Julian Mcauley, Michel Galley, Bodhisattwa Prasad Majumder, Sudha Rao
We lifted 14 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 |
|---|---|---|
| microsoft/clarification-qgen-globalinfo | canonical | 8 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| build_schema_from_desc | Ran | microsoft/clarification-qgen-globalinfo/amazon/get_schema.py code served (permissive licence) · get_code("305f91dc9ae7c818") |
| build_schema_from_table | Ran | microsoft/clarification-qgen-globalinfo/amazon/get_schema.py code served (permissive licence) · get_code("6ded62d9787204e8") |
| clean_html | Ran | microsoft/clarification-qgen-globalinfo/amazon/baselines/create_seq2seq_data.py code served (permissive licence) · get_code("50991f7e06d740d2") |
| clf_predict | Ran | microsoft/clarification-qgen-globalinfo/amazon/predict_usefulness_score.py code served (permissive licence) · get_code("ceae444d11bd25e4") |
| feedfoward_regr_model | Ran | microsoft/clarification-qgen-globalinfo/amazon/question_ranker.py code served (permissive licence) · get_code("1840a84c3d80a3fa") |
| getDF | Ran | microsoft/clarification-qgen-globalinfo/amazon/create_split.py code served (permissive licence) · get_code("ccc731d1ce007e01") |
| load_glove_vectors | Ran | microsoft/clarification-qgen-globalinfo/amazon/global_schema_utils.py code served (permissive licence) · get_code("a38422b38b0a22c7") |
| svr_model | Ran | microsoft/clarification-qgen-globalinfo/amazon/question_ranker.py code served (permissive licence) · get_code("bb028603164d4abd") |
| build_schema | Not yet run | microsoft/clarification-qgen-globalinfo/amazon/get_schema.py code served (permissive licence) · get_code("1922d68fd519cffa") |
| create_keyword_embs | Not yet run | microsoft/clarification-qgen-globalinfo/amazon/global_schema_utils.py code served (permissive licence) · get_code("8fa821a1e0e2be65") |
| get_glove_vector | Not yet run | microsoft/clarification-qgen-globalinfo/amazon/predict_usefulness_score.py code served (permissive licence) · get_code("82bd4d8fb1a2c5b5") |
| get_glove_vector | Not yet run | microsoft/clarification-qgen-globalinfo/amazon/global_schema_utils.py code served (permissive licence) · get_code("139874e879dde9b0") |
| svc_model | Not yet run | microsoft/clarification-qgen-globalinfo/amazon/question_ranker.py code served (permissive licence) · get_code("78054b6fc43b80d3") |
| svc_model_predict | Not yet run | microsoft/clarification-qgen-globalinfo/amazon/predict_usefulness_score.py code served (permissive licence) · get_code("0091bc81c2e128ca") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous experience with similar contexts to form a global view and compare it to the given context to ascertain what is missing and what is useful in the context. Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it. Our model outperforms several baselines as judged by both automatic metrics and humans.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2104.06828")
get_code_for_paper("2104.06828")
have("2104.06828")
Connect an agent — have() is free.