve a set of evidence documents, and
> 2. the documents are then used to compute the probability of reconstructing the original question.
>
> Training for retrieval based on question reconstruction enables effective unsupervised learning of both document and question encoders, which can be later incorporated into complete Open QA systems without any further finetuning.

[Tweet](doc:2022/07/devendra_singh_sachan_sur_twitt)

> Given an
input question, ART first retrieves a small set
of possible evidences documents. It then recon
structs
the original question by attending to these
documents
>
> The
key idea in ART is to consider the retrieved documents
as a noisy representation of the original
question and question reconstruction probability
as a way of denoising that provides soft-labels for
how likely each document is to have been the correct
result

Refers to [[IZACARD 2012.04584] Distilling Knowledge from Reader to Retriever for Question Answering](doc:2020/12/2012_04584_distilling_knowled)

2022-07-06 A propos

Unveiling Transformers with LEGO - YouTube

Tags:

2022-06-30 A propos

Using BERT For Classifying Documents with Long Texts | by Armand Olivares | Medium

Tags:

2022-06-29 A propos

Chris Olah sur Twitter : "I'm excited to *finally* be making progress on understanding the first MLP layer in large transformer LMs. I've tried really hard and prior to SoLU had little success." / Twitter

Tags:

2022-06-27 A propos

sentence bert model in onnx format · Issue #46 · UKPLab/sentence-transformers

Tags:

2022-06-13 A propos

[2205.15952] Knowledge Graph -- Deep Learning: A Case Study in Question Answering in Aviation Safety Domain

Tags:

2022-06-11 A propos

Domain transfer with GGPL: German Generative Pseudo Labeling 🥨 | by Matthias Richter | Jun, 2022 | ML6team

Tags:

2022-06-02 A propos

Nils Reimers sur Twitter : "GPL goes multi-lingual..."

Tags:

2022-06-01 A propos

[2205.08184] SKILL: Structured Knowledge Infusion for Large Language Models

Tags:

2022-05-18 A propos

[2205.05131] Unifying Language Learning Paradigms

Tags:

2022-05-12 A propos

BERTopic: The Future of Topic Modeling | Pinecone

Tags:

2022-05-12 A propos

[2205.04260] EASE: Entity-Aware Contrastive Learning of Sentence Embedding

Tags:

2022-05-11 A propos

[2203.08913] Memorizing Transformers

Tags:

2022-05-07 A propos

Ramsri Goutham Golla sur Twitter : "Hi @Nils_Reimers For GPL you used "msmarco-distilbert-base-tas-b" model and ..."

Tags:

2022-04-27 A propos

[1909.00426] Global Entity Disambiguation with BERT

Tags:

2022-04-18 A propos

[2110.08151] mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models

Tags:

[Ikuya Yamada sur Twitter : "Is entity representation effective to improve multilingual language models?..."](doc:2022/04/ikuya_yamada_sur_twitter_is_)

> Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities. However, **existing methods only exploit entity information in pretraining and do not explicitly use entities in downstream tasks**. In this study, we explore the **effectiveness of leveraging entity representations for downstream cross-lingual tasks**.
>
> the key insight is that incorporating entity representations into the input allows us to extract more language-agnostic features.

[Github](https://github.com/studio-ousia/luke)

> Entity representations are known to enhance
language models in mono-lingual settings
(Zhang et al., 2019: [ERNIE](tag:ernie.html); Peters et al., 2019:  [[1909.04164] Knowledge Enhanced Contextual Word Representations](doc:2020/05/1909_04164_knowledge_enhanced); Wang et al.,
2021 [[1911.06136] KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](doc:2020/11/1911_06136_kepler_a_unified_); Xiong et al., 2020; Yamada et al., 2020: [[2010.01057] LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](doc:2020/11/2010_01057_luke_deep_context))
presumably by introducing real-world knowledge.
We show that using entity representations facilitates
cross-lingual transfer by providing languageindependent
features.
>
> Multilingual extension of LUKE. The model is trained with the multilingual
masked language modeling (MLM) task as well
as the masked entity prediction (MEP) task with
Wikipedia entity embeddings

> We investigate two ways of using the entity representations
in cross-lingual transfer tasks:
> 1. perform
entity linking for the input text, and append
the detected entity tokens to the input sequence.
The entity tokens are expected to provide language independent
features to the model
> 2. use the entity
[MASK] token from the MEP task as a languageindependent
feature extractor.

