Semanlink - Question Answering ; NLP@Google AND Transformers

1. synthetic queries
are generated for each passage from the target corpus (using an existing pre-trained [T5](tag:text_to_text_transfer_transformer)
encoder-decoder)
2. the generated queries are used for mining negative
passages (retrieving the most similar
paragraphs using an existing dense retrieval
model == hard negatives!)
3. the query-passage pairs are labeled by a cross-encoder and used to train the domain-adapted
dense retriever (using method described in [Hofstätter et al.,
2020](doc:2021/12/2010_02666_improving_efficien))

[Nils Reimers sur Twitter](doc:2021/12/nils_reimers_sur_twitter_do_), [GitHub](https://github.com/UKPLab/gpl),  by the author of [TSDAE](doc:2021/09/2104_06979_tsdae_using_trans)

Claims to improve "Doc2Query" [Document Expansion by Query Prediction](doc:2022/01/1904_08375_document_expansion): ([src](https://twitter.com/KexinWang2049/status/1471435779415150598))

> - GPL: Uses doc2query to construct synthetic data and does knowledge distillation (i.e. training) on that data.
> - Doc2query: Generates queries to extend the documents and use BM25 on top of them w/o training.

2021-12-15 About

How to Fine-Tune Sentence-BERT for Question Answering | Capital One

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2021-11-21 About

Which flavor of BERT should you use for your QA task? | by Olesya Bondarenko | Towards Data Science

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2020-10-04 About

[2004.07202] Entities as Experts: Sparse Memory Access with Entity Supervision

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2020-07-11 About

Hugging Face sur Twitter : DistilBERT-cased for Question Answering w/ just 3 lines of javascript

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2020-02-14 About