Semanlink - NLP tasks / problems AND Arxiv Doc

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

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

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

> ---

> extends a traditional **RNN LM**

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

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

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

2022-08-31 About

[2112.07708] Learning to Retrieve Passages without Supervision

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2022-08-28 About

[2208.11857] Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey

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2022-08-27 About

[2208.09982] GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization

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2022-08-24 About

[2012.15156] A Memory Efficient Baseline for Open Domain Question Answering

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2022-08-08 About

[2208.01815] Effidit: Your AI Writing Assistant

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2022-08-06 About

[1807.00745] Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

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2022-07-18 About

[2207.06300] Re2G: Retrieve, Rerank, Generate

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2022-07-14 About

[2006.01969] REL: An Entity Linker Standing on the Shoulders of Giants

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2022-07-12 About

[2205.00820] Entity-aware Transformers for Entity Search

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2022-07-12 About

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

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> **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 About

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

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2022-06-11 About

[2204.08173] TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

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2022-05-11 About

[2012.12624] Learning Dense Representations of Phrases at Scale

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2022-05-11 About

[2205.03983] Building Machine Translation Systems for the Next Thousand Languages

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2022-05-10 About

[2008.09470] Top2Vec: Distributed Representations of Topics

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2022-04-28 About

[1909.00426] Global Entity Disambiguation with BERT

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2022-04-18 About

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

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[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 About

[2203.10581] Cluster & Tune: Boost Cold Start Performance in Text Classification

Tags:

[Leshem Choshen sur Twitter : "Labelled data is scarce, what can we do?..."](doc:2022/04/leshem_choshen_sur_twitter_l)

> **One-sentence Summary**: we suggest adding an unsupervised intermediate classification step, before finetunning and after pretraining BERT, and show it improves performance for data-constrained cases.

> for text classification cold start (when labeled
data is scarce), **add an intermediate unsupervised
classification task**, between the pretraining
and fine-tuning phases:
> perform clustering and
train the pre-trained model on predicting the
cluster labels.

> this additional
classification phase can significantly improve
performance, mainly for **topical classification**
tasks

> we use an efficient clustering technique,
that relies on simple Bag Of Words (BOW)
representations, to partition the unlabeled training
data into relatively homogeneous clusters of text
instances.
>
> Next, we treat these clusters as labeled
data for an intermediate text classification task, and
train the pre-trained model – with or without additional
MLM pretraining – with respect to this
multi-class problem, prior to the final fine-tuning
over the actual target-task labels

> The underlying
intuition is that inter-training the model
over a related text classification task would be more
beneficial compared to MLM inter-training, which
focuses on different textual entities, namely predicting
the identity of a single token.

2022-04-06 About

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

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2022-04-01 About

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

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2022-03-31 About

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

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2022-03-31 About

[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 About

[2006.00632] Neural Unsupervised Domain Adaptation in NLP---A Survey

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2022-03-30 About

[2110.10778] Contrastive Document Representation Learning with Graph Attention Networks

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2022-03-10 About

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

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2022-02-25 About

[2004.11892] Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering

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2022-02-11 About

[2004.07180] SPECTER: Document-level Representation Learning using Citation-informed Transformers

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2022-01-29 About

[2009.02252] KILT: a Benchmark for Knowledge Intensive Language Tasks

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2022-01-23 About

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

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2022-01-19 About

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

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2022-01-19 About

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

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2022-01-11 About

[1904.08375] Document Expansion by Query Prediction

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2022-01-05 About

[2112.09118] Towards Unsupervised Dense Information Retrieval with Contrastive Learning

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2021-12-21 About

[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 About

[1909.06356] Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering

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2021-12-08 About

[1906.04980] Unsupervised Question Answering by Cloze Translation

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2021-12-08 About

[2112.01488] ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

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2021-12-05 About

[1911.02655] Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks

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2021-11-19 About

[2108.13854] Contrastive Domain Adaptation for Question Answering using Limited Text Corpora