2022-04-17 A propos

Ikuya Yamada sur Twitter : "Is entity representation effective to improve multilingual language models?..."

Tags:

2022-04-13 A propos

Tu Vu sur Twitter : "Enormous LMs like GPT-3 exhibit impressive few-shot performance, but w/ self-training a BERT base sized model can achieve much better results!

Tags:

2022-04-13 A propos

Google AI Blog: Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance

Tags:

2022-04-05 A propos

[2008.11228] A simple method for domain adaptation of sentence embeddings

Tags:

2022-04-01 A propos

[2004.05119] Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer

Tags:

2022-03-31 A propos

Sentence Transformer Fine-Tuning (SetFit): Outperforming GPT-3 on few-shot Text-Classification while being 1600 times smaller | by Moshe Wasserblat (2021-12)

Tags:

2022-03-31 A propos

Nils Reimers sur Twitter : "Great post on SetFit"

Tags:

2022-03-31 A propos

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

Tags:

2022-03-31 A propos

Domain Adaptation — Sentence-Transformers documentation

Tags:

2022-03-31 A propos

[2203.14655] Few-Shot Learning with Siamese Networks and Label Tuning

Tags:

> the problem of building text classifiers with little or no training data.
>
> In recent years, an approach based on neural textual entailment models has been found to give strong results on a diverse range of tasks.

(cf. #[NLI](tag:nli), using the input text as the premise and the text representing the label as the hypothesis)

> In this work, we show that **with proper pre-training, Siamese Networks that embed texts and labels** offer a competitive alternative.
>
> We introduce **label tuning: fine-tuning the label embeddings only**. While giving lower performance than model fine-tuning (which updates all params of the model), this approach has the architectural advantage that a single encoder can be shared by many different tasks (we only fine-tune the label embeddings)
> The drop in quality can
be compensated by using a variant of **[Knowledge distillation](tag:knowledge_distillation)**

[Github](https://tinyurl.com/label-tuning), [Tweet](doc:2022/03/thomas_muller_sur_twitter_pa)

2022-03-30 A propos

[2006.05987] Revisiting Few-sample BERT Fine-tuning

Tags:

2022-03-21 A propos

NLP | How to add a domain-specific vocabulary (new tokens) to a subword tokenizer already trained like BERT WordPiece | by Pierre Guillou | Medium

Tags:

2022-03-18 A propos

Studio Ousia sur Twitter : "Now using LUKE is easier than ever!" / Twitter

Tags:

2022-03-15 A propos

Andrew Trask about large language models: The "bigness" is a temporary flaw, not a permanent feature of progress"

Tags:

2022-03-13 A propos

MaartenGr/BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.

Tags:

2022-03-10 A propos

Document Matching for Job Descriptions | Semantic Scholar (2021)

Tags:

2022-03-09 A propos

NAVER LABS Europe : "@Nils_Reimers of @huggingface on 'Unsupervised domain adaptation for neural search'"

Tags:

2022-03-09 A propos

[2109.06304] Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration

Tags:

2022-02-25 A propos

Nils Reimers sur Twitter : "Creating intent classes for chatbots is challenging This tutorial shows how to use sentence-transformers to find potentially overlapping intent classes and how to improve your data annotation work." / Twitter

Tags:

2022-02-19 A propos

Nils Reimers sur Twitter : "how to use the fast clustering algorithm from sentence-transformers..."

