Search Results for author:
Found 26 papers, 18 papers with code
Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models
However, current works in this direction all depend on the GPT models, making it a single point of failure in scientific reproducibility.
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.
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation
Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an updateable knowledge base.
TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval
Robust test collections are crucial for Information Retrieval research.
Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling
This paper studies multi-task training of retrieval-augmented generation models for knowledge-intensive tasks.
Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems
Recently, several dense retrieval (DR) models have demonstrated competitive performance to term-based retrieval that are ubiquitous in search systems.
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.
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.
Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking
An emerging recipe for achieving state-of-the-art effectiveness in neural document re-ranking involves utilizing large pre-trained language models - e. g., BERT - to evaluate all individual passages in the document and then aggregating the outputs by pooling or additional Transformer layers.
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout the training, indexing and query workflows.
Ranked #15 on
Zero-shot Text Search
on BEIR
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.
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).
Local Self-Attention over Long Text for Efficient Document Retrieval
In this work, we propose a local self-attention which considers a moving window over the document terms and for each term attends only to other terms in the same window.
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.
Let's measure run time! Extending the IR replicability infrastructure to include performance aspects
Establishing a docker-based replicability infrastructure offers the community a great opportunity: measuring the run time of information retrieval systems.
On the Effect of Low-Frequency Terms on Neural-IR Models
Low-frequency terms are a recurring challenge for information retrieval models, especially neural IR frameworks struggle with adequately capturing infrequently observed words.
