Semanlink - NLP tasks / problems AND NLP@Google

> **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

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

Tags:

2022-06-29 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

BERTopic: The Future of Topic Modeling | Pinecone

Tags:

2022-05-12 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

[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

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

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

Tags:

2022-03-10 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

gsarti/scibert-nli · Hugging Face

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

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

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

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

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

Tags:

2021-10-13 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

[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

[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

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

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

Tags:

2020-12-14 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

raphaelsty/ckb: Contextual knowledge bases

Tags:

2020-11-09 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

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

Tags:

2020-07-11 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

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

Tags:

2020-06-15 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

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

Tags:

2020-03-06 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

Semantic Text Matching for Long-Form Documents (2019)

Tags:

2020-01-23 A propos

Elasticsearch meets BERT: Building Search Engine with Elasticsearch and BERT

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: