Semanlink - Allen Institute for AI (AI2) AND Embeddings

Printer friendly

Search Tag:

Search Doc:

Preferences...

Tag Cloud

Allen Institute for AI (AI2) - Amazon Alexa - Arxiv Doc - Contextualized word representations - ELMo - Embeddings - EMNLP 2019 - GitHub project - Good - Grounded Language Learning - KD-MKB biblio - Keras - Knowledge Graphs in NLP - Language Model - Multiple Knowledge Bases - NLP@Meta - NLP@Stanford - Transfer learning -

Allen Institute for AI (AI2) AND Embeddings

Common descendants

12 Documents

[2212.09741] One Embedder, Any Task: Instruction-Finetuned Text Embeddings

Tags:

2023-02-17 About

Embedding Recycling: Making Language Model Development More Sustainable | AI2 Blog

Tags:

2023-02-17 About

[1902.06006] Contextual Word Representations: A Contextual Introduction

Tags:

2022-07-08 About

[1909.04164] Knowledge Enhanced Contextual Word Representations

Tags:

2020-05-13 About

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

Tags:

2020-01-05 About

[1902.11269] Efficient Contextual Representation Learning Without Softmax Layer

Tags:

**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

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

Tags:

2018-12-03 About

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

Tags:

2018-11-20 About

iliaschalkidis/ELMo-keras: Re-implementation of ELMo on Keras

Tags:

2018-11-14 About

AI Can Recognize Images, But Text Has Been Tricky—Until Now | WIRED

Tags:

2018-09-08 About

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

Tags:

2018-06-19 About

ELMo: Deep contextualized word representations (2018)

Tags:

ELMo

2018-02-16 About