Semanlink - EMNLP AND Transformers

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Active learning - Adapter modules (LM finetuning) - Allen Institute for AI (AI2) - Anisotropy in LM space - Arxiv Doc - BERT - BERT + Sentence Embeddings - Biomedical NLP - Contextualized word representations - Domain adaptation (NLP) - EMNLP 2019 - EMNLP 2020 - EMNLP 2021 - EMNLP 2022 - EMNLP 2023 - Entities and LM - Entity embeddings - Good - Grounded Language Learning - Ikuya Yamada - Imbalanced Data - Information bottleneck method - Information theory AND Deep Learning - KD-MKB biblio - Knowledge-augmented Language Models - Knowledge-driven embeddings - Knowledge Graphs in NLP - Link Prediction - Memory in deep learning - Multiple Knowledge Bases - Nearest neighbor search - NLP@Harvard - NLP@IBM - NLP@Microsoft - NLP@Stanford - Self-Attention - Sentence Similarity - Siamese networks - Text Classification - Transformers - Tweet -

EMNLP AND Transformers

Common descendants

11 Documents

UKP Lab sur X : "a lightweight solution for few-shot domain-specific sentence classification: AdaSent!..."

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2023-12-09 About

Allen Institute for AI sur Twitter : "MemPrompt, appearing at #EMNLP2022, is a new way to "fix" #GPT3 after deployment via user interaction"

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2022-12-11 About

Active Learning for BERT: An Empirical Study - ACL Anthology

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2022-09-02 About

[2008.12813] HittER: Hierarchical Transformers for Knowledge Graph Embeddings

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2022-06-30 About

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

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2021-04-19 About

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 About

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

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2020-11-26 About

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 About

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

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2020-03-28 About

Evolution of Representations in the Transformer (2019)

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2019-09-16 About

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

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2019-08-28 About