Datasets:

AmyIvan
/

mosaic-emnlp2026

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Expected object or value
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
                  batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
                  examples = [ujson_loads(line) for line in original_batch.splitlines()]
                              ~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

MOSAIC

Dataset Summary

MOSAIC is a course-centric multimodal dataset accompanying a Findings of EMNLP 2026 paper. The dataset centers on mosaic.jsonl, a JSONL file that stores course-level metadata together with nested video-level summaries, subtitles, captions, and auxiliary references.

The public release also includes:

data/graph_p_results/: course-level knowledge graph JSON files keyed by kg
data/all.csv: source-resource metadata for video-level slide references

MOSAIC records video-level links to matched slide resources. It does not redistribute original videos, full slide decks, rendered slide pages, or slide OCR text, and it does not provide manually annotated utterance-to-slide-page alignment.

Supported Tasks

topic-guided lecture segmentation and topic alignment
explicit non-topical utterance modeling
subtitle and caption analysis
course knowledge graph grounding
video-level slide-resource linking
document-aware summarization and educational NLP

Languages

The dataset is primarily in Chinese, with a smaller amount of English content in titles, references, and course materials.

Dataset Structure

.
├── README.md
├── LICENSE
├── DATA_RIGHTS.md
└── data/
    ├── mosaic.jsonl
    ├── all.csv
    └── graph_p_results/
        ├── BIT-1001604004.json
        └── ...

Data Instances

Main file: `data/mosaic.jsonl`

Each line is one course record with the following top-level fields:

url
course_title
contents
kg
caption_anno
overview
objectives
prerequisites
references

Each video entry inside contents[*].courses[*] contains:

video_url
srt_url
summary
subtitle
caption
video_title
ref

The ref object includes:

cate: reference category
doc: zero or more video-level reference document URLs

The ref.doc values are video-level resource links, not utterance-to-page labels.

Knowledge graphs: `data/graph_p_results/*.json`

Each knowledge graph file contains a top-level object with:

code
message
sampled
traceId
result

The main graph payload is stored in:

result.mocKgNodeDtoList

Slide-resource mapping: `data/all.csv`

Columns:

doc_url: document URL referenced in mosaic.jsonl
filename: internal source filename retained for metadata consistency

The corresponding source files are not included in the public release. Access to linked resources remains subject to the source platform's terms and applicable permissions.

Dataset Creation

MOSAIC is constructed from publicly accessible courses on iCourse163, a major Chinese MOOC platform. The source data follows a four-level hierarchy of course, chapter, video, and topic. Each course provides course-level metadata; chapters group related videos and supporting materials; videos include timestamped ASR transcripts, instructor-provided knowledge-point outlines, and summaries; and topics correspond to the predefined knowledge points used for alignment.

MOSAIC comprises two subsets. MOSAIC-G is a human-annotated gold benchmark built from six diverse courses with utterance-level topic labels, explicit non-topical labels, and video-level links to matched slide resources. MOSAIC-S is a large silver subset for the remaining courses produced with DORA, a two-stage pipeline that first refines noisy topic inventories and then performs joint segmentation and topic assignment. Its video-to-slide-resource links are produced automatically using title matching, rule-based filtering, and LLM verification. No manual utterance-to-slide-page annotations are included in either subset.

Statistics

Metric	Value
Courses	179
Videos	14,942
Knowledge graph JSON files	167
Videos with matched slide resources	10,385
Unique matched slide-resource URLs used	7,976

Release Scope and Licensing

The public release focuses on MOSAIC annotations, metadata, resource links, knowledge-graph resources, documentation, and code. Original course videos, full slide decks, rendered slide-page images, and slide OCR text are not redistributed.

The CC BY-NC-SA 4.0 notice applies only to original MOSAIC materials for which the authors hold the necessary rights. It does not license third-party educational source content. Source-derived fields and linked resources remain subject to their respective terms and applicable permissions. See DATA_RIGHTS.md for the complete component-specific scope.

Citation Information

@inproceedings{ai-etal-2026-mosaic,
  title = {MOSAIC: A Large-Scale Multimodal Open-Course Segmentation and Alignment Corpus in Chinese},
  author = {Ai, Yuming and Fan, Shuai and Xu, Hua and Kong, Fang},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2026},
  year = {2026}
}

Downloads last month: 97

Total file size:

482 MB