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Found 8 papers, 5 papers with code
IR2: Information Regularization for Information Retrieval
This approach, representing a novel application of regularization techniques in synthetic data creation for IR, is tested on three recent IR tasks characterized by complex queries: DORIS-MAE, ArguAna, and WhatsThatBook.
BIRCO: A Benchmark of Information Retrieval Tasks with Complex Objectives
We present the Benchmark of Information Retrieval (IR) tasks with Complex Objectives (BIRCO).
IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models
Then, we pair each sample with a bias indicator representing its bias degree, and use these extended samples to train a sample generator.
DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries
In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical.
A Simple yet Effective Self-Debiasing Framework for Transformer Models
In this way, the top-layer sentence representation will be trained to ignore the common biased features encoded by the low-layer sentence representation and focus on task-relevant unbiased features.
Getting the Most out of Simile Recognition
Simile recognition involves two subtasks: simile sentence classification that discriminates whether a sentence contains simile, and simile component extraction that locates the corresponding objects (i. e., tenors and vehicles).
