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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
built_at: string
source: string
selection_method: string
selection_rule: string
figure_count: int64
annotation_count: int64
paper_count: int64
annotator_count_distribution: struct<3: int64, 4: int64, 6: int64, 7: int64, 8: int64, 9: int64>
child 0, 3: int64
child 1, 4: int64
child 2, 6: int64
child 3, 7: int64
child 4, 8: int64
child 5, 9: int64
venue_distribution: struct<ACL: int64, EMNLP: int64, ICML: int64, NeurIPS: int64>
child 0, ACL: int64
child 1, EMNLP: int64
child 2, ICML: int64
child 3, NeurIPS: int64
year_distribution: struct<2020: int64, 2021: int64, 2022: int64, 2023: int64, 2024: int64, 2025: int64>
child 0, 2020: int64
child 1, 2021: int64
child 2, 2022: int64
child 3, 2023: int64
child 4, 2024: int64
child 5, 2025: int64
with_context_figures: int64
without_context_figures: int64
with_context_ratio: double
human_gold_standard: struct<join_key: list<item: string>, aggregation: string, annotator_filter: string>
child 0, join_key: list<item: string>
child 0, item: string
child 1, aggregation: string
child 2, annotator_filter: string
target_figures: int64
output_dir: string
venue: string
paper_id_norm: string
height: int64
annotations: list<item: struct<source_type: string, source_name: string, visual_clarity: double, structure_layout (... 343 chars omitted)
child 0, item: struct<source_type: string, source_name: string, visual_clarity: double, structure_layout: double, c (... 331 chars omitted)
child 0, source_type: string
child 1, source_name: string
child 2, visual_clarity: double
child 3, structure_layout: double
child 4, caption_consistency: double
child 5, context_consistency: double
child 6, misleading_risk: double
child 7, overall_score: double
child 8, summary: string
child 9, suggestion: string
child 10, visual_clarity_reason: string
child 11, structure_layout_reason: string
child 12, caption_consistency_reason: string
child 13, context_consistency_reason: string
child 14, misleading_risk_reason: string
child 15, annotated_at: string
context_count: int64
title: string
context_texts: list<item: string>
child 0, item: string
caption: string
fig_index: int64
domain_l1: string
annotator_count: int64
image_file: string
width: int64
year: int64
domain_l2: string
paper_id: string
section: string
to
{'paper_id': Value('string'), 'paper_id_norm': Value('string'), 'venue': Value('string'), 'year': Value('int64'), 'fig_index': Value('int64'), 'title': Value('string'), 'caption': Value('string'), 'image_file': Value('string'), 'domain_l1': Value('string'), 'domain_l2': Value('string'), 'width': Value('int64'), 'height': Value('int64'), 'section': Value('string'), 'context_texts': List(Value('string')), 'context_count': Value('int64'), 'annotator_count': Value('int64'), 'annotations': List({'source_type': Value('string'), 'source_name': Value('string'), 'visual_clarity': Value('float64'), 'structure_layout': Value('float64'), 'caption_consistency': Value('float64'), 'context_consistency': Value('float64'), 'misleading_risk': Value('float64'), 'overall_score': Value('float64'), 'summary': Value('string'), 'suggestion': Value('string'), 'visual_clarity_reason': Value('string'), 'structure_layout_reason': Value('string'), 'caption_consistency_reason': Value('string'), 'context_consistency_reason': Value('string'), 'misleading_risk_reason': Value('string'), 'annotated_at': Value('string')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
built_at: string
source: string
selection_method: string
selection_rule: string
figure_count: int64
annotation_count: int64
paper_count: int64
annotator_count_distribution: struct<3: int64, 4: int64, 6: int64, 7: int64, 8: int64, 9: int64>
child 0, 3: int64
child 1, 4: int64
child 2, 6: int64
child 3, 7: int64
child 4, 8: int64
child 5, 9: int64
venue_distribution: struct<ACL: int64, EMNLP: int64, ICML: int64, NeurIPS: int64>
child 0, ACL: int64
child 1, EMNLP: int64
child 2, ICML: int64
child 3, NeurIPS: int64
year_distribution: struct<2020: int64, 2021: int64, 2022: int64, 2023: int64, 2024: int64, 2025: int64>
child 0, 2020: int64
child 1, 2021: int64
child 2, 2022: int64
child 3, 2023: int64
child 4, 2024: int64
child 5, 2025: int64
with_context_figures: int64
without_context_figures: int64
with_context_ratio: double
human_gold_standard: struct<join_key: list<item: string>, aggregation: string, annotator_filter: string>