Tags:

2022-02-19 A propos

sentence-transformers/fast_clustering.py at master · UKPLab/sentence-transformers

Tags:

2022-02-18 A propos

gsarti/scibert-nli · Hugging Face

Tags:

2022-01-29 A propos

Semantic Search — Sentence-Transformers documentation

Tags:

2022-01-29 A propos

[1906.00300] Latent Retrieval for Weakly Supervised Open Domain Question Answering

Tags:

2022-01-11 A propos

Domain Transfer with BERT | Pinecone

Tags:

2022-01-04 A propos

Anthropic sur Twitter : "a mathematical framework for trying to reverse engineer transformer language models..."

Tags:

2021-12-23 A propos

Making the Most of Data: Augmentation with BERT | Pinecone

Tags:

2021-12-18 A propos

Using pretrained SBERT model in cross-encoder · Issue #726 · UKPLab/sentence-transformers

Tags:

2021-12-17 A propos

Advance BERT model via transferring knowledge from Cross-Encoders to Bi-Encoders | by Chien Vu | Towards Data Science

Tags:

2021-12-17 A propos

[2112.07577] GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Tags:

An unsupervised domain adaptation technique for dense retrieval models

1. synthetic queries
are generated for each passage from the target corpus (using an existing pre-trained [T5](tag:text_to_text_transfer_transformer)
encoder-decoder)
2. the generated queries are used for mining negative
passages (retrieving the most similar
paragraphs using an existing dense retrieval
model == hard negatives!)
3. the query-passage pairs are labeled by a cross-encoder and used to train the domain-adapted
dense retriever (using method described in [Hofstätter et al.,
2020](doc:2021/12/2010_02666_improving_efficien))

[Nils Reimers sur Twitter](doc:2021/12/nils_reimers_sur_twitter_do_), [GitHub](https://github.com/UKPLab/gpl),  by the author of [TSDAE](doc:2021/09/2104_06979_tsdae_using_trans)

Claims to improve "Doc2Query" [Document Expansion by Query Prediction](doc:2022/01/1904_08375_document_expansion): ([src](https://twitter.com/KexinWang2049/status/1471435779415150598))

> - GPL: Uses doc2query to construct synthetic data and does knowledge distillation (i.e. training) on that data.
> - Doc2query: Generates queries to extend the documents and use BM25 on top of them w/o training.

2021-12-15 A propos

Improving Language Models by Retrieving from Trillions of Tokens | DeepMind

Tags:

2021-12-09 A propos

Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

Tags:

2021-12-05 A propos

Unsupervised_Extractive_Summarization - a Hugging Face Space by Hellisotherpeople

Tags:

2021-12-03 A propos

Unsupervised Training for Sentence Transformers | Pinecone

Tags:

2021-11-24 A propos

How to Fine-Tune Sentence-BERT for Question Answering | Capital One

Tags:

2021-11-21 A propos

Multilingual Sentence Transformers | Pinecone

Tags:

2021-11-04 A propos

Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations – Google Research (WWW 2020)

Tags:

2021-11-04 A propos

Train embeddings by using the Two-Tower built-in algorithm | Vertex AI

Tags:

> The Two-Tower model pairs similar types of objects, such as user profiles, search queries, web documents, answer passages, or images, in the same vector space, so that related items are close to each other. **The Two-Tower model consists of two encoder towers: the query tower and the candidate tower**. These towers embed independent items into a shared embedding space, which lets Matching Engine retrieve similarly matched items.
>
> To train a Two-Tower model, Google uses **pairs of relevant items**. Each pair consists of a query document and a candidate document. Documents contain arbitrary customer-defined features including text, numeric, and categorical features. After training, the Two-Tower built-in algorithm exports two TensorFlow SavedModels—a query encoder and a candidate encoder... Given a query item, Matching Engine uses the query encoder to generate a query embedding, and uses the index to find similar candidate embeddings. Matching Engine uses the candidate encoder to index all the items and serve them by using an approximate nearest neighbor solution.

