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Found 14 papers, 0 papers with code
Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task
And their experiments show that leveraging the answer summaries helps to attend the essential information in original lengthy answers and improve the answer selection performance under certain circumstances.
MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification
The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible.
Improving the Robustness of Large Language Models via Consistency Alignment
The training process is accomplished by self-rewards inferred from the trained model at the first stage without referring to external human preference resources.
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
In this paper, we propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results.
DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment
Dialogue assessment plays a critical role in the development of open-domain dialogue systems.
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.
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.
PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation
In this paper, we focus on two key questions in knowledge distillation for ranking models: 1) how to ensemble knowledge from multi-teacher; 2) how to utilize the label information of data in the distillation process.
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 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.
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.
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.
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.
First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities.
Ranked #8 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