child 0, join_key: list<item: string>
child 0, item: string
child 1, aggregation: string
child 2, annotator_filter: string
target_figures: int64
output_dir: string
venue: string
paper_id_norm: string
height: int64
annotations: list<item: struct<source_type: string, source_name: string, visual_clarity: double, structure_layout (... 343 chars omitted)
child 0, item: struct<source_type: string, source_name: string, visual_clarity: double, structure_layout: double, c (... 331 chars omitted)
child 0, source_type: string
child 1, source_name: string
child 2, visual_clarity: double
child 3, structure_layout: double
child 4, caption_consistency: double
child 5, context_consistency: double
child 6, misleading_risk: double
child 7, overall_score: double
child 8, summary: string
child 9, suggestion: string
child 10, visual_clarity_reason: string
child 11, structure_layout_reason: string
child 12, caption_consistency_reason: string
child 13, context_consistency_reason: string
child 14, misleading_risk_reason: string
child 15, annotated_at: string
context_count: int64
title: string
context_texts: list<item: string>
child 0, item: string
caption: string
fig_index: int64
domain_l1: string
annotator_count: int64
image_file: string
width: int64
year: int64
domain_l2: string
paper_id: string
section: string
to
{'paper_id': Value('string'), 'paper_id_norm': Value('string'), 'venue': Value('string'), 'year': Value('int64'), 'fig_index': Value('int64'), 'title': Value('string'), 'caption': Value('string'), 'image_file': Value('string'), 'domain_l1': Value('string'), 'domain_l2': Value('string'), 'width': Value('int64'), 'height': Value('int64'), 'section': Value('string'), 'context_texts': List(Value('string')), 'context_count': Value('int64'), 'annotator_count': Value('int64'), 'annotations': List({'source_type': Value('string'), 'source_name': Value('string'), 'visual_clarity': Value('float64'), 'structure_layout': Value('float64'), 'caption_consistency': Value('float64'), 'context_consistency': Value('float64'), 'misleading_risk': Value('float64'), 'overall_score': Value('float64'), 'summary': Value('string'), 'suggestion': Value('string'), 'visual_clarity_reason': Value('string'), 'structure_layout_reason': Value('string'), 'caption_consistency_reason': Value('string'), 'context_consistency_reason': Value('string'), 'misleading_risk_reason': Value('string'), 'annotated_at': Value('string')})}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
paper_id string | paper_id_norm string | venue string | year int64 | fig_index int64 | title string | caption string | image_file string | domain_l1 string | domain_l2 string | width int64 | height int64 | section string | context_texts list | context_count int64 | annotator_count int64 | annotations list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0001_2020.acl-main.1 | 2020.acl-main.1 | ACL | 2,020 | 1 | Learning to Understand Child-directed and Adult-directed Speech | Figure 1: Validation performance in early training on natural speech | processed/ACL/2020/figures_clean/images/0001_2020.acl-main.1__fig0001.png | computer_science | natural_language_processing | 576 | 373 | 6 Results | [] | 0 | 3 | [
{
"source_type": "human",
"source_name": "Xiao Zhang (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 5,
"context_consistency": null,
"misleading_risk": 9,
"overall_score": 8,
"summary": "The figure is visually clear and well-structured, but its captio... |
0001_2020.acl-main.1 | 2020.acl-main.1 | ACL | 2,020 | 2 | Learning to Understand Child-directed and Adult-directed Speech | Figure 2: Validation performance in early training on synthetic speech | processed/ACL/2020/figures_clean/images/0001_2020.acl-main.1__fig0002.png | computer_science | natural_language_processing | 573 | 379 | 6 Results | [] | 0 | 3 | [
{
"source_type": "human",
"source_name": "Yutong Wang (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 5,
"context_consistency": null,
"misleading_risk": 9,
"overall_score": 8,
"summary": "The figure is visually clear and well-structured, but its capti... |
0002_2020.acl-main.2 | 2020.acl-main.2 | ACL | 2,020 | 1 | Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts | Figure 1: The architecture of our *JLPC* model with K = 3. For each prompt Pij in interview i, the *Category Inference layer* computes a *latent category membership* vector, hij . These are used as weights to form K separate *Category-Aware Response Aggregations*, which in turn are used by the *Decision Layer* to predi... | processed/ACL/2020/figures_clean/images/0002_2020.acl-main.2__fig0001.png | computer_science | natural_language_processing | 1,190 | 509 | 2 Joint Latent Prompt Categorization | [
