Semanlink - Synthetic data (NLP) AND Arxiv Doc

[2205.11498] Domain Adaptation for Memory-Efficient Dense Retrieval

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2022-09-26 About

[1807.00745] Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

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2022-07-18 About

[2004.11892] Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering

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2022-02-11 About

[2112.07577] GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

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An unsupervised domain adaptation technique for dense retrieval models

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

[1909.06356] Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering

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2021-12-08 About

[1906.04980] Unsupervised Question Answering by Cloze Translation

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2021-12-08 About

[2108.13854] Contrastive Domain Adaptation for Question Answering using Limited Text Corpora

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2021-11-19 About