Semanlink - Favoris ; Semantic technology AND Zero shot

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ACL 2019 - AllenNLP - Arxiv Doc - Benchmark - Discussed with NS - Discuté avec Raphaël - EMNLP 2021 - Entity Retrieval - Few-shot learning - Few-shot text classification - GitHub project - Hard negative mining - Hugging Face - Human in the loop - Information retrieval - Knowledge distillation - Knowledge Extraction - Knowledge Graph Completion - Language Model - Max Halford - Michael Glass - Natural Language Inference - Neural Search - Nils Reimers - NLP: pretraining - NLP@Facebook - NLP@IBM - NLP datasets - NLP sample code - Pre-Trained Language Models - Question Answering - Retriever-Reader - Self-training - Sentence Embeddings - Sequence-to-sequence learning - Simple idea - Slot filling - Text-Aware KG embedding - Text Classification - Tweet - Unsupervised machine learning - Using word embeddings - Yves Peirsman - Zero shot - Zero-Shot Learning - Zero-shot Text Classifier -

Favoris ; Semantic technology AND Zero shot

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27 Documents

[2304.02711] Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning

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2023-04-07 About

elvis sur Twitter : "NEW: Meta AI introduces OPT-IML, a large language model (175B) fine-tuned on 2000 NLP tasks. Uses instruction-tuning to improve zero-shot and few-shot generalization abilities...."

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2022-12-23 About

[2210.16637] Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations

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2022-11-25 About

Zshot: Zero and Few shot named entity & relationships recognition

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2022-10-01 About

[2207.06300] Re2G: Retrieve, Rerank, Generate

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2022-07-14 About

Devendra Singh Sachan sur Twitter : "...Unsupervised Passage Re-ranker (UPR), an approach to re-rank retrieved passages for information retrieval tasks."

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2022-04-18 About

[2203.06169] LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval

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2022-03-29 About

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

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2022-01-19 About

[2110.08207] Multitask Prompted Training Enables Zero-Shot Task Generalization

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2021-10-18 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

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

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2021-07-09 About

UKPLab/beir: A Heterogeneous Benchmark for Information Retrieval.

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2021-07-09 About

Nandan Thakur sur Twitter : "how to create sentence-embeddings when little or zero in-domain training data is available"

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2021-06-03 About

Adventures in Zero-Shot Text Classification

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2021-05-25 About

New pipeline for zero-shot text classification - 🤗Transformers - Hugging Face Forums

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2021-03-15 About

Distill our @huggingface zero-shot classifier with your specified class names

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2021-02-23 About

Zero-shot classifier distillation at master · huggingface/transformers

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2021-02-23 About

Zero-Shot Learning in Modern NLP | Joe Davison Blog (2020-05)

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2021-02-23 About

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

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2021-02-08 About

Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings - (ACL 2019)

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2020-10-05 About

Unsupervised text classification with word embeddings - Max Halford

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2020-10-05 About

Representation learning of knowledge graphs with entity descriptions (AAAI 2016)

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"Description-Embodied Knowledge Representation Learning" (DKRL)

> In most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods... Experimental results on real-world datasets show that, our method outperforms other baselines on the knowledge graph completion and entity classification tasks, especially under the zero-shot setting, which indicates that **our method is capable of building representations for novel entities according to their descriptions**. [Source code on github](https://github.com/xrb92/DKRL).

For fact triples: TransE. Meanwhile, given an entity we will also learn to maximize
the likelihood of predicting its description (using either CBOW or CNN encoder) (head + relation = tail, also in "text space")

Two types of representations for entities: structure-based
representations and description-based representations. They are learned simultaneously into the same
vector space but not forced to be unified **so that novel
entities with only descriptions can be represented**.

2020-10-02 About

Hugging Face sur Twitter : "No labeled data? No problem. The 🤗 Transformers master branch now includes a built-in pipeline for zero-shot text classification...

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2020-08-12 About

[2006.15020] Pre-training via Paraphrasing

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2020-06-30 About

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

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2020-05-02 About

Sebastian Riedel sur Twitter : Happy to introduce BLINK, the @facebookai open-source entity linker!...

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2020-05-02 About