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2021-11-19 About

[1706.03610] Neural Domain Adaptation for Biomedical Question Answering

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2021-11-19 About

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

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2021-10-21 About

[1908.11860] Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification

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2021-10-21 About

[1712.05972] Train Once, Test Anywhere: Zero-Shot Learning for Text Classification

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2021-10-16 About

[2010.07245] Text Classification Using Label Names Only: A Language Model Self-Training Approach

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2021-10-16 About

[2004.09095] The State and Fate of Linguistic Diversity and Inclusion in the NLP World

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2021-10-03 About

[2109.08133] Phrase Retrieval Learns Passage Retrieval, Too

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2021-09-30 About

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

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2021-09-06 About

[2104.06979] TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

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> The most
successful previous approaches like InferSent (Conneau
et al., 2017), Universial Sentence Encoder
(USE) (Cer et al., 2018) and SBERT (Reimers and
Gurevych, 2019) heavily relied on labeled data to
train sentence embedding models.
>
> TSDAE can
achieve up to 93.1% of the performance of indomain
supervised approaches. Further, we
show that TSDAE is **a strong domain adaptation
and pre-training method for sentence
embeddings**, significantly outperforming other
approaches like Masked Language Model.

> During training, TSDAE
encodes corrupted sentences into fixed-sized
vectors and requires the decoder to reconstruct the
original sentences from this sentence embedding.

- <https://www.sbert.net/examples/unsupervised_learning/TSDAE/README.html>
- [github](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE)
- [UKPLab/sentence-transformers: Sentence Embeddings with BERT & XLNet](doc:2020/07/ukplab_sentence_transformers_s)
- [twitter](https://twitter.com/KexinWang2049/status/1433361957579538432):

> **TSDAE can learn domain-specific sentence embeddings with unlabeled sentences**
>
> Most importantly, instead of STS (Semantic Textual Similarity), **we suggest evaluating unsupervised sentence embeddings on the domain-specific tasks&datasets, which is the real use case for them**. Actually, STS scores do not correlate with performance on specific tasks.

2021-09-01 About

[2010.02353] Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages

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2021-08-25 About

[2107.12708] QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

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2021-08-06 About

[1911.02116] Unsupervised Cross-lingual Representation Learning at Scale

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2021-07-29 About

[2107.00676] A Primer on Pretrained Multilingual Language Models

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2021-07-13 About

[2010.06467] Pretrained Transformers for Text Ranking: BERT and Beyond

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2021-07-09 About

[2103.11811] MasakhaNER: Named Entity Recognition for African Languages

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2021-07-06 About

[2010.12309] A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios

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2021-07-06 About

[2006.07264] Low-resource Languages: A Review of Past Work and Future Challenges

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2021-07-06 About

[1906.05685] A Focus on Neural Machine Translation for African Languages

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2021-06-30 About

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

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> 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 About

[2106.00882] Efficient Passage Retrieval with Hashing for Open-domain Question Answering

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2021-06-03 About

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

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2021-06-03 About

[2104.10809] Provable Limitations of Acquiring Meaning from Ungrounded Form: What will Future Language Models Understand?

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2021-05-23 About

[2001.11631] Enhancement of Short Text Clustering by Iterative Classification

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2021-05-20 About

[2103.12953] Supporting Clustering with Contrastive Learning

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2021-05-20 About

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

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2021-05-13 About

[1909.10506] Learning Dense Representations for Entity Retrieval

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> 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 About

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

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2021-04-11 About

[1902.00751] Parameter-Efficient Transfer Learning for NLP

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2021-04-11 About

[2103.12876] Complex Factoid Question Answering with a Free-Text Knowledge Graph

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2021-03-30 About

[1911.03876] Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering

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2021-02-08 About

[2010.00904] Autoregressive Entity Retrieval

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2021-01-14 About

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

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2020-12-14 About

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

Tags:

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

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

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

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

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

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

Refers to:

