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Error code: DatasetGenerationError Exception: TypeError Message: Couldn't cast array of type string to null Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table pa_table = table_cast(pa_table, self._schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2075, in cast_array_to_feature casted_array_values = _c(array.values, feature.feature) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper return func(array, *args, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2116, in cast_array_to_feature return array_cast( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper return func(array, *args, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1962, in array_cast raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}") TypeError: Couldn't cast array of type string to null 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 1524, in compute_config_parquet_and_info_response parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet( File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet builder._prepare_split( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
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paper_id
string | conference
string | decision
null | url
null | hasContent
string | hasReview
string | title
string | authors
sequence |
---|---|---|---|---|---|---|---|
ACL_2017_104 | ACL | null | null | true | true | Bridging Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding | [] |
ACL_2017_105 | ACL | null | null | true | true | Morphological Inflection Generation with Hard Monotonic Attention | [] |
ACL_2017_107 | ACL | null | null | true | true | Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection | [] |
ACL_2017_108 | ACL | null | null | true | true | A Multigraph-based Model for Overlapping Entity Recognition | [] |
ACL_2017_117 | ACL | null | null | true | true | Improved Neural Relation Detection for Knowledge Base Question Answering | [] |
ACL_2017_122 | ACL | null | null | true | true | Neural Belief Tracker: Data-Driven Dialogue State Tracking | [] |
ACL_2017_128 | ACL | null | null | true | true | Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks | [] |
ACL_2017_12 | ACL | null | null | true | true | Time Expression Analysis and Recognition Using Syntactic Types and Simple Heuristic Rules | [] |
ACL_2017_130 | ACL | null | null | true | true | Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts | [] |
ACL_2017_134 | ACL | null | null | true | true | Neural End-to-End Learning for Computational Argumentation Mining | [] |
ACL_2017_145 | ACL | null | null | true | true | null | [] |
ACL_2017_148 | ACL | null | null | true | true | Evaluation Metrics for Reading Comprehension: Prerequisite Skills and Readability | [] |
ACL_2017_150 | ACL | null | null | true | true | Deep Character-Level Neural Machine Translation By Learning Morphology | [] |
ACL_2017_169 | ACL | null | null | true | true | Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction | [] |
ACL_2017_16 | ACL | null | null | true | true | Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms | [] |
ACL_2017_173 | ACL | null | null | true | true | Determining Gains Acquired from Word Embedding Quantitatively using Discrete Distribution Clustering | [] |
ACL_2017_178 | ACL | null | null | true | true | A Weakly-Supervised Method for Jointly Embedding Concepts, Phrases, and Words | [] |
ACL_2017_180 | ACL | null | null | true | true | Identifying Products in Online Cybercrime Marketplaces: A Dataset and Fine-grained Domain Adaptation Task | [] |
ACL_2017_182 | ACL | null | null | true | true | Modeling Contextual Relationships Among Utterances for Multimodal Sentiment Analysis | [] |
ACL_2017_18 | ACL | null | null | true | true | Attention-over-Attention Neural Networks for Reading Comprehension | [] |
ACL_2017_193 | ACL | null | null | true | true | null | [] |
ACL_2017_19 | ACL | null | null | true | true | Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution | [] |
ACL_2017_201 | ACL | null | null | true | true | Investigating Different Context Types and Representations for Learning Word Embeddings | [] |
ACL_2017_214 | ACL | null | null | true | true | Exploring Macro Discourse Structure with Macro-micro Unified Primary-secondary Relationship | [] |
ACL_2017_216 | ACL | null | null | true | true | Topical Coherence in LDA-based Models through Induced Segmentation | [] |
ACL_2017_21 | ACL | null | null | true | true | Transductive Non-linear Learning for Chinese Hypernym Prediction | [] |
ACL_2017_220 | ACL | null | null | true | true | null | [] |
ACL_2017_222 | ACL | null | null | true | true | Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme | [] |
ACL_2017_226 | ACL | null | null | true | true | null | [] |
ACL_2017_237 | ACL | null | null | true | true | A New Approach for Measuring Sentiment Orientation based on Multi-Dimensional Vector Space | [] |
