A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram
Paper • 2302.11097 • Published
Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label PGPS9K@7dfd9dad4e455344b751b87340cd0ecf8798f4db
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2567, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2102, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2134, in _iter_arrow
pa_table = cast_table_to_features(pa_table, self.features)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2197, in cast_table_to_features
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
return feature.cast_storage(array)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1172, in cast_storage
[self._strval2int(label) if label is not None else None for label in storage.to_pylist()]
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label PGPS9K@7dfd9dad4e455344b751b87340cd0ecf8798f4dbNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
PGPS9K: Plane Geometry Problem Solving Dataset
[🌐 Homepage] [💻 Github][📖 Paper]
Introduction
The Plane Geometry Problem Solving Dataset (PGPS9K) was constructed by the State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation of Chinese Academy of Sciences (CASIA). The samples in PGPS9K are labeled with both fine-grained diagram annotation and interpretable solution program, where the diagram annotation is converted into structural clauses and semantic clauses to effectively describe multi-level information in geometry diagram.
Collection and Description
PGPS9K is composed of 9,022 geometry problems paired with non-duplicate 4,000 geometry diagrams, where 2,891 problems paired with 1,738 diagrams are selected from Geometry3K dataset, the rest of problems are collected from five popular textbooks across grades 6-12 on mathematics curriculum websites . Our PGPS9K is divided into 30 problem types as exhibited in Fig. 2, covering almost all problem types of plane geometry problem in corresponding grades.
As shown in Fig. 3, PGPS9K dataset has five properties, which make it focus on the challenges at geometric reasoning and alleviate the bias introduced by the text:
Theorem-based: Solving problems in PGPS9K need to apply geometric theorem knowledge to carry out algebraic calculation and get numerical results finally;
Diagram-dependent: Above 90% of problems must be solved using the diagrams because necessary conditions such as variable content and geometric structure are displayed via visual form instead of text;
Abstract: The diagram is integrated with basic geometric primitives (point, line, circle) and non-geometric primitives (text, symbol). No complex semantic scenarios are involved in textual problem except abstract geometric conditions;
Fine-grained: Problems with the same diagram vary in conditions or targets. Slight distinctions in textual problems usually lead to completely different solutions to problems;
Condition-redundancy: Lots of conditions in semantic clauses or textual problem are not needed in problem solving at hand. The statistics results show that on average, 1.9 conditions are not used in problem solving, 42% of problems have redundant conditions.
Citation
If you find this work useful, welcome to cite/star us.
@inproceedings{Zhang2023PGPS,
title = {A Multi-Modal Neural Geometric Solver with Textual Clauses Parsed from Diagram},
author = {Zhang, Ming-Liang and Yin, Fei and Liu, Cheng-Lin},
booktitle = {IJCAI},
year = {2023},
}
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