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

2020-12-11 About

[2004.10964] Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

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2020-12-01 About

[2011.06993] FLERT: Document-Level Features for Named Entity Recognition

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2020-12-01 About

[1911.11506] Word-Class Embeddings for Multiclass Text Classification

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> In supervised tasks such as multiclass
text classification (the focus of this article) it seems appealing to enhance word representations
with ad-hoc embeddings that encode task-specific information. We propose (supervised) word-class
embeddings (WCEs), and show that, when concatenated to (unsupervised) pre-trained word embeddings,
they substantially facilitate the training of deep-learning models in multiclass classification by
topic.
>
> A differentiating aspect of our method is that it keeps the modelling of word-class interactions separate from the
original word embedding. Word-class correlations are confined in a dedicated vector space, whose vectors enhance
(by concatenation) the unsupervised representations. The net effect is an embedding matrix that is better suited to
classification, and imposes no restriction to the network architecture using it.

[github](https://github.com/AlexMoreo/word-class-embeddings). Refers to [LEAM](doc:2020/02/joint_embedding_of_words_and_la) :

> [in LEAM] Once words and labels are embedded in a common vector space, word-label
compatibility is measured via cosine similarity. Our method instead models these compatibilities directly, without
generating intermediate embeddings for words or labels.

2020-10-11 About

[2004.03705] Deep Learning Based Text Classification: A Comprehensive Review

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2020-10-11 About

[2001.08053] Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization

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2020-10-01 About

[1806.06478] Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

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2020-09-06 About

[1909.01259] Neural Attentive Bag-of-Entities Model for Text Classification

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A model that performs **text classification using entities in a knowledge base**.

> Entities provide unambiguous and relevant semantic signals that are beneficial for capturing semantics in texts. We combine **simple high-recall entity detection based on a dictionary** (word->list of entities), to detect entities in a document, with a novel neural **attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities**.

2 steps:

1. Entity detection
2. Classification using the detected entities (+text) as inputs

Regarding entity linking, a local model which uses cosine
similarity between the embedding of the target
entity and the word-based representation of
the document to capture the relevance of an entity
given a document.

Embeddings from the KB: computed using [#Wikipedia2Vec](tag:wikipedia2vec) (similar words and entities
close to one another in a unified vector space)

Model using attention, with 2 features :

- cosine similarity between the
embedding of the entity and the word based
representation of the document
- the probability that the entity
name refers to the entity in KB.

Somewhat [related](doc:2020/01/investigating_entity_knowledge_)

### Conclusion:

>a neural
network model that performs text classification using
entities in Wikipedia. We combined simple
dictionary-based entity detection with a neural attention
mechanism to enable the model to focus
on a small number of unambiguous and relevant
entities in a document.

2020-09-02 About

[2003.11644] MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

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2020-08-14 About

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

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2020-07-11 About

[2002.10640] Differentiable Reasoning over a Virtual Knowledge Base

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2020-07-11 About

[2006.09462] Selective Question Answering under Domain Shift

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2020-06-30 About

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 About

[1907.04829] BAM! Born-Again Multi-Task Networks for Natural Language Understanding

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2020-05-12 About

[2004.14958] A Call for More Rigor in Unsupervised Cross-lingual Learning

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2020-05-02 About

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

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2020-05-02 About

[2004.10151] Experience Grounds Language

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2020-04-22 About

[2004.05150] Longformer: The Long-Document Transformer

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2020-04-13 About

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

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2020-02-27 About

[1910.04126] Scalable Nearest Neighbor Search for Optimal Transport

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2020-02-20 About

[1805.04174] Joint Embedding of Words and Labels for Text Classification (ACL Anthology 2018)

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2020-02-18 About

[1911.05507] Compressive Transformers for Long-Range Sequence Modelling

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2020-02-11 About

[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 About

[2001.01447] Improving Entity Linking by Modeling Latent Entity Type Information

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2020-01-09 About

[1902.10909] BERT for Joint Intent Classification and Slot Filling

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2020-01-09 About

[1912.08904] Macaw: An Extensible Conversational Information Seeking Platform

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2020-01-01 About

[1909.02164] TabFact: A Large-scale Dataset for Table-based Fact Verification

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2019-12-01 About

[1910.09760] Question Answering over Knowledge Graphs via Structural Query Patterns

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2019-11-06 About

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

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2019-11-06 About

[1909.04120] Span Selection Pre-training for Question Answering

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> a **new pre-training task inspired by reading
comprehension** and an **effort to avoid encoding general knowledge in the transformer network itself**

Current transformer architectures store general knowledge -> large models, long pre-training time. Better to offload the requirement of general knowledge to a sparsely activated network.

