coref-data

A collection of coreference annotations.

Overview

The purpose of this project is to make coreference annotations more easily usable for research purposes.

Dataset creation

The creation of each dataset is fully described within this repo and should be reproducible other than obtaining the original data for certain copyrighted datasets.

Stage 1: Parse raw datasets into HuggingFace datasets

First, the raw datasets were downloaded and then uploaded to the HuggingFace with minimal formatting. To reproduce these steps, see: dataset_creation/README.md.

Stage 2: Convert raw HuggingFace datasets into unified formats

Indiscrim Format

Coreference is often treated as the indiscriminate clustering of textual spans. Portions of certain datasets that be represented this way have been converted to a unfied format, referred to as the "indiscrim" format. The Indiscriminate Identity Coreference collection on HuggingFace hub as a list of datasets in this format.

Converting datasets to indiscrim format can be reproduced by running the following command:

python preprocessing/convert_to_indiscrim.py

The format is as follows:

{
  "id": str, # example id
  "text": str, # untokenized example text
  "sentences": [
    {
      "id": int, # sentence id (starting at 1 unless the first token is a zero/ellipsis)
      "text": str, # untokenized sentence text, may also have attributes start_char and end_char
      "speaker": None, # speaker
      "tokens": [
        {
          # keys are conllu columns: id, text, lemma, upos, xpos, feats, head, deprel, deps, misc
          # start_char and end_char may also be included
        },
        ...
      ]
    },
    ...
  ],
  "coref_chains": List[List[List[int]]], # list of clusters, each cluster is a list of mentions, each mention is a span represented as [sent, local_start, local_end] inclusive indices
  "genre": str, # a string describing the genre of text
  "meta_data": {
      "comment": str, # meta details about the dataset instance
  },
}

Reference

This code was originally written for the following project. See analysis/types_of_coreference.py for how we computed each type of coreference for the error analysis.

@inproceedings{porada-etal-2024-challenges,
    title = "Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective",
    author = "Porada, Ian  and
      Olteanu, Alexandra  and
      Suleman, Kaheer  and
      Trischler, Adam  and
      Cheung, Jackie",
    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.909",
    pages = "15380--15395",
}

ianporada / coref-data

readme