Semanlink - Information retrieval ; Machine learning: problems AND Arxiv Doc

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Information retrieval ; Machine learning: problems AND Arxiv Doc

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27 Documents

[2103.05256] CEQE: Contextualized Embeddings for Query Expansion

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2023-10-28 About

[2309.06131] Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection

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2023-09-14 About

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 About

[2304.09848] Evaluating Verifiability in Generative Search Engines

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2023-04-23 About

[2205.11498] Domain Adaptation for Memory-Efficient Dense Retrieval

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2022-09-26 About

[2207.06300] Re2G: Retrieve, Rerank, Generate

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2022-07-14 About

[2205.00820] Entity-aware Transformers for Entity Search

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2022-07-12 About

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

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2022-01-19 About

[2112.09118] Towards Unsupervised Dense Information Retrieval with Contrastive Learning

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2021-12-21 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

[2109.08133] Phrase Retrieval Learns Passage Retrieval, Too

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2021-09-30 About

[2007.12603] IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles

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2021-04-12 About

[1901.04085] Passage Re-ranking with BERT

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2021-03-26 About

[2007.00077] Similarity Search for Efficient Active Learning and Search of Rare Concepts

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2020-07-02 About

[2004.06842] Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph

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2020-04-17 About

[1911.02168] CoKE: Contextualized Knowledge Graph Embedding

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2020-03-22 About

[1910.04126] Scalable Nearest Neighbor Search for Optimal Transport

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2020-02-20 About

[1902.10909] BERT for Joint Intent Classification and Slot Filling

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2020-01-09 About

[1910.03524] Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

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2019-10-09 About

[1908.10084] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

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2019-08-28 About

[1602.01137] A Dual Embedding Space Model for Document Ranking

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2019-07-17 About

[1806.04411] Named Entity Recognition with Extremely Limited Data

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2019-04-11 About

[1704.08803] Neural Ranking Models with Weak Supervision

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2019-01-27 About

[1703.03129] Learning to Remember Rare Events

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2018-10-23 About

[1710.04099] Wembedder: Wikidata entity embedding web service

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2018-02-13 About

[1607.00570] Representation learning for very short texts using weighted word embedding aggregation

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2017-06-09 About

[1512.00765] Learning Semantic Similarity for Very Short Texts

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2017-06-09 About