2021-11-04 A propos

On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines (2021)

Tags:

2021-10-30 A propos

Next-Gen Sentence Embeddings with Multiple Negatives Ranking Loss | Pinecone

Tags:

2021-10-27 A propos

Sentence Embeddings and Transformers | Pinecone

Tags:

2021-10-23 A propos

Sahajtomar/french_semantic · Hugging Face

Tags:

2021-10-14 A propos

Omer Levy sur Twitter : "What if I told you that fine-tuning T5-Large (0.8B params) on a couple hundred examples could outperform GPT-3 (175B params) on a bunch of tasks?"

Tags:

2021-10-13 A propos

Google AI Blog: Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer (2020)

Tags:

2021-10-13 A propos

[2106.04647] Compacter: Efficient Low-Rank Hypercomplex Adapter Layers

Tags:

2021-09-29 A propos

[2010.12566] DICT-MLM: Improved Multilingual Pre-Training using Bilingual Dictionaries

Tags:

2021-09-06 A propos

[2102.07043] Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

Tags:

> a method for constructing **a virtual KB (VKB) trained entirely from text**

Open Predicate Query Language (OPQL): constructing a virtual knowledge base (VKB) that supports KB reasoning & open-domain QA, tackling the incompleteness of knowledge bases by constructing a virtual KB only from text

> OPQL constructs
a VKB by **encoding and indexing a set of
relation mentions** in a way that naturally enables
reasoning and can be trained without any structured
supervision.

> can be used
as an **external memory integrated into a language
model**

cf. this earlier paper [[2002.10640] Differentiable Reasoning over a Virtual Knowledge Base](doc:2020/07/2002_10640_differentiable_rea). But does not require an initial structured KB for distant
supervision.

> The key idea in constructing the OPQL VKB is to use a
dual-encoder pre-training process, similar to 
[[1906.03158] Matching the Blanks: Distributional Similarity for Relation Learning](doc:2021/05/1906_03158_matching_the_blank)

Related work section refers to [[1909.04164] Knowledge Enhanced Contextual Word Representations](doc:2020/05/1909_04164_knowledge_enhanced). Also refers to [[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](doc:2020/07/2007_00849_facts_as_experts_) (some authors in common)

2021-06-20 A propos

Semantic Search with S-BERT is all you need

Tags:

2021-06-05 A propos

[1906.03158] Matching the Blanks: Distributional Similarity for Relation Learning

Tags:

2021-05-13 A propos

[1909.10506] Learning Dense Representations for Entity Retrieval

Tags:

> 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 A propos

Nils Reimers sur Twitter : "SBERT Release v1.1.0"

Tags:

2021-04-22 A propos

[2011.05864] On the Sentence Embeddings from Pre-trained Language Models

Tags:

2021-04-19 A propos

SimCSE: Simple Contrastive Learning of Sentence Embeddings

Tags:

2021-04-18 A propos

Nils Reimers sur Twitter : "New models for Neural Information Retrieval..."

Tags:

2021-04-17 A propos

[2007.12603] IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles

Tags:

2021-04-12 A propos

[2007.15779] Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Tags:

2021-04-11 A propos

[1902.00751] Parameter-Efficient Transfer Learning for NLP

Tags:

2021-04-11 A propos

exBERT: Extending Pre-trained Models with Domain-specific Vocabulary Under Constrained Training Resources - ACL Anthology

Tags:

**Focus on the Embedding of Domain-specific Vocabulary.**

> exBERT
adds a new domain-specific vocabulary and the corresponding
embedding layer, as well as a small
extension module to the original unmodified model

> a pretraining
method allowing **low-cost embedding of
domain-specific vocabulary in the context of an
existing large pre-trained model such as BERT**

> exBERT... explicitly incorporates
the new domain’s vocabulary, while being able to
**reuse the original pre-trained model’s weights as is**
to reduce required computation and training data. Specifically, exBERT extends BERT by augmenting
its embeddings for the original vocabulary with
new embeddings for the domain-specific vocabulary
via **a learned small “extension” module**. **The
output of the original and extension modules are
combined via a trainable weighted sum operation**

In a way similar to concept developed in

> [[1902.00751] Parameter-Efficient Transfer Learning for NLP](doc:2021/04/1902_00751_parameter_efficien), but not in the fine-tuning paradigm.

[Github](https://github.com/cgmhaicenter/exBERT)

2021-04-11 A propos

[1910.02227] Making sense of sensory input

Tags:

2021-04-10 A propos

[1901.04085] Passage Re-ranking with BERT

Tags:

2021-03-26 A propos

SentenceTransformers Documentation

Tags:

2021-03-25 A propos

Rodrigo Nogueira sur Twitter : "Slides of our WSDM 2021 tutorial "Pretrained Transformers for Text Ranking: BERT and Beyond"

Tags:

2021-03-09 A propos

Zero-Shot Learning in Modern NLP | Joe Davison Blog (2020-05)

Tags:

2021-02-23 A propos

kamalkraj/BERT-NER: Pytorch-Named-Entity-Recognition-with-BERT

Tags:

2021-02-07 A propos

[1911.03681] E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT

Tags:

> way of **injecting factual knowledge about entities into the pretrained BERT model**.

(Feeding entity vectors
into BERT as if they
were wordpiece vectors without additional encoder
pretraining)

>
> **We align [Wikipedia2Vec](tag:wikipedia2vec) entity vectors (Yamada et al., 2016) with BERT's native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors**. The resulting entity-enhanced version of BERT (called E-BERT) is similar in spirit to [ERNIE](tag:ernie) (Zhang et al., 2019) and [KnowBert](tag:knowbert) (Peters et al., 2019), but it **requires no expensive further pretraining of the BERT encoder**.
>
> Our vector space alignment strategy is inspired by
cross-lingual word vector alignment

Related work on Entity-enhanced BERT:

> ([ERNIE](doc:2019/08/_1905_07129_ernie_enhanced_la) and [Knowbert](doc:2020/05/1909_04164_knowledge_enhanced)) are based on the design principle
that BERT be adapted to entity vectors. They introduce
new encoder layers to feed pretrained entity
vectors into the Transformer, and they require additional
pretraining to integrate the new parameters.
In contrast, E-BERT’s design principle is that entity
vectors be adapted to BERT.
>
> Two other knowledge-enhanced MLMs are [KEPLER](doc:2020/11/1911_06136_kepler_a_unified_)
(Wang et al., 2019c) and K-Adapter (Wang
et al., 2020)... Their factual knowledge
does not stem from entity vectors – instead, they
are trained in a multi-task setting on relation classification
and knowledge base completion.

Not to be cofounded with [[2009.02835] E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce](doc:2020/12/2009_02835_e_bert_a_phrase_a)

2021-01-12 A propos

google/tapas-base-finetuned-wtq · Hugging Face

Tags:

2020-12-17 A propos

[2009.02835] E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce

Tags:

2020-12-14 A propos

[2002.08909] REALM: Retrieval-Augmented Language Model Pre-Training

Tags:

**Augment language model pre-training with a retriever module**, which
is trained using the masked language modeling objective.

> To capture knowledge in a more modular and interpretable way, we augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. **For the first time, we show how to pre-train such a knowledge retriever in an unsupervised manner**, using masked language modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents

Hum, #TODO: parallel to be drawn with techniques in [KG-augmented Language Models](tag:knowledge_graph_augmented_language_models) which focus "on the problem of capturing declarative knowledge in the learned parameters of a language model."

[Google AI Blog Post](doc:2020/08/google_ai_blog_realm_integrat)

[Summary](https://joeddav.github.io/blog/2020/03/03/REALM.html) for the [Hugging Face awesome-papers reading group](doc:2021/03/huggingface_awesome_papers_pap)

2020-12-12 A propos

Supporting content decision makers with machine learning | Dec, 2020 | Netflix TechBlog

Tags:

2020-12-11 A propos

Keyword Extraction with BERT | Towards Data Science

Tags:

2020-12-06 A propos

Salmon Run: Word Sense Disambiguation using BERT as a Language Model

Tags:

2020-12-01 A propos

Domain-Specific BERT Models · Chris McCormick

Tags:

