Datasets:

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Reviewer2_PGE_raw

Tasks:

Text Generation

Languages:

English

Size:

10K<n<100K

ArXiv:

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train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed

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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