Datasets:
Sub-tasks:
dialogue-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
crowdsourced
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
License:
# 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, | |
# 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. | |
"""TODO: Add a description here.""" | |
import json | |
import datasets | |
_CITATION = """\ | |
@misc{he2018decoupling, | |
title={Decoupling Strategy and Generation in Negotiation Dialogues}, | |
author={He He and Derek Chen and Anusha Balakrishnan and Percy Liang}, | |
year={2018}, | |
eprint={1808.09637}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CL} | |
} | |
""" | |
_DESCRIPTION = """\ | |
We study negotiation dialogues where two agents, a buyer and a seller, | |
negotiate over the price of an time for sale. We collected a dataset of more | |
than 6K negotiation dialogues over multiple categories of products scraped from Craigslist. | |
Our goal is to develop an agent that negotiates with humans through such conversations. | |
The challenge is to handle both the negotiation strategy and the rich language for bargaining. | |
""" | |
_HOMEPAGE = "https://stanfordnlp.github.io/cocoa/" | |
_LICENSE = "" | |
_URLs = { | |
"train": "https://worksheets.codalab.org/rest/bundles/0xd34bbbc5fb3b4fccbd19e10756ca8dd7/contents/blob/parsed.json", | |
"validation": "https://worksheets.codalab.org/rest/bundles/0x15c4160b43d44ee3a8386cca98da138c/contents/blob/parsed.json", | |
"test": "https://worksheets.codalab.org/rest/bundles/0x54d325bbcfb2463583995725ed8ca42b/contents/blob/", | |
} | |
class CraigslistBargains(datasets.GeneratorBasedBuilder): | |
""" | |
Dialogue for buyer and a seller negotiating | |
the price of an item for sale on Craigslist. | |
""" | |
VERSION = datasets.Version("1.1.0") | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"agent_info": datasets.features.Sequence( | |
{ | |
"Bottomline": datasets.Value("string"), | |
"Role": datasets.Value("string"), | |
"Target": datasets.Value("float"), | |
} | |
), | |
"agent_turn": datasets.features.Sequence(datasets.Value("int32")), | |
"dialogue_acts": datasets.features.Sequence( | |
{"intent": datasets.Value("string"), "price": datasets.Value("float")} | |
), | |
"utterance": datasets.features.Sequence(datasets.Value("string")), | |
"items": datasets.features.Sequence( | |
{ | |
"Category": datasets.Value("string"), | |
"Images": datasets.Value("string"), | |
"Price": datasets.Value("float"), | |
"Description": datasets.Value("string"), | |
"Title": datasets.Value("string"), | |
} | |
), | |
} | |
) | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# 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. | |
supervised_keys=None, | |
# Homepage of the dataset for documentation | |
homepage=_HOMEPAGE, | |
# License for the dataset if available | |
license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
my_urls = _URLs | |
data_dir = dl_manager.download_and_extract(my_urls) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": data_dir["train"], | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": data_dir["test"], "split": "test"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": data_dir["validation"], | |
"split": "validation", | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
"""Yields examples.""" | |
# Set default values for items when the information is missing | |
# `items` is the description of the item advertised on craigslist | |
# to which the conversation is referring | |
default_items = {"Category": "", "Images": "", "Price": -1.0, "Description": "", "Title": ""} | |
# Set default values for the rules-based `metadata` generated by | |
# the Stanford NLP Cocoa project for the Craigslist Bargains dataset | |
# For more information on producing the `metadata` values for the train | |
# and dev sets, see https://worksheets.codalab.org/bundles/0xd34bbbc5fb3b4fccbd19e10756ca8dd7 | |
default_metadata = {"price": -1.0, "intent": ""} | |
with open(filepath, encoding="utf-8") as f: | |
concat_sep = "," | |
jsons = json.loads(f.read()) | |
for id_, j in enumerate(jsons): | |
# Get scenario information. | |
# This is nformation about position of each agent | |
scenario = j.get("scenario") | |
kbs = scenario["kbs"] | |
agent_info = [kb["personal"] for kb in kbs] | |
agent_info = [{k: str(v) for k, v in ai.items()} for ai in agent_info] | |
# Get item information. | |
# This is information about item listing for each agent | |
items = [i["item"] for i in kbs] | |
# Flatten `list` elements in items | |
# (e.g. if there are multiple image names, descriptions...) | |
# to align more easily with arrow schema | |
for item in items: | |
for k in item: | |
if type(item[k]) == list: | |
item[k] = concat_sep.join(item[k]) | |
# Check for missing elements in `items` | |
# and fill with default values | |
for item in items: | |
for k in default_items: | |
if k not in item: | |
item[k] = default_items[k] | |
elif not item[k]: | |
item[k] = default_items[k] | |
# Get interaction information. | |
# This is information about messages exchanged | |
# and rules-based dialogue acts assigned to each | |
# dialogue segment | |
events = j.get("events") | |
agents = [e.get("agent") for e in events] | |
agents = [a if type(a) == int else -1 for a in agents] | |
data = [e.get("data") for e in events] | |
utterances = [u if type(u) == str else "" for u in data] | |
metadata = [e.get("metadata") for e in events] | |
metadata = [m if m else default_metadata for m in metadata] | |
# Check for missing keys in metadata, or missing | |
# metadata altogether for test data split. | |
# If anything missing, fill with defaults above. | |
for m in metadata: | |
for k in default_metadata: | |
if k not in m: | |
m[k] = default_metadata[k] | |
elif not m[k]: | |
m[k] = default_metadata[k] | |
yield id_, { | |
"agent_info": agent_info, | |
"agent_turn": agents, | |
"dialogue_acts": metadata, | |
"utterance": utterances, | |
"items": items, | |
} | |