"sponse to that prompt. Together, (Pij, Rij) form the jth turn in ith interview. Each interview Xi is labeled with a ground-truth class Yi ∈{1, ..C}, where C is the number of possible labels. In our case, there are two possible labels: depressed or not depressed. Our model, shown in Figure 1, takes as input an inte... | 3 | 3 | [
{
"source_type": "human",
"source_name": "Wanqing Xu (Human)",
"visual_clarity": 8,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 8,
"misleading_risk": 9,
"overall_score": 8.600000381469727,
"summary": "This figure provides a clear and well-structured ar... |
0002_2020.acl-main.2 | 2020.acl-main.2 | ACL | 2,020 | 2 | Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts | Figure 2: Ablation study on validation set demonstrating the importance of prompt categorization and entropy regularization for our model. | processed/ACL/2020/figures_clean/images/0002_2020.acl-main.2__fig0002.png | computer_science | natural_language_processing | 435 | 264 | 4 Experiments | [
"We analyzed how the prompt categorization and entropy regularization contribute to our model’s validation performance. The contributions of each component are visualized in Figure 2. Our analy- sis shows that while both components are impor- tant, latent prompt categorization yields the high- est contribution to t... | 1 | 3 | [
{
"source_type": "human",
"source_name": "yucen qian",
"visual_clarity": 6,
"structure_layout": 5,
"caption_consistency": 5,
"context_consistency": 6,
"misleading_risk": 4,
"overall_score": 5.199999809265137,
"summary": "This ablation study figure suffers from a lack of clarity r... |
0003_2020.acl-main.3 | 2020.acl-main.3 | ACL | 2,020 | 1 | Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling | Figure 1: Cross-domain slot filling frameworks. | processed/ACL/2020/figures_clean/images/0003_2020.acl-main.3__fig0001.png | computer_science | natural_language_processing | 560 | 413 | Abstract | [
"classification models from adapting to the target domain without any target domain supervision sig- nals. Recently, Bapna et al. (2017) proposed a cross-domain slot filling framework, which enables zero-shot adaptation. As illustrated in Figure 1a, their model conducts slot filling individually for each slot type. It... | 2 | 3 | [
{
"source_type": "human",
"source_name": "standard",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 6,
"context_consistency": 9,
"misleading_risk": 9,
"overall_score": 8.399999618530273,
"summary": "The figure clearly presents two slot filling frameworks with ... |
0003_2020.acl-main.3 | 2020.acl-main.3 | ACL | 2,020 | 2 | Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling | Figure 2: Illustration of our framework, *Coach*, and the template regularization approach. | processed/ACL/2020/figures_clean/images/0003_2020.acl-main.3__fig0002.png | computer_science | natural_language_processing | 1,113 | 530 | 1 Introduction | [
"As depicted in Figure 2, the slot filling process in our Coach framework consists of two steps. In the first step, we utilize a BiLSTM-CRF struc- ture (Lample et al., 2016) to learn the general pattern of slot entities by having our model pre- dict whether tokens are slot entities or not (i.e.,",
"In many cases, s... | 2 | 3 | [
{
"source_type": "human",
"source_name": "Chuanzhi Xu (Human)",
"visual_clarity": 7,
"structure_layout": 8,
"caption_consistency": 9,
"context_consistency": 9,
"misleading_risk": 9,
"overall_score": 8.399999618530273,
"summary": "This figure provides a detailed and well-structure... |
0004_2020.acl-main.4 | 2020.acl-main.4 | ACL | 2,020 | 2 | Designing Precise and Robust Dialogue Response Evaluators | Figure 2: Performance of the RoBERTa evaluator w.r.t amount of supervised training data (§[6.2\)](#page-3-3). | processed/ACL/2020/figures_clean/images/0004_2020.acl-main.4__fig0002.png | computer_science | natural_language_processing | 555 | 267 | 7 Conclusion | [
"of training with even fewer data. Figure 2 shows that, with only around 100 samples, the RoBERTa evaluator can reach performance close to the result obtained using the entire 720 samples."
