Sohee Yang, Minjoon Seo
We lifted 5 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| clovaai/minimal-rnr-qa | canonical | 4 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| whitespace_tokenize | Ran | clovaai/minimal-rnr-qa/playground/workspace/minimal_rnr/tfserving/bert_tokenizer/tokenization_bert.py code served (permissive licence) · get_code("cf9ffa02a42184af") |
| get_first_matched_file_path | Ran | clovaai/minimal-rnr-qa/playground/workspace/minimal_rnr/utils/inference.py code served (permissive licence) · get_code("4f19f0dd0d6275a1") |
| load_vocab | Ran | clovaai/minimal-rnr-qa/playground/workspace/minimal_rnr/tfserving/bert_tokenizer/tokenization_bert.py code served (permissive licence) · get_code("e7fbc7a74a3457c7") |
| read_txt | Ran | clovaai/minimal-rnr-qa/playground/workspace/minimal_rnr/utils/inference.py code served (permissive licence) · get_code("fe09325ee5e2d803") |
| extend_span_to_full_words | Not yet run | clovaai/minimal-rnr-qa/playground/workspace/minimal_rnr/utils/inference.py code served (permissive licence) · get_code("9d264e3b0a582677") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In open-domain question answering (QA), retrieve-and-read mechanism has the inherent benefit of interpretability and the easiness of adding, removing, or editing knowledge compared to the parametric approaches of closedbook QA models. However, it is also known to suffer from its large storage footprint due to its document corpus and index. Here, we discuss several orthogonal strategies to drastically reduce the footprint of a retrieve-andread open-domain QA system by up to 160x. Our results indicate that retrieve-and-read can be a viable option even in a highly constrained serving environment such as edge devices, as we show that it can achieve better accuracy than a purely parametric model with comparable docker-level system size. 1
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2104.07242")
get_code_for_paper("2104.07242")
have("2104.07242")
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