Search Results for author: Nils Reimers

Found 27 papers, 23 papers with code

DAPR: A Benchmark on Document-Aware Passage Retrieval

1 code implementation23 May 2023 Kexin Wang, Nils Reimers, Iryna Gurevych

To fill this gap, we propose and name this task Document-Aware Passage Retrieval (DAPR) and build a benchmark including multiple datasets from various domains, covering both DAPR and whole-document retrieval.

Passage Retrieval Retrieval

Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking

1 code implementation19 Oct 2022 Tim Baumgärtner, Leonardo F. R. Ribeiro, Nils Reimers, Iryna Gurevych

Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets.

Argument Retrieval Information Retrieval +4

Efficient Few-Shot Learning Without Prompts

1 code implementation22 Sep 2022 Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo, Luke Bates, Daniel Korat, Moshe Wasserblat, Oren Pereg

This simple framework requires no prompts or verbalizers, and achieves high accuracy with orders of magnitude less parameters than existing techniques.

Few-Shot Learning

Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense Retrieval

2 code implementations23 May 2022 Nandan Thakur, Nils Reimers, Jimmy Lin

In our work, we evaluate LTH and vector compression techniques for improving the downstream zero-shot retrieval accuracy of the TAS-B dense retriever while maintaining efficiency at inference.

Ad-Hoc Information Retrieval Information Retrieval +3

UKP-SQUARE: An Online Platform for Question Answering Research

1 code implementation ACL 2022 Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva, Max Eichler, Gregor Geigle, Clifton Poth, Hannah Sterz, Haritz Puerto, Leonardo F. R. Ribeiro, Jonas Pfeiffer, Nils Reimers, Gözde Gül Şahin, Iryna Gurevych

Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e. g., extractive, abstractive), require different model architectures (e. g., generative, discriminative), and setups (e. g., with or without retrieval).

Explainable Models Information Retrieval +2

BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

2 code implementations17 Apr 2021 Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych

To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval.

Argument Retrieval Benchmarking +12

Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval

1 code implementation22 Mar 2021 Gregor Geigle, Jonas Pfeiffer, Nils Reimers, Ivan Vulić, Iryna Gurevych

Current state-of-the-art approaches to cross-modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image.

Cross-Modal Retrieval Retrieval

The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes

no code implementations ACL 2021 Nils Reimers, Iryna Gurevych

Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25.

Information Retrieval Retrieval

Generalizing Cross-Document Event Coreference Resolution Across Multiple Corpora

1 code implementation CL (ACL) 2021 Michael Bugert, Nils Reimers, Iryna Gurevych

This raises strong concerns on their generalizability -- a must-have for downstream applications where the magnitude of domains or event mentions is likely to exceed those found in a curated corpus.

coreference-resolution Event Coreference Resolution

AdapterDrop: On the Efficiency of Adapters in Transformers

1 code implementation EMNLP 2021 Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, Iryna Gurevych

Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements.

Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

10 code implementations EMNLP 2020 Nils Reimers, Iryna Gurevych

The training is based on the idea that a translated sentence should be mapped to the same location in the vector space as the original sentence.

Knowledge Distillation Sentence Embedding +1

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

53 code implementations IJCNLP 2019 Nils Reimers, Iryna Gurevych

However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10, 000 sentences requires about 50 million inference computations (~65 hours) with BERT.

Clustering Linear-Probe Classification +5

Alternative Weighting Schemes for ELMo Embeddings

1 code implementation5 Apr 2019 Nils Reimers, Iryna Gurevych

We evaluate different methods that combine the three vectors from the language model in order to achieve the best possible performance in downstream NLP tasks.

Language Modelling Word Embeddings

Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches

1 code implementation26 Mar 2018 Nils Reimers, Iryna Gurevych

In this publication, we show that there is a high risk that a statistical significance in this type of evaluation is not due to a superior learning approach.

BIG-bench Machine Learning NER

Event Time Extraction with a Decision Tree of Neural Classifiers

no code implementations TACL 2018 Nils Reimers, Nazanin Dehghani, Iryna Gurevych

We use this tree to incrementally infer, in a stepwise manner, at which time frame an event happened.

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

5 code implementations EMNLP 2017 Nils Reimers, Iryna Gurevych

In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches.

Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

6 code implementations21 Jul 2017 Nils Reimers, Iryna Gurevych

Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.

Chunking Event Detection +3

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