Datasets:

google
/

air_dialogue

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+---
+annotations_creators:
+- human-annotated
+language_creators:
+- machine-generated
+languages:
+- en
+licenses:
+- cc-by-nc-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+- sequence-modeling
+task_ids:
+- conditional-text-generation-other-dialogue-generation
+- dialogue-modeling
+- language-modeling
+---
+# Dataset Card for air_dialogue
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+## Dataset Description
+- **Homepage:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
+- **Repository:** https://github.com/google/airdialogue
+- **Paper:** https://www.aclweb.org/anthology/D18-1419/
+- **Leaderboard:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
+- **Point of Contact:** [AirDialogue-Google](mailto:[email protected])
+[Aakash Gupta](mailto:[email protected])
+### Dataset Summary
+AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.
+### Supported Tasks and Leaderboards
+We use perplexity and BLEU score to evaluate the quality of the language generated by the model. We also compare the dialogue state generated by the model s and the ground truth state s0. Two categories of the metrics are used: exact match scores and scaled scores
+The inference competition & leaderboard can be found here:
+https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
+### Languages
+The text in the dataset is in English. The BCP 47 code is `en`
+## Dataset Structure
+### Data Instances
+The data is provided in two set of files. The first one has the dialogues (`air_dialogue_data`) and the knowledge-base (`air_dialogue_kb`)
+BuilderConfig: `air_dialogue_data`
+```
+{"action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "intent": {"return_month": "June", "return_day": "14", "max_price": 200, "departure_airport": "DFW", "return_time": "afternoon", "max_connections": 1, "departure_day": "12", "goal": "book", "departure_month": "June", "name": "Emily Edwards", "return_airport": "IAD"}, "timestamps": [1519233239, 1519233244, 1519233249, 1519233252, 1519233333, 1519233374, 1519233392, 1519233416, 1519233443, 1519233448, 1519233464, 1519233513, 1519233525, 1519233540, 1519233626, 1519233628, 1519233638], "dialogue": ["customer: Hello.", "agent: Hello.", "customer: My name is Emily Edwards.", "agent: How may I help you out?", "customer: I need some help in my flight ticket reservation to attend a convocation meeting, can you please help me?", "agent: Sure, I will help you out. May I know your travelling dates please?", "customer: Thank you and my dates are 06/12 and back on 06/14.", "agent: Can I know your airport codes?", "customer: The airport codes are from DFW to IAD.", "agent: Ok, please wait a moment.", "customer: Sure.", "agent: There is a flight with connection 1 and price 200, can I proceed with this flight?", "customer: Yes, do proceed with booking.", "agent: Ok, your ticket has been booked.", "customer: Thank you for your assistance in my flight ticket reservation.", "agent: Thank you for choosing us.", "customer: You are welcome."], "expected_action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "correct_sample": true}
+```
+BuilderConfig: `air_dialogue_kb`
+```
+{"kb": [{"return_airport": "DTW", "airline": "Spirit", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1000, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1001, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 15, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 500}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1002, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 13, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 600}, {"return_airport": "IAD", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1003, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 5, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1004, "departure_month": "June", "departure_time_num": 9, "class": "economy", "return_time_num": 11, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "AA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1005, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 17, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1006, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1007, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 20, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "AA", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1008, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1009, "departure_month": "June", "departure_time_num": 18, "class": "economy", "return_time_num": 6, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Frontier", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1010, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1011, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 100}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1012, "departure_month": "June", "departure_time_num": 13, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1013, "departure_month": "June", "departure_time_num": 16, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1014, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1015, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 300}, {"return_airport": "DTW", "airline": "UA", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1016, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1017, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1018, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1019, "departure_month": "June", "departure_time_num": 7, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1020, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 200}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1021, "departure_month": "June", "departure_time_num": 11, "class": "business", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 1000}, {"return_airport": "IAD", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1022, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 14, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 200}, {"return_airport": "IAD", "airline": "Frontier", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1023, "departure_month": "June", "departure_time_num": 19, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "UA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1024, "departure_month": "June", "departure_time_num": 11, "class": "economy", "return_time_num": 19, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Hawaiian", "departure_day": "11", "departure_airport": "IAD", "flight_number": 1025, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1026, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 300}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1027, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 15, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "IAD", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1028, "departure_month": "June", "departure_time_num": 23, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Spirit", "departure_day": "11", "departure_airport": "DTW", "flight_number": 1029, "departure_month": "June", "departure_time_num": 22, "class": "business", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 800}], "reservation": 0}
+```
+### Data Fields
+BuilderConfig: `air_dialogue_data`:
+Provides for customer context, dialogue states and environment
+key name | Description |
+|---|---|
+|'search_action' | search action performed by customer |
+|'action' | Action taken by the agent |
+|'intent' | Intents from the conversation |
+|'timestamps' | Timestamp for each of the dialogues |
+|'dialogue' | Dialogue recorded between agent & customer |
+|'expected_action' | Expected action from agent (human-annotated)|
+|'correct_sample' | whether action performed by agent was same as expected_action |
+BuilderConfig: `air_dialogue_kb`:
+Provides for the Agent Context _ca_ = (_db_, _r_ )
+key name | Description |
+|---|---|
+|'kb' | Available flights in the database |
+|'reservation' | whether customer has an existing reservation|
+### Data Splits
+Data is split into Train/Dev & Test in the ration of 80%, 10% and 10%
+## Dataset Creation
+### Curation Rationale
+[Needs More Information]
+### Source Data
+#### Initial Data Collection and Normalization
+[Needs More Information]
+#### Who are the source language producers?
+[Needs More Information]
+### Annotations
+#### Annotation process
+To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail.
+#### Who are the annotators?
+[Needs More Information]
+### Personal and Sensitive Information
+No personal and sensitive information is stored
+## Considerations for Using the Data
+### Social Impact of Dataset
+[Needs More Information]
+### Discussion of Biases
+[Needs More Information]
+### Other Known Limitations
+[Needs More Information]
+## Additional Information
+### Dataset Curators
+[AirDialogue team](mailto:[email protected])
+For issues regarding HuggingFace Dataset Hub implementation [Aakash Gupta](mailto:[email protected])
+### Licensing Information
+cc-by-nc-4.0
+### Citation Information
+@inproceedings{wei-etal-2018-airdialogue,
+    title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research",
+    author = "Wei, Wei  and
+      Le, Quoc  and
+      Dai, Andrew  and
+      Li, Jia",
+    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
+    month = oct # "-" # nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D18-1419",
+    doi = "10.18653/v1/D18-1419",
+    pages = "3844--3854",
+    abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.",
+}

