Reranking
213 papers with code • 0 benchmarks • 1 datasets
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Most implemented papers
Facebook FAIR's WMT19 News Translation Task Submission
huggingface/transformers
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WS 2019
This paper describes Facebook FAIR's submission to the WMT19 shared news translation task.
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
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.
MTEB: Massive Text Embedding Benchmark
MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages.
The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models
We propose a design pattern for tackling text ranking problems, dubbed "Expando-Mono-Duo", that has been empirically validated for a number of ad hoc retrieval tasks in different domains.
Faster R-CNN Features for Instance Search
imatge-upc/retrieval-2016-deepvision
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This work explores the suitability for instance retrieval of image- and region-wise representations pooled from an object detection CNN such as Faster R-CNN.
Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval
terrierteam/pyterrier_colbert
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In particular, based on the pseudo-relevant set of documents identified using a first-pass dense retrieval, we extract representative feedback embeddings (using KMeans clustering) -- while ensuring that these embeddings discriminate among passages (based on IDF) -- which are then added to the query representation.
A Temporal Variational Model for Story Generation
dwlmt/knowledgeable-stories
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Recent language models can generate interesting and grammatically correct text in story generation but often lack plot development and long-term coherence.
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
makcedward/nlpaug
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EMNLP 2021
We study generating abstractive summaries that are faithful and factually consistent with the given articles.
RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
Researchers have successfully applied large language models (LLMs) such as ChatGPT to reranking in an information retrieval context, but to date, such work has mostly been built on proprietary models hidden behind opaque API endpoints.
