Semanlink - Machine learning: problems AND Pretrained models

[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?..."

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2022-04-13 A propos

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

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2022-04-01 A propos

[1910.06294] Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models

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2022-03-31 A propos

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

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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)

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2022-03-31 A propos

Nils Reimers sur Twitter : "Great post on SetFit"

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2022-03-31 A propos

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

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2022-03-31 A propos

Domain Adaptation — Sentence-Transformers documentation

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2022-03-31 A propos

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

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> 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

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

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2022-03-18 A propos

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

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2022-03-15 A propos

Document Matching for Job Descriptions | Semantic Scholar (2021)

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2022-03-09 A propos

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

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2022-03-09 A propos

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

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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

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2022-02-19 A propos

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

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2022-02-19 A propos

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

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2022-02-18 A propos

gsarti/scibert-nli · Hugging Face

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2022-01-29 A propos

Semantic Search — Sentence-Transformers documentation

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2022-01-29 A propos

[2108.13934] Robust Retrieval Augmented Generation for Zero-shot Slot Filling

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2022-01-19 A propos

Domain Transfer with BERT | Pinecone

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2022-01-04 A propos

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

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2021-12-17 A propos

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

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2021-12-17 A propos

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

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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

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

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2021-12-05 A propos

Unsupervised_Extractive_Summarization - a Hugging Face Space by Hellisotherpeople

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2021-12-03 A propos

Unsupervised Training for Sentence Transformers | Pinecone

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2021-11-24 A propos

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

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2021-11-21 A propos

Multilingual Sentence Transformers | Pinecone

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2021-11-04 A propos

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

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2021-10-27 A propos

Sentence Embeddings and Transformers | Pinecone

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2021-10-23 A propos

[2106.13474] Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains

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2021-10-21 A propos

Sahajtomar/french_semantic · Hugging Face

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2021-10-14 A propos

Semantic Search with S-BERT is all you need

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2021-06-05 A propos

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

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2021-04-22 A propos

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

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2021-04-19 A propos

SimCSE: Simple Contrastive Learning of Sentence Embeddings

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2021-04-18 A propos

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

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2021-04-17 A propos

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

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2021-04-12 A propos

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

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2021-04-11 A propos

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

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**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

[1901.04085] Passage Re-ranking with BERT

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2021-03-26 A propos

SentenceTransformers Documentation

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2021-03-25 A propos

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

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2021-03-09 A propos

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

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2021-02-23 A propos

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

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2021-02-07 A propos

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

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> 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

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

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2020-12-14 A propos

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

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2020-12-11 A propos

Domain-Specific BERT Models · Chris McCormick

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Domain-Specific BERT

2020-12-01 A propos

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

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2020-11-26 A propos

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 A propos

[2010.03496] Inductive Entity Representations from Text via Link Prediction

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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

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

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2020-07-14 A propos

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

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2020-07-12 A propos

BERT Word Embeddings Tutorial · Chris McCormick

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2020-07-06 A propos

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

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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

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 A propos

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

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2020-03-06 A propos

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

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2020-02-27 A propos

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

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2020-01-23 A propos

Named Entity Recognition with Bert – Depends on the definition

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2020-01-09 A propos

[1902.10909] BERT for Joint Intent Classification and Slot Filling

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2020-01-09 A propos

Richer Sentence Embeddings using Sentence-BERT — Part I

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Sentence-BERT

2020-01-06 A propos

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

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2020-01-05 A propos

Efficient multi-lingual language model fine-tuning · fast.ai NLP

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2019-10-22 A propos

[1908.10084] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

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2019-08-28 A propos

[1905.07129] ERNIE: Enhanced Language Representation with Informative Entities

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2019-08-05 A propos

20% Accuracy Bump in Text Classification with ME-ULMFiT

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2019-06-23 A propos

[1906.02715] Visualizing and Measuring the Geometry of BERT

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2019-06-07 A propos

"I made a bet that a Naive Bayes classifier would work as well on humor recognition as a neural net with fine-tuned Bert embeddings. I won"

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2019-06-06 A propos

A Technique for Building NLP Classifiers Efficiently with Transfer Learning and Weak Supervision

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2019-05-22 A propos

[1904.08398] DocBERT: BERT for Document Classification

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2019-04-18 A propos

kheyer/Genomic-ULMFiT: ULMFiT for Genomic Sequence Data

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2019-04-02 A propos

[1902.11269] Efficient Contextual Representation Learning Without Softmax Layer

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**how to accelerate contextual representation learning**.

> Contextual representation models are difficult to train due to the large parameter sizes and high computational complexity

> We find that the softmax layer (the output layer) causes significant inefficiency due to the large vocabulary size.
Therefore, we redesign the learning objectiv.
> Specifically, the proposed approach bypasses the softmax layer by performing language modeling with dimension reduction, and allows the models to leverage pre-trained word embeddings.
Our framework reduces the time spent on the output layer to a negligible level, eliminates almost all the trainable parameters of the softmax layer and performs language modeling without truncating the vocabulary.
When applied to ELMo, our method achieves a 4 times speedup and eliminates 80% trainable parameters while achieving competitive performance on downstream tasks.

**decouples learning contexts and words**

> Instead of using
a softmax layer to predict the distribution of the
missing word, we utilize and extend the SEMFIT
layer (Kumar and Tsvetkov, 2018) to **predict the
embedding of the missing word**.

2019-03-02 A propos

The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar

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