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Found 17 papers, 3 papers with code
I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval
Moreover, the proposed implicit interaction is compatible with special pre-training and knowledge distillation for passage retrieval, which brings a new state-of-the-art performance.
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction
In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner.
Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning.
Boosting Event Extraction with Denoised Structure-to-Text Augmentation
Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations.
Learning to Tokenize for Generative Retrieval
As an alternative, generative retrieval represents documents as identifiers (docid) and retrieves documents by generating docids, enabling end-to-end modeling of document retrieval tasks.
Layout-aware Webpage Quality Assessment
To assess webpage quality from complex DOM tree data, we propose a graph neural network (GNN) based method that extracts rich layout-aware information that implies webpage quality in an end-to-end manner.
Approximated Doubly Robust Search Relevance Estimation
We further instantiate the proposed unbiased relevance estimation framework in Baidu search, with comprehensive practical solutions designed regarding the data pipeline for click behavior tracking and online relevance estimation with an approximated deep neural network.
A Large Scale Search Dataset for Unbiased Learning to Rank
The unbiased learning to rank (ULTR) problem has been greatly advanced by recent deep learning techniques and well-designed debias algorithms.
Geometry Contrastive Learning on Heterogeneous Graphs
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data.
A Simple yet Effective Framework for Active Learning to Rank
To handle the diverse query requests from users at web-scale, Baidu has done tremendous efforts in understanding users' queries, retrieve relevant contents from a pool of trillions of webpages, and rank the most relevant webpages on the top of results.
ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval
Our method 1) introduces a self on-the-fly distillation method that can effectively distill late interaction (i. e., ColBERT) to vanilla dual-encoder, and 2) incorporates a cascade distillation process to further improve the performance with a cross-encoder teacher.
Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking
To leverage a reliable knowledge, we propose a novel knowledge graph distillation method and obtain a knowledge meta graph as the bridge between query and passage.
Graph Enhanced BERT for Query Understanding
In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries.
Pre-trained Language Model for Web-scale Retrieval in Baidu Search
More importantly, we present a practical system workflow for deploying the model in web-scale retrieval.
Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation
Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness.
Pre-trained Language Model based Ranking in Baidu Search
However, it is nontrivial to directly apply these PLM-based rankers to the large-scale web search system due to the following challenging issues:(1) the prohibitively expensive computations of massive neural PLMs, especially for long texts in the web-document, prohibit their deployments in an online ranking system that demands extremely low latency;(2) the discrepancy between existing ranking-agnostic pre-training objectives and the ad-hoc retrieval scenarios that demand comprehensive relevance modeling is another main barrier for improving the online ranking system;(3) a real-world search engine typically involves a committee of ranking components, and thus the compatibility of the individually fine-tuned ranking model is critical for a cooperative ranking system.
Etymo: A New Discovery Engine for AI Research
We present Etymo (https://etymo. io), a discovery engine to facilitate artificial intelligence (AI) research and development.
