Search Results for author:
Found 27 papers, 23 papers with code
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning
Learning sentence embeddings often requires a large amount of labeled data.
DAPR: A Benchmark on Document-Aware Passage Retrieval
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.
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking
Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets.
MTEB: Massive Text Embedding Benchmark
MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages.
Ranked #1 on
Text Clustering
on MTEB
Efficient Few-Shot Learning Without Prompts
This simple framework requires no prompts or verbalizers, and achieves high accuracy with orders of magnitude less parameters than existing techniques.
Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense Retrieval
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.
UKP-SQUARE: An Online Platform for Question Answering Research
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).
GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval
This limits the usage of dense retrieval approaches to only a few domains with large training datasets.
Ranked #9 on
Zero-shot Text Search
on BEIR
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
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.
Ranked #1 on
Argument Retrieval
on ArguAna (BEIR)
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning
Learning sentence embeddings often requires a large amount of labeled data.
Ranked #1 on
Paraphrase Identification
on TURL
TWEAC: Transformer with Extendable QA Agent Classifiers
We argue that there exist a wide range of specialized QA agents in literature.
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval
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.
The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes
Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25.
Generalizing Cross-Document Event Coreference Resolution Across Multiple Corpora
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.
AdapterDrop: On the Efficiency of Adapters in Transformers
Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements.
Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks
Bi-encoders, on the other hand, require substantial training data and fine-tuning over the target task to achieve competitive performance.
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
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.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
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.
Ranked #5 on
Semantic Textual Similarity
on SICK
Classification and Clustering of Arguments with Contextualized Word Embeddings
We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search.
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution
Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model's success.
coreference-resolution
Cross Document Coreference Resolution
+3
Alternative Weighting Schemes for ELMo Embeddings
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.
Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches
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.
Event Time Extraction with a Decision Tree of Neural Classifiers
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
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
Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.
Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity
Semantic Textual Similarity (STS) is a foundational NLP task and can be used in a wide range of tasks.
