Semanlink - Arxiv Doc AND Information retrieval

> **approach for training dense retrieval models that does not require any labeled training data**. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negative mining and denoising of positive examples.
>
> ART, in contrast, only requires access to unpaired inputs and outputs (e.g. questions and potential answer documents).
>
> It uses a new document-retrieval autoencoding scheme, where
> 1. an input question is used to retrieve a set of evidence documents, and
> 2. the documents are then used to compute the probability of reconstructing the original question.
>
> Training for retrieval based on question reconstruction enables effective unsupervised learning of both document and question encoders, which can be later incorporated into complete Open QA systems without any further finetuning.

[Tweet](doc:2022/07/devendra_singh_sachan_sur_twitt)

> Given an
input question, ART first retrieves a small set
of possible evidences documents. It then recon
structs
the original question by attending to these
documents
>
> The
key idea in ART is to consider the retrieved documents
as a noisy representation of the original
question and question reconstruction probability
as a way of denoising that provides soft-labels for
how likely each document is to have been the correct
result

Refers to [[IZACARD 2012.04584] Distilling Knowledge from Reader to Retriever for Question Answering](doc:2020/12/2012_04584_distilling_knowled)

2022-07-06 A propos

[2203.08913] Memorizing Transformers

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2022-05-07 A propos

[2203.13088] Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction

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2022-03-30 A propos

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

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2022-03-29 A propos

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

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2022-01-19 A propos

[2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

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2022-01-19 A propos

[2004.12832] ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

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2022-01-12 A propos

[2007.00814] Relevance-guided Supervision for OpenQA with ColBERT

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2022-01-07 A propos

[1904.08375] Document Expansion by Query Prediction

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2022-01-05 A propos

[2112.09118] Towards Unsupervised Dense Information Retrieval with Contrastive Learning

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2021-12-21 A propos

[2010.02666] Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation

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2021-12-16 A propos

[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 A propos

[2112.01488] ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

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2021-12-05 A propos

[2109.08133] Phrase Retrieval Learns Passage Retrieval, Too

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2021-09-30 A propos

[2010.06467] Pretrained Transformers for Text Ranking: BERT and Beyond

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2021-07-09 A propos

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

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2021-07-09 A propos

[2106.04612] Neural Extractive Search

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how to extend a
search paradigm we call “**extractive search**” with
neural similarity techniques.

> 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 A propos

[2106.00882] Efficient Passage Retrieval with Hashing for Open-domain Question Answering

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2021-06-03 A propos

[2004.04906] Dense Passage Retrieval for Open-Domain Question Answering

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2021-06-03 A propos

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

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2021-04-12 A propos

[1901.04085] Passage Re-ranking with BERT

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2021-03-26 A propos

[2002.08909] REALM: Retrieval-Augmented Language Model Pre-Training

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**Augment language model pre-training with a retriever module**, which
is trained using the masked language modeling objective.

> To capture knowledge in a more modular and interpretable way, we augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend over documents from a large corpus such as Wikipedia, used during pre-training, fine-tuning and inference. **For the first time, we show how to pre-train such a knowledge retriever in an unsupervised manner**, using masked language modeling as the learning signal and backpropagating through a retrieval step that considers millions of documents

Hum, #TODO: parallel to be drawn with techniques in [KG-augmented Language Models](tag:knowledge_graph_augmented_language_models) which focus "on the problem of capturing declarative knowledge in the learned parameters of a language model."

[Google AI Blog Post](doc:2020/08/google_ai_blog_realm_integrat)

[Summary](https://joeddav.github.io/blog/2020/03/03/REALM.html) for the [Hugging Face awesome-papers reading group](doc:2021/03/huggingface_awesome_papers_pap)

2020-12-12 A propos

[2012.04584] Distilling Knowledge from Reader to Retriever for Question Answering

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> a method to train an information retrieval module for downstream tasks, **without using pairs of queries and documents as annotations**.

Uses two models (standard pipeline for open-domain QA):

- the first one retrieves documents from a large source of knowledge (the retriever)
- the second one processes the support documents to solve the task (the reader).

> First the retriever selects support passages in a large knowledge
source. Then these passages are processed by the reader, along with the question, to generate an
answer

Inspired by knowledge distillation: the reader model is the teacher and the retriever is the student.

> More precisely, we use a sequence-to-sequence model as the reader, and use
the attention activations over the input documents as synthetic labels to train the retriever. 
> (**train the retriever by learning to approximate the attention score of the reader**)

Refers to:

- [REALM: Retrieval-Augmented Language Model Pre-Training](doc:2020/12/2002_08909_realm_retrieval_a)
- [Dehghani: Neural Ranking Models with Weak Supervision](doc:?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1704.08803)

2020-12-11 A propos

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