Dense Passage Retrieval for Open-Domain Question Answering
- Thesis: use dense retrieval as opposed to sparse retrieval used in methods such as BM25/TF-IDF to retrieve relevant passages from provided context. The dense representation is learned using dual encoders (BERT) with questions and passages dataset
- Methodology:
- Train using in-batch question/answers where negative passages are simply all the answers for different questions in the batch
- Similarity function is just inner product of the dense vectors from question and passage encoders
- BERT-base-uncased is used as the encoder for both question and passage
- Contributions: Finetuning the retrieval on question/answer pairs outperform the best BM25 on ODQA datasets. Also, having high precision retrieval trained using DPR results in higher end-to-end QA systems training (only training the reader).
- Improvements:
- There is no interaction between question and passage(s)
- Training retrieval and reader is independent; can be seen as both positive and negative
- Notes:
- Large sparse representations obtained from models such as BM25 suffers from having difficulties of mapping question to passages if the words have similar meanings but are completely different. The learned representation is obtained from fine-tuning dual encoders (BERT) using small dataset (1000 of pairs of questions/answers)
#nlp #rag
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