"Span selection" as an additional auxiliary task: the query is a sentence drawn from a corpus
with a term replaced with a special token: [BLANK]. The term replaced by the blank is the answer term. The passage is
relevant as determined by a BM25 search, and answer-bearing (containing the answer
term). Unlike BERT’s cloze task, where the answer must be drawn from the model itself, the answer is found in a passage
using language understanding.

> **We hope to progress to a model of general purpose language modeling that uses an indexed long
term memory to retrieve world knowledge, rather than holding it in the densely activated transformer encoder layers.**

2019-09-18 About

[1909.03186] On Extractive and Abstractive Neural Document Summarization with Transformer Language Models

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2019-09-11 About

[1908.08983] A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers

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2019-08-28 About

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

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2019-08-28 About

[1904.02342] Text Generation from Knowledge Graphs with Graph Transformers

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2019-08-23 About

[1905.10070] Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

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2019-06-22 About

[1810.09164] Named Entity Disambiguation using Deep Learning on Graphs

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2019-04-26 About

[1802.01021] DeepType: Multilingual Entity Linking by Neural Type System Evolution

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2019-04-25 About

[1812.09449] A Survey on Deep Learning for Named Entity Recognition

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2019-04-24 About

[1807.06036] Pangloss: Fast Entity Linking in Noisy Text Environments

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2019-04-23 About

[1808.07699] End-to-End Neural Entity Linking

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2019-04-23 About

[1904.08398] DocBERT: BERT for Document Classification

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2019-04-18 About

[1806.04411] Named Entity Recognition with Extremely Limited Data

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2019-04-11 About

[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 About

[1902.05196] Categorical Metadata Representation for Customized Text Classification

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2019-02-18 About

[1901.11504] Multi-Task Deep Neural Networks for Natural Language Understanding

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2019-02-17 About

[1711.09677] Binary classification models with "Uncertain" predictions

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2019-02-02 About

[1601.01343] Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

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2019-01-27 About

[1812.04616] Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs

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2018-12-14 About

[1811.05370] Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents

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2018-11-20 About

[1810.04805] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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2018-10-12 About

[1710.06632] Towards a Seamless Integration of Word Senses into Downstream NLP Applications

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2018-10-09 About

[1704.05358] Representing Sentences as Low-Rank Subspaces

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2018-10-06 About

[1809.01797] Describing a Knowledge Base

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2018-09-07 About

[1809.00782] Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

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2018-09-06 About

[1601.03764] Linear Algebraic Structure of Word Senses, with Applications to Polysemy

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> Here it is shown that multiple word senses reside
in linear superposition within the word
embedding and simple sparse coding can recover
vectors that approximately capture the
senses

> Each extracted word sense is accompanied by one of about  2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense.

> The success of the approach is mathematically explained using a variant of
the random walk on discourses model

("random walk": a generative model for language). Under the assumptions of this model,  there
exists a linear relationship between the vector of a
word w and the vectors of the words in its contexts (It is not the average of the words in w's context, but in a given corpus the matrix of the linear relationship does not depend on w. It can be estimated, and so we can compute the embedding of a word from the contexts it belongs to)

[Related blog post](/doc/?uri=https%3A%2F%2Fwww.offconvex.org%2F2016%2F07%2F10%2Fembeddingspolysemy%2F)

2018-08-28 About

[1806.04470] Design Challenges and Misconceptions in Neural Sequence Labeling

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2018-06-28 About

[1806.06259] Evaluation of sentence embeddings in downstream and linguistic probing tasks

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