Datasets:
Tasks:
Question Answering
Sub-tasks:
open-domain-qa
Languages:
English
Multilinguality:
monolingual
Annotations Creators:
crowdsourced
Source Datasets:
extended|doc2dial
ArXiv:
License:
File size: 15,996 Bytes
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# 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.
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents"""
import json
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{feng2021multidoc2dial,
title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents},
author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra},
booktitle={EMNLP},
year={2021}
}
"""
_DESCRIPTION = """\
MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. \
Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a \
single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking \
conversation involves multiple topics, and hence is grounded on different documents.
"""
_HOMEPAGE = "https://doc2dial.github.io/multidoc2dial/"
_URL = "https://doc2dial.github.io/multidoc2dial/file/multidoc2dial.zip"
class MultiDoc2dial(datasets.GeneratorBasedBuilder):
"""MultiDoc2Dial v1.0"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="dialogue_domain",
version=VERSION,
description="This part of the dataset covers the dialogue domain that has questions, answers and the associated doc ids",
),
datasets.BuilderConfig(
name="document_domain",
version=VERSION,
description="This part of the dataset covers the document domain which details all the documents in the various domains",
),
datasets.BuilderConfig(
name="multidoc2dial",
version=VERSION,
description="Load MultiDoc2Dial dataset for machine reading comprehension tasks",
),
]
DEFAULT_CONFIG_NAME = "multidoc2dial"
def _info(self):
if self.config.name == "dialogue_domain":
features = datasets.Features(
{
"dial_id": datasets.Value("string"),
"domain": datasets.Value("string"),
"turns": [
{
"turn_id": datasets.Value("int32"),
"role": datasets.Value("string"),
"da": datasets.Value("string"),
"references": [
{
"id_sp": datasets.Value("string"),
"label": datasets.Value("string"),
"doc_id": datasets.Value("string"),
}
],
"utterance": datasets.Value("string"),
}
],
}
)
elif "document_domain" in self.config.name:
features = datasets.Features(
{
"domain": datasets.Value("string"),
"doc_id": datasets.Value("string"),
"title": datasets.Value("string"),
"doc_text": datasets.Value("string"),
"spans": [
{
"id_sp": datasets.Value("string"),
"tag": datasets.Value("string"),
"start_sp": datasets.Value("int32"),
"end_sp": datasets.Value("int32"),
"text_sp": datasets.Value("string"),
"title": datasets.Value("string"),
"parent_titles": datasets.features.Sequence(
{
"id_sp": datasets.Value("string"),
"text": datasets.Value("string"),
"level": datasets.Value("string"),
}
),
"id_sec": datasets.Value("string"),
"start_sec": datasets.Value("int32"),
"text_sec": datasets.Value("string"),
"end_sec": datasets.Value("int32"),
}
],
"doc_html_ts": datasets.Value("string"),
"doc_html_raw": datasets.Value("string"),
}
)
else:
features = datasets.Features(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"da": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
"utterance": datasets.Value("string"),
"domain": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URL)
if self.config.name == "dialogue_domain":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_dial_train.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_dial_validation.json"),
},
),
]
elif self.config.name == "document_domain":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_doc.json"),
},
)
]
elif "multidoc2dial_" in self.config.name:
domain = self.config.name.split("_")[-1]
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(
data_dir,
"multidoc2dial_domain",
domain,
"multidoc2dial_dial_validation.json",
),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(
data_dir,
"multidoc2dial_domain",
domain,
"multidoc2dial_dial_train.json",
),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(
data_dir,
"multidoc2dial_domain",
domain,
