태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The MSLR-WEB10K dataset consists of 10,000 search queries over the documents from search results. The data also contains the values of 136 features and a corresponding user-labeled relevance factor on a scale of one to five with respect to each query-document pair. It is a subset of the MSLR-WEB30K dataset. Source: Dueling Bandits with Qualitative Feedback
The MQ2007 dataset consists of queries, corresponding retrieved documents and labels provided by human experts. The possible relevance labels for each document are “relevant”, “partially relevant”, and “not relevant”. Source: ARSM GRADIENT ESTIMATOR FOR SUPERVISED LEARNING TO RANK
The MQ2008 dataset is a dataset for Learning to Rank. It contains 800 queries with labelled documents.
The Yahoo! Learning to Rank Challenge dataset consists of 709,877 documents encoded in 700 features and sampled from query logs of the Yahoo! search engine, spanning 29,921 queries. Source: Ranking for Relevance and Display Preferencesin Complex Presentation Layouts
Publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. Source: MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
Retrieval Question-Answering (ReQA) benchmark tests a model’s ability to retrieve relevant answers efficiently from a large set of documents. Source: ReQA: An Evaluation for End-to-End Answer Retrieval Models
ART consists of over 20k commonsense narrative contexts and 200k explanations.
The Flick Cropping Dataset consists of high quality cropping and pairwise ranking annotations used to evaluate the performance of automatic image cropping approaches. Source: https://arxiv.org/abs/1701.01480
Consists of English-French sentence-pairs annotated with semantic divergence classes and token-level rationales. Source: Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank
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