eraser_multi_rc.py

# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""Passage, query, answers and answer classification with explanations."""

from __future__ import absolute_import, division, print_function

import json
import os

import datasets


_CITATION = """
@unpublished{eraser2019,
    title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
    author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
}
@inproceedings{MultiRC2018,
    author = {Daniel Khashabi and Snigdha Chaturvedi and Michael Roth and Shyam Upadhyay and Dan Roth},
    title = {Looking Beyond the Surface:A Challenge Set for Reading Comprehension over Multiple Sentences},
    booktitle = {NAACL},
    year = {2018}
}
"""

_DESCRIPTION = """
Eraser Multi RC is a dataset for queries over multi-line passages, along with
answers and a rationalte. Each example in this dataset has the following 5 parts
1. A Mutli-line Passage
2. A Query about the passage
3. An Answer to the query
4. A Classification as to whether the answer is right or wrong
5. An Explanation justifying the classification
"""

_DOWNLOAD_URL = "http://www.eraserbenchmark.com/zipped/multirc.tar.gz"


class EraserMultiRc(datasets.GeneratorBasedBuilder):
    """Multi Sentence Reasoning with Explanations (Eraser Benchmark)."""

    VERSION = datasets.Version("0.1.1")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "passage": datasets.Value("string"),
                    "query_and_answer": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=["False", "True"]),
                    "evidences": datasets.features.Sequence(datasets.Value("string")),
                }
            ),
            supervised_keys=None,
            homepage="https://cogcomp.seas.upenn.edu/multirc/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
        data_dir = os.path.join(dl_dir, "multirc")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "train.jsonl")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "val.jsonl")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "test.jsonl")},
            ),
        ]

    def _generate_examples(self, data_dir, filepath):
        """Yields examples."""

        multirc_dir = os.path.join(data_dir, "docs")
        with open(filepath, encoding="utf-8") as f:
            for line in f:
                row = json.loads(line)
                evidences = []

                for evidence in row["evidences"][0]:
                    docid = evidence["docid"]
                    evidences.append(evidence["text"])

                passage_file = os.path.join(multirc_dir, docid)
                with open(passage_file, encoding="utf-8") as f1:
                    passage_text = f1.read()

                yield row["annotation_id"], {
                    "passage": passage_text,
                    "query_and_answer": row["query"],
                    "label": row["classification"],
                    "evidences": evidences,
                }