ACL_2017_239 | ACL | null | null | true | true | How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks | [] |
ACL_2017_251 | ACL | null | null | true | true | null | [] |
ACL_2017_256 | ACL | null | null | true | true | Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders | [] |
ACL_2017_266 | ACL | null | null | true | true | Improving sentiment classification with task-specific data | [] |
ACL_2017_26 | ACL | null | null | true | true | An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge | [] |
ACL_2017_270 | ACL | null | null | true | true | Enhanced LSTM for Natural Language Inference | [] |
ACL_2017_276 | ACL | null | null | true | true | Semi-supervised Multitask Learning for Sequence Labeling | [] |
ACL_2017_288 | ACL | null | null | true | true | The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task | [] |
ACL_2017_318 | ACL | null | null | true | true | Improved Word Representation Learning with Sememes | [] |
ACL_2017_31 | ACL | null | null | true | true | Event Factuality Identification via Deep Neural Networks | [] |
ACL_2017_323 | ACL | null | null | true | true | A Neural Local Coherence Model | [] |
ACL_2017_326 | ACL | null | null | true | true | Adversarial Multi-Criteria Learning for Chinese Word Segmentation | [] |
ACL_2017_331 | ACL | null | null | true | true | Connecting the dots: Summarizing and Structuring Large Document Collections Using Concept Maps | [] |
ACL_2017_333 | ACL | null | null | true | true | Selective Encoding for Abstractive Sentence Summarization | [] |
ACL_2017_335 | ACL | null | null | true | true | Gated Self-Matching Networks for Reading Comprehension and Question Answering | [] |
ACL_2017_338 | ACL | null | null | true | true | Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors | [] |
ACL_2017_33 | ACL | null | null | true | true | Linguistically Regularized LSTM for Sentiment Classification | [] |
ACL_2017_343 | ACL | null | null | true | true | Neural Word Segmentation with Rich Pretraining | [] |
ACL_2017_350 | ACL | null | null | true | true | null | [] |
ACL_2017_352 | ACL | null | null | true | true | null | [] |
ACL_2017_355 | ACL | null | null | true | true | Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis | [] |
ACL_2017_365 | ACL | null | null | true | true | Learning attention for historical text normalization by learning to pronounce | [] |
ACL_2017_367 | ACL | null | null | true | true | null | [] |
ACL_2017_369 | ACL | null | null | true | true | Morphology Generation for Statistical Machine Translation using Deep Learning Techniques | [] |
ACL_2017_371 | ACL | null | null | true | true | null | [] |
ACL_2017_375 | ACL | null | null | true | true | CANE: Context-Aware Network Embedding for Relation Modeling | [] |
ACL_2017_376 | ACL | null | null | true | true | Event-based, Recursive Neural Networks for the Extraction and Aggregation of International Alliance Relations | [] |
ACL_2017_37 | ACL | null | null | true | true | Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots | [] |
ACL_2017_382 | ACL | null | null | true | true | Creating Training Corpora for NLG Micro-Planning | [] |
ACL_2017_384 | ACL | null | null | true | true | Identifying 1950s American Jazz Composers: Fine-Grained IsA Extraction via Modifier Composition | [] |
ACL_2017_387 | ACL | null | null | true | true | Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network | [] |
ACL_2017_388 | ACL | null | null | true | true | null | [] |
ACL_2017_395 | ACL | null | null | true | true | DRL-Sense: Deep Reinforcement Learning for Multi-Sense Word Representations | [] |
ACL_2017_419 | ACL | null | null | true | true | One-Shot Neural Cross-Lingual Transfer for Paradigm Completion | [] |
ACL_2017_433 | ACL | null | null | true | true | null | [] |
ACL_2017_435 | ACL | null | null | true | true | Neural Disambiguation of Causal Lexical Markers based on Context | [] |
ACL_2017_440 | ACL | null | null | true | true | null | [] |
ACL_2017_444 | ACL | null | null | true | true | Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting | [] |
ACL_2017_447 | ACL | null | null | true | true | Neural Discourse Structure for Text Categorization | [] |
ACL_2017_462 | ACL | null | null | true | true | Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models | [] |
ACL_2017_467 | ACL | null | null | true | true | null | [] |
ACL_2017_477 | ACL | null | null | true | true | From Characters to Words to in Between: Do We Capture Morphology? | [] |
ACL_2017_481 | ACL | null | null | true | true | null | [] |
ACL_2017_483 | ACL | null | null | true | true | Here’s My Point: Argumentation Mining with Pointer Networks | [] |
ACL_2017_484 | ACL | null | null | true | true | null | [] |
ACL_2017_489 | ACL | null | null | true | true | Combining distributional and referential information for naming objects through cross-modal mapping and direct word prediction | [] |
ACL_2017_494 | ACL | null | null | true | true | Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules | [] |
ACL_2017_496 | ACL | null | null | true | true | What do Neural Machine Translation Models Learn about Morphology? | [] |
ACL_2017_49 | ACL | null | null | true | true | Chunk-based Decoder for Neural Machine Translation | [] |
ACL_2017_501 | ACL | null | null | true | true | Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task | [] |
ACL_2017_503 | ACL | null | null | true | true | Probabilistic Regular Graph Languages | [] |
ACL_2017_516 | ACL | null | null | true | true | Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling | [] |
ACL_2017_520 | ACL | null | null | true | true | SHAPEWORLD: A new test methodology for multimodal language understanding | [] |
ACL_2017_524 | ACL | null | null | true | true | A Comparison of Robust Parsing Methods for HPSG | [] |
ACL_2017_543 | ACL | null | null | true | true | Learning Character-level Compositionality with Visual Features | [] |
ACL_2017_553 | ACL | null | null | true | true | null | [] |
ACL_2017_554 | ACL | null | null | true | true | Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling | [] |
ACL_2017_557 | ACL | null | null | true | true | End-to-End Neural Relation Extraction with Global Optimization | [] |
ACL_2017_561 | ACL | null | null | true | true | Semi-supervised sequence tagging with bidirectional language models | [] |
ACL_2017_562 | ACL | null | null | true | true | Zero-Shot Relation Extraction via Reading Comprehension | [] |
ACL_2017_563 | ACL | null | null | true | true | Exploring Vector Spaces for Semantic Relations | [] |
ACL_2017_564 | ACL | null | null | true | true | Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search | [] |
ACL_2017_56 | ACL | null | null | true | true | Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics | [] |
ACL_2017_578 | ACL | null | null | true | true | Robust Incremental Neural Semantic Graph Parsing | [] |
ACL_2017_579 | ACL | null | null | true | true | MinIE: Minimizing Facts in Open Information Extraction | [] |
ACL_2017_588 | ACL | null | null | true | true | Rare Entity Prediction: Language Understanding with External Knowledge using Hierarchical LSTMs | [
"Peter Ackroyd"
] |
ACL_2017_606 | ACL | null | null | true | true | Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision | [] |
ACL_2017_614 | ACL | null | null | true | true | null | [] |
ACL_2017_619 | ACL | null | null | true | true | A Corpus of Annotated Revisions for Studying Argumentative Writing | [] |
ACL_2017_627 | ACL | null | null | true | true | Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access | [] |
End of preview.
Raw Review Dataset for Reviewer2
This is the raw version of our dataset. The cleaned data files that can be directly used for fine-tuning is in this directory.
Dataset Structure
The folders are structured in the following way:
venue
|--venue_year
|--venue_year_metadata
|--venue_year_id1_metadata.json
|--venue_year_id2_metadata.json
...
|--venue_year_paper
|--venue_year_id1_paper.json
|--venue_year_id2_paper.json
...
|--venue_year_review
|--venue_year_id1_review.json
|--venue_year_id2_review.json
...
|--venue_year_pdf
|--venue_year_id1_pdf.pdf
|--venue_year_id2_pdf.pdf
...
Dataset Content
Paper Contents
- title: title of the paper
- authors: list of author names
- emails: list of author emails
- sections: list of sections of the paper
- heading: heading of the section
- text: text of the section
- references: list of references of the paper
- title: title of the reference
- author: list of author names of the reference
- venue: venue of the reference
- citeRegEx: citation expression
- shortCiteRegEx: short citation expression
- year: publication year of the reference
- referenceMentions: the location of the reference
in the paper
- referenceID: numerical reference id
- context: context of the reference in the paper
- startOffset: start index of the context
- endOffset: end index of the context
- year: year of publication
- abstractText: abstract of the paper
Metadata Contents
- id: unique id of the paper
- conference: venue for the paper
- decision: final decision for the paper (accept/reject)
- url: link to the PDF of the paper
- review_url: link to the review of the paper
- title: title of the paper
- authors: list of the authors of the paper
Dataset Sources
We incorporate parts of the PeerRead and NLPeer datasets along with an update-to-date crawl from ICLR and NeurIPS on OpenReview and NeurIPS Proceedings.
Citation
If you find this dataset useful in your research, please cite the following paper:
@misc{gao2024reviewer2,
title={Reviewer2: Optimizing Review Generation Through Prompt Generation},
author={Zhaolin Gao and Kianté Brantley and Thorsten Joachims},
year={2024},
eprint={2402.10886},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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