Domain-Specific BERT

2020-12-01 A propos

[2010.01057] LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

Tags:

2020-11-26 A propos

raphaelsty/ckb: Contextual knowledge bases

Tags:

2020-11-09 A propos

[2010.03496] Inductive Entity Representations from Text via Link Prediction

Tags:

BLP "BERT for Link Prediction". Central idea: **training an entity encoder with a
link prediction objective** (using the textual descriptions of entities when computing entity representations - hence not failing with entities unknown in training)

> a method for **learning representations
of entities**, that uses a **pre-trained Transformer** based
architecture as an entity encoder, and
**link prediction training on a knowledge graph
with textual entity descriptions**.

> using entity descriptions,
an entity encoder is trained for link prediction in
a knowledge graph. The encoder can then be used
without fine-tuning to obtain features for entity classification
and information retrieval

Cites [Xie et al](doc:2020/10/representation_learning_of_know) and [Kepler](doc:2020/11/1911_06136_kepler_a_unified_). They claim that their
objective targeted exclusively for link prediction (and not an objective that combines language modeling
and link prediction as Kepler)
performs better than Kepler's more complex one.

2020-11-03 A propos

Which flavor of BERT should you use for your QA task? | by Olesya Bondarenko | Towards Data Science

Tags:

2020-10-04 A propos

[2010.00402] From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

Tags:

2020-10-03 A propos

Google AI Blog: REALM: Integrating Retrieval into Language Representation Models

Tags:

2020-08-13 A propos

UKPLab/sentence-transformers: Sentence Embeddings with BERT & XLNet

Tags:

2020-07-14 A propos

How to use BERT for finding similar sentences or similar news? · Issue #876 · huggingface/transformers

Tags:

2020-07-12 A propos

[2004.07202] Entities as Experts: Sparse Memory Access with Entity Supervision

Tags:

2020-07-11 A propos

[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge

Tags:

> a neural language model that includes **an explicit interface between symbolically interpretable factual information and subsymbolic neural knowledge.**... **The model can be updated without re-training by manipulating its symbolic representations**. In particular this model allows us to add new facts and overwrite existing ones.

> a **neural language model which learns to access information
in a symbolic knowledge graph.**

> This
model builds on the recently-proposed [Entities as
Experts](doc:2020/07/2004_07202_entities_as_expert) (EaE) language model (Févry et al., 2020),
which extends the same transformer (Vaswani
et al., 2017) architecture of BERT (Devlin et al., 2019) with an additional external memory for entities.
>
> After training EaE, the embedding associated
with an entity will (ideally) capture information
about the textual context in which that
entity appears, and by inference, the entity’s semantic
properties
>
> we include an additional
memory called a fact memory, which encodes
triples from a symbolic KB.
>
> This combination results in a
neural language model which learns to access information
in a the symbolic knowledge graph.

TODO:

- read again IBM's [Span Selection Pre-training for Question Answering](doc:2019/09/_1909_04120_span_selection_pre) ("an effort to avoid encoding general knowledge in the transformer network itself")
- compare with [[1907.05242] Large Memory Layers with Product Keys](doc:2019/07/_1907_05242_large_memory_layer)
- how does it relate with [[2002.08909] REALM: Retrieval-Augmented Language Model Pre-Training](doc:2020/12/2002_08909_realm_retrieval_a)?

2020-07-09 A propos

BERT Word Embeddings Tutorial · Chris McCormick

Tags:

2020-07-06 A propos

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

Tags:

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

Patrick von Platen sur Twitter : "Today, @huggingface is the start of our Reformer series..."

