time_dial / time_dial.py
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Update files from the datasets library (from 1.11.0)
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
"""Temporal Commonsense Reasoning in Dialog"""
import json
import datasets
_CITATION = """\
title = "{TimeDial: Temporal Commonsense Reasoning in Dialog}",
author = "Qin, Lianhui and Gupta, Aditya and Upadhyay, Shyam and He, Luheng and Choi, Yejin and Faruqui, Manaal",
booktitle = "Proc. of ACL",
year = "2021"
TimeDial presents a crowdsourced English challenge set, for temporal commonsense reasoning, formulated
as a multiple choice cloze task with around 1.5k carefully curated dialogs. The dataset is derived from
the DailyDialog (Li et al., 2017), which is a multi-turn dialog corpus.
In order to establish strong baselines and provide information on future model development, we
conducted extensive experiments with state-of-the-art LMs. While humans can easily answer these
questions (97.8%), the best T5 model variant struggles on this challenge set (73%). Moreover, our
qualitative error analyses show that the models often rely on shallow, spurious features (particularly text
matching), instead of truly doing reasoning over the context.
_HOMEPAGE = "https://github.com/google-research-datasets/timedial"
_LICENSE = "TimeDial dataset is licensed under CC BY-NC-SA 4.0"
_URL = "https://raw.githubusercontent.com/google-research-datasets/TimeDial/main/test.json"
class TimeDial(datasets.GeneratorBasedBuilder):
"""Temporal Commonsense Reasoning in Dialog"""
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
"id": datasets.Value("int32"),
"conversation": datasets.features.Sequence(datasets.Value("string")),
"correct1": datasets.Value("string"),
"correct2": datasets.Value("string"),
"incorrect1": datasets.Value("string"),
"incorrect1_rule": datasets.Value("string"),
"incorrect2": datasets.Value("string"),
"incorrect2_rule": datasets.Value("string"),
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
# Homepage of the dataset for documentation
# License for the dataset if available
# Citation for the dataset
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
return [
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": dl_manager.download_and_extract(_URL), "split": "test"},
def _generate_examples(
self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
"""Yields examples as (key, example) tuples."""
with open(filepath, encoding="utf-8") as f:
glob_id = 0
row = json.load(f)
for data in row:
yield glob_id, {
"id": data["id"],
"conversation": data["conversation"],
"correct1": data["correct1"],
"correct2": data["correct2"],
"incorrect1": data["incorrect1"],
"incorrect1_rule": data["incorrect1_rule"],
"incorrect2": data["incorrect2"],
"incorrect2_rule": data["incorrect2_rule"],
glob_id += 1