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Found 9 papers, 7 papers with code
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.
Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports
Obtaining such a corpus from crowdworkers, however, has been shown to be ineffective since (i) workers usually lack domain-specific expertise to conduct the task with sufficient quality, and (ii) the standard approach of annotating entire abstracts of trial reports as one task-instance (i. e. HIT) leads to an uneven distribution in task effort.
Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering
We extend the ranked retrieval annotations of the Deep Learning track of TREC 2019 with passage and word level graded relevance annotations for all relevant documents.
DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts
of 0. 68 to experts in DEXA vs. 0. 40 in CONTROL); (ii) already three per majority voting aggregated annotations of the DEXA approach reach substantial agreements to experts of 0. 78/0. 75/0. 69 for P/I/O (in CONTROL 0. 73/0. 58/0. 46).
Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking
In addition, to gain insight into TK, we perform a clustered query analysis of TK's results, highlighting its strengths and weaknesses on queries with different types of information need and we show how to interpret the cause of ranking differences of two documents by comparing their internal scores.
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations
While existing disease-symptom relationship extraction methods are used as the foundation in the various medical tasks, no collection is available to systematically evaluate the performance of such methods.
Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results
In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results.
TU Wien @ TREC Deep Learning '19 -- Simple Contextualization for Re-ranking
The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpretable at the same time.
