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Found 13 papers, 10 papers with code
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection
We further find that gains provided by AL strategies come at the expense of more assessments (thus higher annotation costs) and AL strategies underperform random selection when comparing effectiveness given a fixed annotation cost.
Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation
In this paper, we introduce Ranger - a toolkit to facilitate the easy use of effect-size-based meta-analysis for multi-task evaluation in NLP and IR.
TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval
Robust test collections are crucial for Information Retrieval research.
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction
Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compared to classical approaches.
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval
However in the web domain we are in a setting with large amounts of training data and a query-to-passage or a query-to-document retrieval task.
Establishing Strong Baselines for TripClick Health Retrieval
We present strong Transformer-based re-ranking and dense retrieval baselines for the recently released TripClick health ad-hoc retrieval collection.
A Time-Optimized Content Creation Workflow for Remote Teaching
We describe our workflow to create an engaging remote learning experience for a university course, while minimizing the post-production time of the educators.
Description-based Label Attention Classifier for Explainable ICD-9 Classification
ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient's diagnosis and treatments are annotated with multiple ICD-9 codes.
DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval
We address this challenge by combining lexical and dense retrieval methods on the paragraph-level of the cases for the first stage retrieval.
Linguistically Informed Masking for Representation Learning in the Patent Domain
Domain-specific contextualized language models have demonstrated substantial effectiveness gains for domain-specific downstream tasks, like similarity matching, entity recognition or information retrieval.
Mitigating the Position Bias of Transformer Models in Passage Re-Ranking
In this work we analyze position bias on datasets, the contextualized representations, and their effect on retrieval results.
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility Study
For reproducibility and transparency as well as to benefit the community we make our source code and the trained models publicly available.
Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation
Based on this finding, we propose a cross-architecture training procedure with a margin focused loss (Margin-MSE), that adapts knowledge distillation to the varying score output distributions of different BERT and non-BERT passage ranking architectures.