Tags:

2020-06-29 A propos

[2001.04451] Reformer: The Efficient Transformer

Tags:

2020-06-29 A propos

Representation Learning for Information Extraction from Form-like Documents – Google Research

Tags:

2020-06-15 A propos

Google AI Blog: Extracting Structured Data from Templatic Documents (2020)

Tags:

[About this paper](doc:2020/06/representation_learning_for_inf)

Templatic documents (eg. invoices): such documents do not contain “natural
language” but
instead resemble forms, with data often presented in tables

> an approach that **uses knowledge of target field types to identify
candidate fields**. These are then scored using **a neural network that
learns a dense representation of each candidate using the words in its
neighborhood**. Experiments on two corpora (invoices and receipts) show
that we’re able to generalize well to unseen layouts.
>
> An understanding of the **two-dimensional layout of text**
on the page is key to understanding such documents. On the other hand,
treating this purely as an image segmentation problem makes it difficult
to take advantage of the semantics of the text.
>
> Our approach to this problem allows developers to train and deploy an
extraction system for a given domain (like invoices) using **two inputs — a
target schema (i.e., a list of fields to extract and their corresponding
types) and a small collection of documents labeled with the ground truth
for use as a training set**

- The input document is first run through an [OCR service](doc:2020/06/detecter_le_texte_dans_les_fich).
- a candidate generator identifies spans of text in the OCR output that might correspond to
an instance of a given field (uses pre-existing
libraries associated with each field type)
- Each candidate is then scored using a neural
network (that is trained as a binary classifier)

2020-06-15 A propos

[1804.03235] Large scale distributed neural network training through online distillation

Tags:

2020-06-06 A propos

On word embeddings

Tags:

2020-06-05 A propos

[1909.04164] Knowledge Enhanced Contextual Word Representations

Tags:

2020-05-13 A propos

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

Tags:

2020-05-02 A propos

BERT, ELMo, & GPT-2: How Contextual are Contextualized Word Representations? | SAIL Blog

Tags:

2020-03-28 A propos

[1909.03193] KG-BERT: BERT for Knowledge Graph Completion

Tags:

2020-03-22 A propos

[1909.07606] K-BERT: Enabling Language Representation with Knowledge Graph

Tags:

2020-03-08 A propos

Unsupervised NER using BERT - Hands-on NLP model review - Quora

Tags:

2020-03-06 A propos

[2002.12327] A Primer in BERTology: What we know about how BERT works

Tags:

2020-02-28 A propos

[2002.11402] Detecting Potential Topics In News Using BERT, CRF and Wikipedia

Tags:

2020-02-27 A propos

Distilling BERT models with spaCy - Towards Data Science (2019)

Tags:

2020-02-15 A propos

Hugging Face sur Twitter : DistilBERT-cased for Question Answering w/ just 3 lines of javascript

Tags:

2020-02-14 A propos

How Much Knowledge Can You Pack Into the Parameters of a Language Model?

Tags:

> It has recently been observed that neural language
models trained on unstructured text can
implicitly store and retrieve knowledge using
natural language queries.

indeed, cf. Facebook's paper [Language Models as Knowledge Bases?](/doc/2019/09/_1909_01066_language_models_as)

> In this short paper,
we measure the practical utility of this
approach by fine-tuning pre-trained models to
answer questions without access to any external
context or knowledge.

> we show that a large language
model pre-trained on unstructured text can
attain competitive results on open-domain question
answering benchmarks without any access
to external knowledge

BUT:

>1. state-of-the-art results only with the largest model
which had 11 billion parameters.
>1. “open-book” models
typically provide some indication of what information
they accessed when answering a question
that provides a useful form of interpretability.
In contrast, our model distributes knowledge
in its parameters in an inexplicable way, which
precludes this form of interpretability.
>1. **the maximum-likelihood objective provides no guarantees as to whether
a model will learn a fact or not.**

So, what's the point? To be compared with this [IBM's paper](/doc/2019/09/_1909_04120_span_selection_pre): "a new pre-training task inspired by reading comprehension and an effort to avoid encoding general knowledge in the transformer network itself"

2020-02-11 A propos

Adam Roberts sur Twitter : "New preprint: How Much Knowledge Can You Pack into the Parameters of a Language Model?..."

Tags:

2020-02-11 A propos

[1911.05507] Compressive Transformers for Long-Range Sequence Modelling

Tags:

2020-02-11 A propos

[2002.02925] BERT-of-Theseus: Compressing BERT by Progressive Module Replacing

Tags:

2020-02-10 A propos

Canwen Xu sur Twitter : "WTF? We brutally dismember BERT and replace all his organs?"