] | 1 | 3 | [
{
"source_type": "human",
"source_name": "Xiao Zhang (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 7,
"context_consistency": 9,
"misleading_risk": 9,
"overall_score": 8.600000381469727,
"summary": "The figure is visually clear, well-structured, and ... |
0004_2020.acl-main.4 | 2020.acl-main.4 | ACL | 2,020 | 3 | Designing Precise and Robust Dialogue Response Evaluators | Figure 3: Distributions of human annotations and model outputs on the test data (90 responses). | processed/ACL/2020/figures_clean/images/0004_2020.acl-main.4__fig0003.png | computer_science | natural_language_processing | 1,160 | 336 | 7 Conclusion | [
"The distribution of human annotation scores on the 900 annotated responses has been given in Fig- ure 1(a). To analyze the distribution of model out- puts, we show the distributions of human anno- tation, ADEM’s outputs, RUBER’s outputs, and RoBERTa-eval’s outputs on the test data of 90 re- sponses in Figure 3. We... | 1 | 3 | [
{
"source_type": "human",
"source_name": "Yuyan Lin (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 10,
"context_consistency": 10,
"misleading_risk": 10,
"overall_score": 9.600000381469727,
"summary": "This multi-panel figure is exceptionally clear, w... |
0005_2020.acl-main.5 | 2020.acl-main.5 | ACL | 2,020 | 1 | Dialogue State Tracking with Explicit Slot Connection Modeling | Figure 1: DST-SC model architecture (best viewed in color). Three processing flows leading to Pgen, Pwc, Pvc are respectively generation (brown), copying from dialogue history (green), copying from last dialogue states (purple). | processed/ACL/2020/figures_clean/images/0005_2020.acl-main.5__fig0001.png | computer_science | natural_language_processing | 1,729 | 417 | 2 Model | [
"In this section, we will describe DST-SC model in detail. DST-SC is an open vocabulary model based on the encoder-decoder architecture. As shown in Figure 1, there are three components that contribute to obtain the target slot value: (1) word generation from the vocabulary; (2) word copying from the dialogue histo... | 1 | 3 | [
{
"source_type": "human",
"source_name": "Siyuan Zhao (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 10,
"misleading_risk": 9,
"overall_score": 9.199999809265137,
"summary": "The figure clearly illustrates the DST-SC model a... |
0006_2020.acl-main.6 | 2020.acl-main.6 | ACL | 2,020 | 1 | Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy | Figure 1: The architecture of KIC. Here, U t d is calculated by decode-input and utterance context vector C t u at current step , C t k represents the knowledge context vector resulted from dynamic knowledge attention. ugen and kgen are two soft switches that control the copy pointer to utterance attention distribution... | processed/ACL/2020/figures_clean/images/0006_2020.acl-main.6__fig0001.png | computer_science | natural_language_processing | 1,206 | 545 | 2 Model Description | [
"Given a dataset D = {(Xi, Yi, Ki)}N i=1, where N is the size of the dataset, a dialog response Y = {y1, y2, ..., yn} is produced by the conver- sation history utterance X = {x1, x2, ..., xm}, using also the relative knowledge set K = {k1, k2, ..., ks}. Here, m and n are the numbers of tokens in the conversation hi... | 2 | 3 | [
{
"source_type": "human",
"source_name": "Junjie Ma (Human)",
"visual_clarity": 7,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 9,
"misleading_risk": 9,
"overall_score": 8.600000381469727,
"summary": "This figure presents a clear and well-structured arc... |
0006_2020.acl-main.6 | 2020.acl-main.6 | ACL | 2,020 | 2 | Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy | Figure 2: Bleu improvements on Wizard-of-Wikipedia. | processed/ACL/2020/figures_clean/images/0006_2020.acl-main.6__fig0002.png | computer_science | natural_language_processing | 507 | 327 | 3 Experiments | [
"Bleu metrics are shown in Figure 2, we can find that the improvement of result increasing with the augment of Bleu’s grams, which means the dia- log response produced via model KIC is more in line with the real distribution of ground-truth re- sponse in the phrase level, and the better improve- ment on higher gram’... | 1 | 3 | [
{
"source_type": "human",
"source_name": "yucen qian",
"visual_clarity": 9,
"structure_layout": 8,
"caption_consistency": 5,
"context_consistency": 8,
"misleading_risk": 6,
"overall_score": 7.199999809265137,
"summary": "The plot is visually clear and well-structured, supported b... |
0007_2020.acl-main.7 | 2020.acl-main.7 | ACL | 2,020 | 1 | Guiding Variational Response Generator to Exploit Persona | Figure 1: The architecture of the Persona-Aware Variational Response Generator (PAGenerator) described in this paper. ⊕ represents the concatenation of inputs and CE denotes the cross-entropy of predictions. The dotted arrow line indicates the connection is optional, and the default model named PAGenerator decodes with... | processed/ACL/2020/figures_clean/images/0007_2020.acl-main.7__fig0001.png | computer_science | natural_language_processing | 1,206 | 311 | 1 Introduction | [
