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Data and code to accompany the paper: Halterman, Keith, Sarwar, and O’Connor. “Corpus-Level Evaluation for Event QA:
The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence.”
Findings of ACL, 2021.

If you use this data or code, please cite the paper:

@inproceedings{halterman2021corpus,
    title = "Corpus-Level Evaluation for Event {QA}: The {I}ndia{P}olice{E}vents Corpus Covering the 2002 {G}ujarat Violence",
    author = "Halterman, Andrew  and
      Keith, Katherine  and
      Sarwar, Sheikh  and
      O{'}Connor, Brendan",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.371",
    doi = "10.18653/v1/2021.findings-acl.371",
    pages = "4240--4253",
}

Directory structure

  • evaluate.py Evaluation script for the three tasks presented in the paper

  • explore_data.ipynb Jupyter notebook that walks the user through using the data and using basline models (e.g. RoBERTa+MNLI) for inference on the data.

  • data/

    • final/ Final data with adjudicated labels used for evaluation in the paper
      • sents.jsonl (docid, sentid, text, labels)
      • sents.csv
      • docs.jsonl (docid, text, labels)
      • docs.csv
      • metadata.jsonl (docid, url, date)
      • metadata.csv
    • retrieval/ In TREC format for information retrieval tasks
    • raw/ Raw data with the complete annotation information
  • archived_code/

    • models/ Baseline zero-shot models described in the paper
      • roberta_mnli.py
      • electra_msmarco.py
      • keyword_matching.py
      • bm25_variants.py
    • eval/ Evaluation scripts
      • master_eval.py

evaluate.py

Script with the evaluation metrics for Task 1-3 in the paper.

Example usage:

python evaluate.py sent_predictions.csv 1

python evaluate.py document_ranking_predictions.csv 2

python evaluate.py document_classification_predictions.csv 3

Where sent_predictions.csv, document_ranking_predictions.csv, and document_classification_predictions.csv are your own prediction files whose document and sentence order match the corresponding gold-standard .csv files: data/final/*.csv.

explore_data.ipynb

We provide a Jupyter Notebook script explore_data.ipynb with examples for users on how to explore the data and use RoBERTa trained on MNLI data for inference.

IndiaPoliceEvents Corpus

The final, adjudicated IndiaPoliceEvents corpus is located in the data/final/ folder. The .jsonl and .csv files hold the same data in different formats for user convenience. Sentence-level labels can be found in the sents.jsonl and sents.csv, document-level labels can be found in the docs.jsonl and docs.csv, and metadata about the documents (document url and document dates) can be found in metadata.jsonl and metadata.csv.

The sentence and document-level labels correspond to a positive answer to the boolean questions in the paper:

  • "KILL": The text item is indicative of “Yes” to the question “Did police kill someone?”
  • "ARREST": The text item is indicative of “Yes” to the question “Did police arrest someone?”
  • "FAIL": The text item is indicative of “Yes” to the question “Did police fail to intervene”
  • "FORCE": The text item is indicative of “Yes” to the question “Did police use force or violence?”
  • "ANY_ACTION": The text item is indicative of “Yes” to the question “Did police do anything?”

Raw Data

The raw annotated data in data/raw/raw_annotations.jsonl consists of information about the original text along with our collected annotations. The data is presented as a newline-delimited JSON file, each row of which is a dictionary with information on one article from the news source.

Document information:

  • full_text: The full text of the news story
  • sents: The sentence splits used in the annotation process. Note that many of the stories are all lower case meaning that the sentence boundary detection is often imperfect.
  • doc_id: A document ID for internal use
  • date: The publication date of the story
  • url: The URL of the story

Raw annotations:

Each document has annotations for each sentence and for the document as a whole:

  • sent_labels: An array with the length of sents, each of which contains the final, adjudicated labels for each sentence. Note that one sentence can have multiple labels.
  • doc_labels: The set of labels that were applied to at least one sentence in the document.

The labels are as follows:

  • "ann_kill": “Did police kill someone?”
  • "ann_arrest": “Did police arrest someone?”
  • "ann_fail": “Did police fail to act?”
  • "ann_other_response": “Did police use other force or violence?”
  • "ann_na": “None of the above/police did something else.”
  • "ann_multi_sent": Records whether annotators reported using information from elsewhere in the document to annotate the sentence.
  • "ann_help": During the adjudication round, adjudicators could flag difficult examples as unresolved, and the item was sent to a domain expert and an NLP expert to make the final judgment.

Note that the final form of the data used in the paper collapses these categories:

  • "KILL": {"ann_kill"}
  • "ARREST": {"ann_arrest"}
  • "FAIL": {"ann_fail"}
  • "FORCE": {"ann_kill", "ann_other_response"}
  • "ANY_ACTION": {"ann_kill", "ann_arrest", "ann_other_response", "ann_na"}

The documents also have metadata about the annotation process and detailed information on each annotator’s labels.

  • assigned_annotators: Which annotators were assigned to the story? In most cases, this is two annotators. Annotators are identified with an ID number only to preserve their privacy.
  • adjudication: a boolean reporting whether the sentence was adjudicated. Sentences on which the original two annotators disagreed were referred to a third “adjudicator” to resolve the disagreement.
  • raw_annotations: list of length of sents, each element of which is a list of dictionary items of each annotator and their annotation. For example, the annotations on one sentence could look like [{'coder': 9019, 'annotation': ['ann_na']}, {'coder': 9020, 'annotation': []}, {'coder': '9017_adjudication', 'annotation': ['ann_na']}]. If the sentence went to adjudication, the adjudicator’s annotation is indicated with {id_number)_adjudication.
  • annotation_explanations: Annotators had the option to describe why they chose a particular label or to point out a difficult or borderline annotation. This is an array with the length of sents.

archived_code

In the archived_code/ directory we provide code that was used to run the zero-shot baseline model experiments in the paper. We provide this code for documentation and replication purposes, but most of it is not directly runable.

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