Papers with sparse and dense representations
Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent approaches to information retrieval (IR) and natural language processing (NLP) use contextual language models, which can improve both synonymy and polysemy problems associated with words. |
| Approach: | They propose an ultra-high dimensional representation scheme equipped with directly controllable sparsity and a bucketing method where embeddings from multiple layers of BERT are selected/merged to represent diverse linguistic aspects. |
| Outcome: | The proposed representation scheme outperforms sparse models with MS MARCO and TREC CAR, and shows that it is highly efficient for storage and search. |
Generation-Augmented Retrieval for Open-Domain Question Answering (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to answer open-domain questions use sparse representations and sparsity. |
| Approach: | They propose a method which augments a query by generating relevant contexts from heuristically discovered contexts without external supervision. |
| Outcome: | The proposed approach outperforms state-of-the-art dense retrieval methods on natural questions and triviaQA datasets. |