"Utilizing latent variables in response generation has become a widely accepted methodology in NRG due to their Bayesian essence. It helps to deal with external knowledge efficiently, e.g. Per- sona. Therefore, our proposed model is built based on the generation model with latent variables. The overall architecture ... | 2 | 3 | [
{
"source_type": "human",
"source_name": "Xiao Zhang (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 8,
"misleading_risk": 9,
"overall_score": 8.800000190734863,
"summary": "The figure provides an exceptionally clear and well... |
0008_2020.acl-main.8 | 2020.acl-main.8 | ACL | 2,020 | 1 | Large Scale Multi-Actor Generative Dialog Modeling | Figure 1: Illustration of input representation for a conversation from three different speakers (A, B, C) composed of a sequence of four turns (denoted A1 , B, A2 , C in the "Tokens" row) when separately modeling two different target speakers (A and C for (i) and (ii) respectively). The model receives different referen... | processed/ACL/2020/figures_clean/images/0008_2020.acl-main.8__fig0001.png | computer_science | natural_language_processing | 1,049 | 270 | 4 Model | [
"Due to supporting multi-actor conversations present in our dataset, special care is needed for presenting this information to the model. In gen- eral, this is accomplished by designating a speaker of interest to model in a conversation. As visualized in Figure 1, the designated speaker’s reference history tokens a... | 2 | 3 | [
{
"source_type": "human",
"source_name": "Yutong Wang (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 8,
"misleading_risk": 9,
"overall_score": 8.800000190734863,
"summary": "This figure is exceptionally clear and well-struct... |
0008_2020.acl-main.8 | 2020.acl-main.8 | ACL | 2,020 | 2 | Large Scale Multi-Actor Generative Dialog Modeling | Figure 2: Test scores compared against the number of dialog turns given as context prior to generating samples for GCC-DEC (355M) and GCC-NRC (355M). | processed/ACL/2020/figures_clean/images/0008_2020.acl-main.8__fig0002.png | computer_science | natural_language_processing | 463 | 489 | 5 Experiments | [
"Reference use From our qualitative study we can clearly see the benefit of using reference history as was alluded to in prior sections. In all four exper- iments the presence of references leads to better ground truth performance compared to GCC-NRC . In Figure 2 we delve deeper into the results of the ground truth... | 1 | 3 | [
{
"source_type": "human",
"source_name": "standard",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 5,
"context_consistency": 8,
"misleading_risk": 7,
"overall_score": 7.599999904632568,
"summary": "The figure is visually clear and well-structured but suffers ... |
0008_2020.acl-main.8 | 2020.acl-main.8 | ACL | 2,020 | 6 | Large Scale Multi-Actor Generative Dialog Modeling | Figure 6: Coherency Task Turk Layout. | processed/ACL/2020/figures_clean/images/0008_2020.acl-main.8__fig0006.png | computer_science | natural_language_processing | 1,208 | 539 | 7 Conclusions | [] | 0 | 3 | [
{
"source_type": "human",
"source_name": "zihan Deng (Human)",
"visual_clarity": 7,
"structure_layout": 9,
"caption_consistency": 5,
"context_consistency": null,
"misleading_risk": 7,
"overall_score": 7,
"summary": "The figure effectively shows a human evaluation task layout, but... |
0009_2020.acl-main.9 | 2020.acl-main.9 | ACL | 2,020 | 1 | PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable | Figure 1: Graphical illustration of response generation (gray lines) and latent act recognition (dashed blue lines). | processed/ACL/2020/figures_clean/images/0009_2020.acl-main.9__fig0001.png | computer_science | natural_language_processing | 523 | 188 | 1 Introduction | [
"The probabilistic relationships among these el- ements are elaborated with the graphical model in Figure 1. Given a context c, there are multi- ple latent speech acts which can be taken as re- sponse intents (represented by the latent variable z). Conditioned on the context and one selected latent speech act, the ... | 1 | 3 | [
{
"source_type": "human",
"source_name": "Yuyan Lin (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 10,
"misleading_risk": 9,
"overall_score": 9.199999809265137,
"summary": "The figure provides an exceptionally clear and well... |
0009_2020.acl-main.9 | 2020.acl-main.9 | ACL | 2,020 | 2 | PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable | Figure 2: Architecture of dialogue generation with discrete latent variable. In self-attention visualization, red and blue lines denote bi-directional attention, and dashed orange lines denote uni-directional attention. | processed/ACL/2020/figures_clean/images/0009_2020.acl-main.9__fig0002.png | computer_science | natural_language_processing | 1,208 | 475 | 2 Dialogue Generation Pre-training | [