Tags:

2020-02-10 A propos

[1503.03832] FaceNet: A Unified Embedding for Face Recognition and Clustering

Tags:

2020-01-25 A propos

Paris NLP Season 4 Meetup #3 – Paris NLP (2020)

Tags:

2020-01-23 A propos

Semantic Text Matching for Long-Form Documents (2019)

Tags:

2020-01-23 A propos

Building a Search Engine with BERT and TensorFlow - Towards Data Science

Tags:

2020-01-12 A propos

Elasticsearch meets BERT: Building Search Engine with Elasticsearch and BERT

Tags:

2020-01-10 A propos

NLP's Clever Hans Moment has Arrived

Tags:

2020-01-10 A propos

[2003.05473] Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking (CoNNL 2019)

Tags:

Training BERT-base-uncased on English Wikipedia and then fine-tuned and evaluating it
on an entity linking (EL) benchmark (EL implemented as a token classification over the entity vocabulary)

> BERT+Entity is a straightforward extension on top
of BERT, i.e. we initialize BERT with the publicly
available weights from the BERT-base-uncased
model and add an output classification layer on
top of the architecture. Given a contextualized token,
the classifier computes the probability of an
entity link for each entry in the entity vocabulary.

Can BERT’s architecture learn all entity
linking steps jointly? To answer:

> an extreme
simplification of the **entity linking setup that
works surprisingly well**: simply cast it as **a
per token classification over the entire entity
vocabulary** (over 700K classes in our case).

> the model
is the first that performs entity linking without any
pipeline or any heuristics, compared to all prior
approaches. We found that with our approach we
can learn additional entity knowledge in BERT that
helps in entity linking. **However, we also found
that almost none of the downstream tasks really
required entity knowledge**.

### Related work

- > [Durrett and Klein (2014)](/doc/2020/01/a_joint_model_for_entity_analys) were the first to propose
jointly modelling Mention detection, Candidate generation and Entity disambiguation in a graphical
model and could show that each of those steps are
interdependent and benefit from a joint objective

This paper uses neural techniques instead of CRF.

- > [Yamada](/showprop.do?pptyuri=http%3A%2F%2Fwww.semanlink.net%2F2001%2F00%2Fsemanlink-schema%23arxiv_author&pptyval=Ikuya%2BYamada) (2016, 2017) was the first to
investigate neural text representations and entity
linking, but their approach is limited to ED.

cf. [#Wikipedia2Vec](tag:wikipedia2vec). Compare with [newer work by Yamada](doc:2020/09/1909_01259_neural_attentive_b)

2020-01-09 A propos

Named Entity Recognition with Bert – Depends on the definition

Tags:

2020-01-09 A propos

[1902.10909] BERT for Joint Intent Classification and Slot Filling

Tags:

2020-01-09 A propos

Richer Sentence Embeddings using Sentence-BERT — Part I

Tags:

Sentence-BERT

2020-01-06 A propos

Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019

Tags:

2020-01-05 A propos

[1807.00082] Amanuensis: The Programmer's Apprentice

Tags:

**The use of natural language to facilitate communication
between the expert programmer and apprentice AI system.**

> an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon **insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems** that leverage and extend the state of the art in machine learning **by integrating human and machine intelligence**. As a concrete example we focus on digital assistants that learn from continuous dialog with an expert software engineer while providing initial value as powerful analytical, computational and mathematical savants.

> [#Dehaene](/tag/stanislas_dehaene)'s work extends the [#Global Workspace Theory](/tag/global_workspace_theory) of Bernard Baars. Dehaene’s version of the theory combined with Yoshua Bengio’s concept of a [#consciousness prior](/tag/consciousness_prior.html) and deep reinforcement learning suggest a model for constructing and maintaining the cognitive states that arise and persist during complex problem solving.

2019-11-12 A propos

CamemBERT

Tags: