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Found 9 papers, 2 papers with code
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.
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.
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.
Text Level Graph Neural Network for Text Classification
Recently, researches have explored the graph neural network (GNN) techniques on text classification, since GNN does well in handling complex structures and preserving global information.
Ranked #2 on
Text Classification
on Ohsumed
Exploring Sequence-to-Sequence Learning in Aspect Term Extraction
Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem.
Ranked #1 on
Term Extraction
on SemEval 2014 Task 4 Laptop
Joint Learning for Targeted Sentiment Analysis
Targeted sentiment analysis (TSA) aims at extracting targets and classifying their sentiment classes.
Cascading Multiway Attentions for Document-level Sentiment Classification
Document-level sentiment classification aims to assign the user reviews a sentiment polarity.
Ranked #5 on
Sentiment Analysis
on User and product information
Interactive Attention Networks for Aspect-Level Sentiment Classification
In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning.
