Ruslan Salakhutdinov, Yoshua Bengio, William Cohen, Christopher Manning, Peng Qi, Zhilin Yang, Saizheng Zhang
We lifted 8 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| hotpotqa/hotpot | canonical | 8 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| exact_match_score | Ran | hotpotqa/hotpot/hotpot_evaluate_v1.py code served (permissive licence) · get_code("9d0dc82a4491f803") |
| f1_score | Ran | hotpotqa/hotpot/hotpot_evaluate_v1.py code served (permissive licence) · get_code("32a5f1733d9a8971") |
| find_nearest | Ran | hotpotqa/hotpot/prepro.py code served (permissive licence) · get_code("5733fa44ca6deb1f") |
| fix_span | Ran | hotpotqa/hotpot/prepro.py code served (permissive licence) · get_code("40e9cb6c8ca7fb3d") |
| get_buckets | Ran | hotpotqa/hotpot/util.py code served (permissive licence) · get_code("ffc8a36cce91c106") |
| has_digit | Ran | hotpotqa/hotpot/util.py code served (permissive licence) · get_code("2be785f92fd075df") |
| normalize_answer | Ran | hotpotqa/hotpot/hotpot_evaluate_v1.py code served (permissive licence) · get_code("dae7ab386661a4f4") |
| prepro | Ran | hotpotqa/hotpot/util.py code served (permissive licence) · get_code("c15e3bee3bb007b7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HOTPOTQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems' ability to extract relevant facts and perform necessary comparison. We show that HOTPOTQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions. * These authors contributed equally. The order of authorship is decided through dice rolling. † Work done when WWC was at CMU.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1809.09600")
get_code_for_paper("1809.09600")
have("1809.09600")
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