Papers with Passage retrieval

3 papers
Joint Inference of Retrieval and Generation for Passage Re-ranking (2024.findings-eacl)

Copied to clipboard

Challenge: Existing methods for re-ranking documents are sparse and do not require training.
Approach: They propose a method that optimizes mutual information between query and passage distributions by integrating cross-encoders and generative models in the re-ranking process.
Outcome: The proposed method outperforms conventional re-rankers and language model scorers in open-domain QA retrieval settings and diverse retrieval benchmarks under zero-shot settings.
Dealing with Typos for BERT-based Passage Retrieval and Ranking (2021.emnlp-main)

Copied to clipboard

Challenge: Current approaches to passage retrieval and ranking rely on pre-trained deep language models that model the semantic matching between queries and passages.
Approach: They propose a typos-aware training framework for DR and BERT to address this issue.
Outcome: The proposed models respond and adapt to keyword typos occurring in queries, and significantly improve their retrieval and ranking effectiveness.
A Deep Metric Learning Method for Biomedical Passage Retrieval (2020.coling-main)

Copied to clipboard

Challenge: Existing methods for passage retrieval are based on metric learning . the proposed approach is particularly well suited for domain-specific passage retrievals where it is very important to take into account different sources of information.
Approach: They propose a method that learns a metric for questions and passages based on their internal semantic interactions.
Outcome: The proposed method outperforms triplet loss and state-of-the-art methods in a biomedical passage retrieval task and outperformed triplet losses by 10% and 26%.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations