We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| shirley-wu/text_to_table | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| convert_mr_to_table | Ran | shirley-wu/text_to_table/data_preprocessing/preprocess_e2e.py code served (permissive licence) · get_code("f483fb76a75970fc") |
| fix_key | Ran | shirley-wu/text_to_table/data_preprocessing/preprocess_e2e.py code served (permissive licence) · get_code("47bba1c3faf3edcd") |
| read_dataset | Ran | shirley-wu/text_to_table/data_preprocessing/preprocess_e2e.py code served (permissive licence) · get_code("3c9701a9b37d913c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study a new problem setting of information extraction (IE), referred to as text-to-table. In text-to-table, given a text, one creates a table or several tables expressing the main content of the text, while the model is learned from text-table pair data. The problem setting differs from those of the existing methods for IE. First, the extraction can be carried out from long texts to large tables with complex structures. Second, the extraction is entirely data-driven, and there is no need to explicitly define the schemas. As far as we know, there has been no previous work that studies the problem. In this work, we formalize text-to-table as a sequence-to-sequence (seq2seq) problem. We first employ a seq2seq model fine-tuned from a pre-trained language model to perform the task. We also develop a new method within the seq2seq approach, exploiting two additional techniques in table generation: table constraint and table relation embeddings. We consider text-to-table as an inverse problem of the well-studied table-to-text, and make use of four existing table-to-text datasets in our experiments on text-to-table. Experimental results show that the vanilla seq2seq model can outperform the baseline methods of using relation extraction and named entity extraction. The results also show that our method can further boost the performances of the vanilla seq2seq model. We further discuss the main challenges of the proposed task. The code and data are available at https://github.com/shirley-wu/text_to_table.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2109.02707")
get_code_for_paper("2109.02707")
have("2109.02707")
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