"The probabilistic relationships among these el- ements are elaborated with the graphical model in Figure 1. Given a context c, there are multi- ple latent speech acts which can be taken as re- sponse intents (represented by the latent variable z). Conditioned on the context and one selected latent speech act, the ... | 3 | 3 | [
{
"source_type": "human",
"source_name": "Chuanzhi Xu (Human)",
"visual_clarity": 8,
"structure_layout": 7,
"caption_consistency": 5,
"context_consistency": 7,
"misleading_risk": 8,
"overall_score": 7,
"summary": "The figure provides a detailed architectural overview with good vi... |
0010_2020.acl-main.10 | 2020.acl-main.10 | ACL | 2,020 | 1 | Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network | Figure 1: IRN consists of two modules: an experience replay buffer and a pointer rewriter. The experience replay buffer collects mistaken cases from NLG baseline, template and IRN itself (the red dashed arrow) whereas the pointer network outputs templates with improved performance metrics. In each epoch of rectificatio... | processed/ACL/2020/figures_clean/images/0010_2020.acl-main.10__fig0001.png | computer_science | reinforcement_learning | 1,207 | 569 | 3 Architeture | [
"Figure 1 shows the architecture of Iterative Recti- fication Network. It consists of two components: a pointer rewriter to produce templates with im- proved performance metrics and an experience re- play buffer to gather and sample training data. The improvements on slot consistency are ob- tained via an iterative ... | 1 | 3 | [
{
"source_type": "human",
"source_name": "zihan Deng (Human)",
"visual_clarity": 7,
"structure_layout": 8,
"caption_consistency": 8,
"context_consistency": 9,
"misleading_risk": 8,
"overall_score": 8,
"summary": "This figure provides a clear, well-structured architectural overvie... |
0010_2020.acl-main.10 | 2020.acl-main.10 | ACL | 2,020 | 2 | Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network | Figure 2: Correcting a candidate given a reference template. d c , d l , and d π are inferred by simple rules. | processed/ACL/2020/figures_clean/images/0010_2020.acl-main.10__fig0002.png | computer_science | natural_language_processing | 1,216 | 303 | 3 Architeture | [
"One key idea behind the proposed IRN model is to conduct distant supervision on the actions of tem- plate copy and generation. We diagram its motiva- tion in Figure 2. During training, only candidate y and its reference z are given. The exact actions that convert template y to z have to be inferred from the two te... | 2 | 3 | [
{
"source_type": "human",
"source_name": "Xiaotong Wu (Human)",
"visual_clarity": 10,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 7,
"misleading_risk": 9,
"overall_score": 8.800000190734863,
"summary": "This figure clearly illustrates the process of co... |
0011_2020.acl-main.11 | 2020.acl-main.11 | ACL | 2,020 | 1 | Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations | Figure 1: Turn-based span extraction with the new RESTAURANTS-8K data set. Note how the requested slot feature is needed to differentiate time or party size in short utterances like "7". The single-turn examples are extracted from different conversations. | processed/ACL/2020/figures_clean/images/0011_2020.acl-main.11__fig0001.png | computer_science | natural_language_processing | 596 | 375 | 1 Introduction | [
"time and number of guests with correct values given by the user (e.g. tomorrow, 8pm, 3 people) in or- der to proceed with a booking. A particular chal- lenge is to deploy slot-filling systems in low-data regimes (i.e., few-shot learning setups), which is needed to enable quick and wide portability of con- versation... | 2 | 3 | [
{
"source_type": "human",
"source_name": "Chuanzhi Xu (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 6,
"misleading_risk": 9,
"overall_score": 8.399999618530273,
"summary": "This figure clearly and effectively demonstrates t... |
0011_2020.acl-main.11 | 2020.acl-main.11 | ACL | 2,020 | 2 | Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations | Figure 2: Span-ConveRT model architecture. Contextual subword embeddings, computed by ConveRT, are augmented with token features, and fed through a CNN. The outputs of the CNN parameterise a CRF sequence model, defining a distribution over sequence tag labellings, using the *before*, *begin*, *inside*, *after* scheme. ... | processed/ACL/2020/figures_clean/images/0011_2020.acl-main.11__fig0002.png | computer_science | natural_language_processing | 503 | 755 | 2 Methodology: Span-ConveRT | [
"Span ConveRT: Final Model. We now describe our model architecture, illustrated in Figure 2. Our approach builds on established sequence tagging models using Conditional Random Fields (CRFs) (Ma and Hovy, 2016; Lample et al., 2016). We propose to replace the LSTM part of the model with fixed ConveRT embeddings.4 We ... | 1 | 3 | [
{
"source_type": "human",
"source_name": "Yuyan Lin (Human)",
"visual_clarity": 8,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 9,
"misleading_risk": 9,
