Shuo Zhang, Jiancheng Lv, Chao Wang, Tat-Seng Chua, Youcheng Huang, Fuli Feng, Wenqiang Lei, Fengbin Zhu
We lifted 12 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 |
|---|---|---|
| NExTplusplus/TAT-QA | canonical | 8 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| create_logger | Ran | NExTplusplus/TAT-QA/tag_op/tagop/util.py code served (permissive licence) · get_code("ea74b65463265ba2") |
| extract_gold_answers | Ran | NExTplusplus/TAT-QA/tatqa_metric.py code served (permissive licence) · get_code("2be876d386453689") |
| get_continuous_tag_slots | Ran | NExTplusplus/TAT-QA/tag_op/tagop/modeling_tagop.py code served (permissive licence) · get_code("f39958953701ff98") |
| get_span_tokens_from_paragraph | Ran | NExTplusplus/TAT-QA/tag_op/tagop/modeling_tagop.py code served (permissive licence) · get_code("43ec29e1df123bda") |
| get_span_tokens_from_table | Ran | NExTplusplus/TAT-QA/tag_op/tagop/modeling_tagop.py code served (permissive licence) · get_code("275419aee12c926d") |
| is_number | Ran | NExTplusplus/TAT-QA/tatqa_utils.py code served (permissive licence) · get_code("1a0411f2ec690e26") |
| warmup_constant | Ran | NExTplusplus/TAT-QA/tag_op/tagop/optimizer.py code served (permissive licence) · get_code("e7d542062316094a") |
| warmup_linear | Ran | NExTplusplus/TAT-QA/tag_op/tagop/optimizer.py code served (permissive licence) · get_code("c58d57224530d17e") |
| extract_one_num_from_str | Not yet run | NExTplusplus/TAT-QA/tatqa_utils.py code served (permissive licence) · get_code("b591a51b9d4dd3bb") |
| metric_max_over_ground_truths | Not yet run | NExTplusplus/TAT-QA/tatqa_metric.py code served (permissive licence) · get_code("03692148eacb9928") |
| scale_to_num | Not yet run | NExTplusplus/TAT-QA/tatqa_utils.py code served (permissive licence) · get_code("71b0d5b420628a19") |
| warmup_cosine | Not yet run | NExTplusplus/TAT-QA/tag_op/tagop/optimizer.py code served (permissive licence) · get_code("35f7cddf90dd05d4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Hybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is largely neglected in existing research. In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and their compositions. We further propose a novel QA model termed TAGOP, which is capable of reasoning over both tables and text. It adopts sequence tagging to extract relevant cells from the table along with relevant spans from the text to infer their semantics, and then applies symbolic reasoning over them with a set of aggregation operators to arrive at the final answer. TAGOP achieves 58.0% in F 1 , which is an 11.1% absolute increase over the previous best baseline model, according to our experiments on TAT-QA. But this result still lags far behind the performance of human expert, i.e. 90.8% in F 1 . It demonstrates that our TAT-QA is very challenging and can serve as a benchmark for training and testing powerful QA models that address hybrid data. Our dataset is publicly available for noncommercial use at https://nextplusplus. github.io/TAT-QA/.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2105.07624")
get_code_for_paper("2105.07624")
have("2105.07624")
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