"multidoc2dial_dial_test.json",
),
},
),
]
elif self.config.name == "multidoc2dial":
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_dial_validation.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_dial_train.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_dial_test.json"),
},
),
]
def _load_doc_data_rc(self, filepath):
doc_filepath = os.path.join(os.path.dirname(filepath), "multidoc2dial_doc.json")
with open(doc_filepath, encoding="utf-8") as f:
data = json.load(f)["doc_data"]
return data
def _get_answers_rc(self, references, spans, doc_text):
"""Obtain the grounding annotation for a given dialogue turn"""
if not references:
return []
start, end = -1, -1
ls_sp = []
for ele in references:
id_sp = ele["id_sp"]
start_sp, end_sp = spans[id_sp]["start_sp"], spans[id_sp]["end_sp"]
if start == -1 or start > start_sp:
start = start_sp
if end < end_sp:
end = end_sp
ls_sp.append(doc_text[start_sp:end_sp])
answer = {"text": doc_text[start:end], "answer_start": start}
return [answer]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
if self.config.name == "dialogue_domain":
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for domain in data["dial_data"]:
for dialogue in data["dial_data"][domain]:
x = {
"dial_id": dialogue["dial_id"],
"turns": dialogue["turns"],
"domain": domain,
}
yield dialogue["dial_id"], x
elif self.config.name == "document_domain":
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for domain in data["doc_data"]:
for doc_id in data["doc_data"][domain]:
yield doc_id, {
"domain": domain,
"doc_id": doc_id,
"title": data["doc_data"][domain][doc_id]["title"],
"doc_text": data["doc_data"][domain][doc_id]["doc_text"],
"spans": [
{
"id_sp": data["doc_data"][domain][doc_id]["spans"][i]["id_sp"],
"tag": data["doc_data"][domain][doc_id]["spans"][i]["tag"],
"start_sp": data["doc_data"][domain][doc_id]["spans"][i]["start_sp"],
"end_sp": data["doc_data"][domain][doc_id]["spans"][i]["end_sp"],
"text_sp": data["doc_data"][domain][doc_id]["spans"][i]["text_sp"],
"title": data["doc_data"][domain][doc_id]["spans"][i]["title"],
"parent_titles": data["doc_data"][domain][doc_id]["spans"][i]["parent_titles"],
"id_sec": data["doc_data"][domain][doc_id]["spans"][i]["id_sec"],
"start_sec": data["doc_data"][domain][doc_id]["spans"][i]["start_sec"],
"text_sec": data["doc_data"][domain][doc_id]["spans"][i]["text_sec"],
"end_sec": data["doc_data"][domain][doc_id]["spans"][i]["end_sec"],
}
for i in data["doc_data"][domain][doc_id]["spans"]
],
"doc_html_ts": data["doc_data"][domain][doc_id]["doc_html_ts"],
"doc_html_raw": data["doc_data"][domain][doc_id]["doc_html_raw"],
}
elif "multidoc2dial" in self.config.name:
logger.info("generating examples from = %s", filepath)
doc_data = self._load_doc_data_rc(filepath)
d_doc_data = {}
for domain, d_doc in doc_data.items():
for doc_id, data in d_doc.items():
d_doc_data[doc_id] = data
with open(filepath, encoding="utf-8") as f:
dial_data = json.load(f)["dial_data"]
for domain, dialogues in dial_data.items():
for dial in dialogues:
all_prev_utterances = []
for idx, turn in enumerate(dial["turns"]):
doc_id = turn["references"][0]["doc_id"]
doc = d_doc_data[doc_id]
utterance_line = turn["utterance"].replace("\n", " ").replace("\t", " ")
all_prev_utterances.append("{}: {}".format(turn["role"], utterance_line))
if turn["role"] == "agent":
continue
if idx + 1 < len(dial["turns"]):
if (
dial["turns"][idx + 1]["role"] == "agent"
and dial["turns"][idx + 1]["da"] != "respond_no_solution"
):
turn_to_predict = dial["turns"][idx + 1]
else:
continue
else:
continue
question_str = utterance_line + "[SEP]" + "||".join(reversed(all_prev_utterances[:-1]))
id_ = "{}_{}".format(dial["dial_id"], turn["turn_id"])
qa = {
"id": id_,
"title": doc_id,
"context": doc["doc_text"],
"question": question_str,
"da": turn["da"],
"answers": self._get_answers_rc(
turn_to_predict["references"],
doc["spans"],
doc["doc_text"],
),
"utterance": turn_to_predict["utterance"],
"domain": domain,
}
yield id_, qa
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