"overall_score": 8.800000190734863,
"summary": "This figure provides a clear, well-structured, and t... |
0011_2020.acl-main.11 | 2020.acl-main.11 | ACL | 2,020 | 3 | Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations | Figure 3: Breakdown of errors made on the test set of RESTAURANTS-8K after training on the entire train set. | processed/ACL/2020/figures_clean/images/0011_2020.acl-main.11__fig0003.png | computer_science | natural_language_processing | 1,161 | 572 | 5 Conclusion and Future Work | [
"When training on the full training set (Figure 3), there is little difference in error breakdown between Span-ConveRT and V-CNN-CRF. This suggests the behavior of these models is similar when trained in a high-data setting, but improvements made by Span-ConveRT are on all fronts. When trained on a 16th of the data... | 1 | 3 | [
{
"source_type": "human",
"source_name": "Haoran Sun (Human)",
"visual_clarity": 7,
"structure_layout": 9,
"caption_consistency": 6,
"context_consistency": 9,
"misleading_risk": 8,
"overall_score": 7.800000190734863,
"summary": "The figure is well-structured and visually clear, a... |
0011_2020.acl-main.11 | 2020.acl-main.11 | ACL | 2,020 | 4 | Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations | Figure 4: Breakdown of errors made on the test set of RESTAURANTS-8K after training on a 16th of the train set. | processed/ACL/2020/figures_clean/images/0011_2020.acl-main.11__fig0004.png | computer_science | natural_language_processing | 1,177 | 579 | 5 Conclusion and Future Work | [
"When training on the full training set (Figure 3), there is little difference in error breakdown between Span-ConveRT and V-CNN-CRF. This suggests the behavior of these models is similar when trained in a high-data setting, but improvements made by Span-ConveRT are on all fronts. When trained on a 16th of the data... | 1 | 3 | [
{
"source_type": "human",
"source_name": "Yutong Wang (Human)",
"visual_clarity": 7,
"structure_layout": 9,
"caption_consistency": 6,
"context_consistency": 9,
"misleading_risk": 8,
"overall_score": 7.800000190734863,
"summary": "This figure is well-structured and visually clear ... |
0012_2020.acl-main.12 | 2020.acl-main.12 | ACL | 2,020 | 3 | Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking | Figure 3: Breakdown of accuracy by turn number and number of slots of the TRADE model on the "Restaurant" domain. "Zero-shot" results are trained by withholding in-domain data, and "Zero-shot (DM)" is our data synthesis based on the Dialogue Model. "Full dataset" refers to training with all domains. | processed/ACL/2020/figures_clean/images/0012_2020.acl-main.12__fig0003.png | computer_science | natural_language_processing | 554 | 262 | 5 Experiments | [
"To analyze the errors, we break down the result according to the turn number and number of slots in the dialogues in the test set, as shown in Fig. 3. We perform this analysis using the TRADE model on the “Restaurant” domain, which is the largest domain in MultiWOZ. We observe that the base- line model achieves 10... | 1 | 3 | [
{
"source_type": "human",
"source_name": "Xiao Zhang (Human)",
"visual_clarity": 8,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 9,
"misleading_risk": 9,
"overall_score": 8.800000190734863,
"summary": "The figure is visually clear and well-structured, w... |
0012_2020.acl-main.12 | 2020.acl-main.12 | ACL | 2,020 | 4 | Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking | Figure 4: Accuracy plots for the few-shot MultiWOZ experiments. X axis indicates the percentage of real target domain data included in training. Y axis indicates joint accuracy. | processed/ACL/2020/figures_clean/images/0012_2020.acl-main.12__fig0004.png | computer_science | natural_language_processing | 1,236 | 222 | 5 Experiments | [
"Following Wu et al. (2019), we also evaluate the effect of mixing a small percentage of real train- ing data in our augmented training sets. We use a naive few-shot training strategy, where we di- rectly add a portion of the original training data in the domain of interest to the training set. Fig. 4 plots the joi... | 1 | 3 | [
{
"source_type": "human",
"source_name": "Xiao Zhang (Human)",
"visual_clarity": 8,
"structure_layout": 9,
"caption_consistency": 6,
"context_consistency": 8,
"misleading_risk": 8,
"overall_score": 7.800000190734863,
"summary": "The figure is visually clear and well-structured, e... |
0013_2020.acl-main.13 | 2020.acl-main.13 | ACL | 2,020 | 1 | A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle | Figure 1: Overview of our Chinese discourse parser. | processed/ACL/2020/figures_clean/images/0013_2020.acl-main.13__fig0001.png | computer_science | natural_language_processing | 568 | 681 | 2 Methodology | [
"Figure 1 gives an overview of our parser. Five stages are performed to transform a raw document into a parse tree: EDU segmentation, tree structure construction, rhetorical relation and nuclearity clas- sification, binary tree conversion, and beam search."
