Argument Retrieval
7 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Argument Retrieval
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BEIR
Webis-Touché-2020
Most implemented papers
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
UKPLab/beir
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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.
Evaluating Fairness in Argument Retrieval
In this work, we analyze a range of non-stochastic fairness-aware ranking and diversity metrics to evaluate the extent to which argument stances are fairly exposed in argument retrieval systems.
Fine-Grained Argument Unit Recognition and Classification
trtm/AURC
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In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling.
Diversity Aware Relevance Learning for Argument Search
fromm-m/ecir2021-am-search
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In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects.
SGPT: GPT Sentence Embeddings for Semantic Search
muennighoff/sgpt
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To this end, we propose SGPT to use decoders for sentence embeddings and semantic search via prompting or fine-tuning.
No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
This has made distilled and dense models, due to latency constraints, the go-to choice for deployment in real-world retrieval applications.
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking
ukplab/incorporating-relevance
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Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets.
