https://github.com/mia-workshop/mia-shared-task-2022
Latent Retrieval for Weakly Supervised Open Domain Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Information retrieval is the task of ranking a list of documents or search results in response to a query
<span style="color:grey; opacity: 0.6">( Image credit: sudhanshumittal )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Latent Retrieval for Weakly Supervised Open Domain Question Answering
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
LLF-Bench: Benchmark for Interactive Learning from Language Feedback
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
A Critical Review of Recurrent Neural Networks for Sequence Learning
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
Marvin: Semantic annotation using multiple knowledge sources
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Open-Domain Question Answering Goes Conversational via Question Rewriting
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
Latent Retrieval for Weakly Supervised Open Domain Question Answering
Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation
Science Checker Reloaded: A Bidirectional Paradigm for Transparency and Logical Reasoning
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Query-dominant User Interest Network for Large-Scale Search Ranking
Declarative Experimentation in Information Retrieval using PyTerrier
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.