Semanlink - Machine learning: problems AND Education

> 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

Pablo Castro sur Twitter : "Random finding of the day for word embeddings: vec("apple")-vec("apples") yields a vector close to ipad, ipod, etc. (apples removes the "fruitness" from apple)

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2020-12-18 About

[2010.00402] From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

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2020-10-03 About

A Study of multilabel text classification and the effect of label hierarchy (2015)

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2020-08-15 About

dicksontsai/stanford-nlp-local-extension: Chrome extension for sending content to localhost server running Stanford NLP tools.

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2020-07-03 About

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2020-07-02 About

FastHugs | ntentional

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2020-02-19 About

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

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2020-01-05 About

[1807.00082] Amanuensis: The Programmer's Apprentice

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**The use of natural language to facilitate communication
between the expert programmer and apprentice AI system.**

> an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon **insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems** that leverage and extend the state of the art in machine learning **by integrating human and machine intelligence**. As a concrete example we focus on digital assistants that learn from continuous dialog with an expert software engineer while providing initial value as powerful analytical, computational and mathematical savants.

> [#Dehaene](/tag/stanislas_dehaene)'s work extends the [#Global Workspace Theory](/tag/global_workspace_theory) of Bernard Baars. Dehaene’s version of the theory combined with Yoshua Bengio’s concept of a [#consciousness prior](/tag/consciousness_prior.html) and deep reinforcement learning suggest a model for constructing and maintaining the cognitive states that arise and persist during complex problem solving.

2019-11-12 About

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

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2019-10-22 About

[1802.07044] The Description Length of Deep Learning Models

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2019-10-11 About

[1703.02507] Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

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2019-03-25 About

[1902.09229] A Theoretical Analysis of Contrastive Unsupervised Representation Learning

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2019-03-20 About

Contrastive Unsupervised Learning of Semantic Representations: A Theoretical Framework – Off the convex path (2019-03)

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2019-03-20 About

Word Embeddings: Explaining their properties – Off the convex path (2016)

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2019-03-20 About

Weak Supervision: A New Programming Paradigm for Machine Learning | SAIL Blog

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2019-03-12 About

nlp-notebooks/Simple Sentence Similarity.ipynb at master · nlptown/nlp-notebooks

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2019-02-07 About

Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python - Analytics Vidhya

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2018-12-01 About

Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline (Ethayarajh 2018)

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2018-10-08 About

Linear algebraic structure of word meanings – Off the convex path

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2018-09-20 About

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (2018)

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A La Carte Embedding

2018-09-18 About

Simple and efficient semantic embeddings for rare words, n-grams, and language features – Off the convex path

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2018-09-18 About

A Latent Variable Model Approach to PMI-based Word Embeddings (2016)

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2018-08-28 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

A Framework for Semi supervised Concept Extraction from MOOC content (2017)

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2018-08-12 About

Deep-learning-free Text and Sentence Embedding, Part 2 – Off the convex path

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2018-06-25 About

Deep-learning-free Text and Sentence Embedding, Part 1 – Off the convex path

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2018-06-25 About

Training Classifiers with Natural Language Explanations