Semanlink - Allen Institute for AI (AI2)

> some information needs require extracting
and aggregating sub-sentence information
(words, phrases, or entities) from multiple documents
(e.g. a list of all the risk factors for a specific
disease and their number of mentions, or a comprehensive
table of startups and CEOs).

> extractive search combines
document selection with information extraction. **The query is extended with capture slots**:
these are **search terms that act as variables, whose
values should be extracted**.
> The user
is then presented with the matched documents, each
annotated with the corresponding captured spans,
as well as aggregate information over the captured
spans

Conclusion :

> We presented a system for neural extractive search.
While we found our system to be useful for scientific
search, it also has clear limitations and areas
for improvement, both in terms of accuracy (only
72.2% of the returned results are relevant, both the
alignment and similarity models generalize well to
some relations but not to others), and in terms of
scale

[Video of demo](https://www.youtube.com/watch?v=TtqWi2GgB5A&t=1832s)

2021-06-23 About

[1807.04905] Ultra-Fine Entity Typing

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2021-06-22 About

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

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2021-02-08 About

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

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2020-12-01 About

Semantic Scholar | TLDR Feature

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2020-11-24 About

TL;DR: This AI summarizes research papers so you don’t have to

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2020-11-24 About

Semantic Scholar | AI-Powered Research Tool

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2020-11-24 About

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 About

[1906.07241] Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling

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2020-05-11 About

Isabel Cachola sur Twitter : "TLDR: Extreme Summarization of Scientific Documents"

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2020-05-03 About

[2004.05150] Longformer: The Long-Document Transformer

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2020-04-13 About

[2002.05867] Transformers as Soft Reasoners over Language

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2020-02-17 About

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

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2020-01-05 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

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

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