Passage Retrieval

60 papers with code • 2 benchmarks • 4 datasets

Passage retrieval is a specialized type of IR application that retrieves relevant passages (or pieces of text) rather than an entire ranked set of documents.

Libraries

Use these libraries to find Passage Retrieval models and implementations

Most implemented papers

Dense Passage Retrieval for Open-Domain Question Answering

facebookresearch/DPR EMNLP 2020

Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method.

Passage Re-ranking with BERT

nyu-dl/dl4marco-bert 13 Jan 2019

Recently, neural models pretrained on a language modeling task, such as ELMo (Peters et al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved impressive results on various natural language processing tasks such as question-answering and natural language inference.

Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

microsoft/ANCE ICLR 2021

In this paper, we identify that the main bottleneck is in the training mechanisms, where the negative instances used in training are not representative of the irrelevant documents in testing.

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

princeton-nlp/DensePhrases EACL 2021

Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge.

Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval

AdeDZY/DeepCT 23 Oct 2019

When applied to passages, DeepCT-Index produces term weights that can be stored in an ordinary inverted index for passage retrieval.

Overview of the TREC 2019 deep learning track

bmitra-msft/TREC-Deep-Learning-Quick-Start 17 Mar 2020

The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime.

Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently

jingtaozhan/DRhard 20 Oct 2020

Through this process, it teaches the DR model how to retrieve relevant documents from the entire corpus instead of how to rerank a potentially biased sample of documents.

Dealing with Typos for BERT-based Passage Retrieval and Ranking

ielab/typos-aware-bert EMNLP 2021

Our experimental results on the MS MARCO passage ranking dataset show that, with our proposed typos-aware training, DR and BERT re-ranker can become robust to typos in queries, resulting in significantly improved effectiveness compared to models trained without appropriately accounting for typos.

Robust Retrieval Augmented Generation for Zero-shot Slot Filling

ibm/kgi-slot-filling EMNLP 2021

Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI.

Slot Filling for Biomedical Information Extraction

ypapanik/biomedical-slot-filling BioNLP (ACL) 2022

In this work we present a slot filling approach to the task of biomedical IE, effectively replacing the need for entity and relation-specific training data, allowing us to deal with zero-shot settings.