] | 1 | 3 | [
{
"source_type": "human",
"source_name": "Xiao Zhang (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 6,
"context_consistency": 8,
"misleading_risk": 9,
"overall_score": 8.199999809265137,
"summary": "This figure provides a clear and well-structured ov... |
0014_2020.acl-main.14 | 2020.acl-main.14 | ACL | 2,020 | 1 | TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition | Figure 1: TransS-driven joint learning architecture of our proposed model. | processed/ACL/2020/figures_clean/images/0014_2020.acl-main.14__fig0001.png | computer_science | natural_language_processing | 1,084 | 421 | 2 The Proposed Model | [
"The implicit discourse relation recognition task is usually formalized as a classification problem. In this section, we give an overview of the TransS- driven joint learning framework, which consists of four parts: embedding layer, multi-level encoder, latent geometric structure learning, and semantic feature learn... | 1 | 3 | [
{
"source_type": "human",
"source_name": "yucen qian",
"visual_clarity": 8,
"structure_layout": 9,
"caption_consistency": 8,
"context_consistency": 9,
"misleading_risk": 9,
"overall_score": 8.600000381469727,
"summary": "This figure provides a clear, well-structured overview of t... |
0014_2020.acl-main.14 | 2020.acl-main.14 | ACL | 2,020 | 2 | TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition | Figure 2: The illustration of multi-level encoder. | processed/ACL/2020/figures_clean/images/0014_2020.acl-main.14__fig0002.png | computer_science | natural_language_processing | 552 | 338 | 2 The Proposed Model | [
"To enrich the discourse argument representations, we exploit multi-level encoder shown in Figure 2 to learn the argument representations at the different levels. Particularly, the higher-level states of multi- level encoder could capture context-dependent as- pects of words while the lower-level states could model... | 1 | 3 | [
{
"source_type": "human",
"source_name": "Yutong Wang (Human)",
"visual_clarity": 9,
"structure_layout": 9,
"caption_consistency": 9,
"context_consistency": 9,
"misleading_risk": 9,
"overall_score": 9,
"summary": "This figure is visually clear and well-structured, providing an ex... |
0014_2020.acl-main.14 | 2020.acl-main.14 | ACL | 2,020 | 4 | TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition | Figure 4: The effect of encoder layers' number. | processed/ACL/2020/figures_clean/images/0014_2020.acl-main.14__fig0004.png | computer_science | natural_language_processing | 361 | 278 | 3 Experiments | [
"as comparison experiments on the 4-way classi- fication. Figure 4 shows that the F1 scores are increasing until three encoder layers. And when the size of the encoder layer is four or five, the performance of our model is decreasing obviously. With the increasing of the number of encoder lay- ers, the model could ca... | 1 | 3 | [
{
"source_type": "human",
"source_name": "lambert",
"visual_clarity": 8,
"structure_layout": 8,
"caption_consistency": 7,
"context_consistency": 9,
"misleading_risk": 9,
"overall_score": 8.199999809265137,
"summary": "The figure clearly illustrates the effect of encoder layers on... |
End of preview.
SciFigQual-Bench
Full-manuscript-context benchmark for multidimensional quality assessment of scientific paper figures from ACL, EMNLP, ICML, and NeurIPS (2020–2025).
Dataset summary
| Item | Count |
|---|---|
| Curated figures | 7,609 (corpus) |
| Gold-labeled instances (this release) | 6,308 |
| Raw annotation records (multi-rater) | 20,166 |
Public eval split eval1200 |
1,200 |
Each instance is a tuple (I, c, T, m): figure image, caption, citing paragraphs, and metadata. Human gold scores aggregate multiple expert ratings per figure (mean over annotators).
Files
figures.jsonl— 6,308 instances with caption, context, rawannotations[]human_means.csv— aggregated gold means (VC, SL, CC, CTX, MR, overall)images/— 6,308 PNG cropssplits/eval1200.jsonl— fixed 1,200-figure public test manifestcorpus_stats.json— construction funnel statistics
Join keys
figure_id or (paper_id, fig_index)
Five-dimensional rubric (1–10)
Visual Clarity (VC), Structure & Layout (SL), Caption Consistency (CC), Context Consistency (CTX), Misleading Risk (MR).
Model predictions
See companion release 3_supporting_eval_results/ (29 protocol–backend runs on eval1200).
Citation
@inproceedings{scifigqual2027,
title={SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context},
year={2027}
}
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