air_dialogue.py ADDED Viewed

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+# 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.
+"""AirDialogue: A large dataset for goal oriented conversations."""
+from __future__ import absolute_import, division, print_function
+import json
+import os
+import datasets
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{wei-etal-2018-airdialogue,
+    title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research",
+    author = "Wei, Wei  and
+      Le, Quoc  and
+      Dai, Andrew  and
+      Li, Jia",
+    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
+    month = oct # "-" # nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D18-1419",
+    doi = "10.18653/v1/D18-1419",
+    pages = "3844--3854",
+    abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.",
+}
+"""
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.
+"""
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59"
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "cc-by-nc-4.0"
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLs = {
+    "air_dialogue_data": "https://storage.googleapis.com/airdialogue/airdialogue_data.tar.gz",
+    "air_dialogue_kb": "https://storage.googleapis.com/airdialogue/airdialogue_data.tar.gz",
+}
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class AirDialogue(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+    VERSION = datasets.Version("1.1.0")
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="air_dialogue_data", version=VERSION, description="This part of my dataset covers the dialog files"
+        ),
+        datasets.BuilderConfig(
+            name="air_dialogue_kb", version=VERSION, description="This part of my dataset covers the knowledge base"
+        ),
+    ]
+    DEFAULT_CONFIG_NAME = (
+        "air_dialogue_data"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    )
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if (
+            self.config.name == "air_dialogue_data"
+        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "action": {
+                        "status": datasets.Value("string"),
+                        "name": datasets.Value("string"),
+                        "flight": datasets.features.Sequence(datasets.Value("int32")),
+                    },
+                    "intent": {
+                        "return_month": datasets.Value("string"),
+                        "return_day": datasets.Value("string"),
+                        "max_price": datasets.Value("int32"),
+                        "departure_airport": datasets.Value("string"),
+                        "max_connections": datasets.Value("int32"),
+                        "departure_day": datasets.Value("string"),
+                        "goal": datasets.Value("string"),
+                        "departure_month": datasets.Value("string"),
+                        "name": datasets.Value("string"),
+                        "return_airport": datasets.Value("string"),
+                    },
+                    "timestamps": datasets.features.Sequence(datasets.Value("int64")),
+                    "dialogue": datasets.features.Sequence(datasets.Value("string")),
+                    "expected_action": {
+                        "status": datasets.Value("string"),
+                        "name": datasets.Value("string"),
+                        "flight": datasets.features.Sequence(datasets.Value("int32")),
+                    },
+                    "search_info": [
+                        {
+                            "button_name": datasets.Value("string"),
+                            "field_name": datasets.Value("string"),
+                            "field_value": datasets.Value("string"),
+                            "timestmamp": datasets.Value("int64"),
+                        },
+                    ],
+                    "correct_sample": datasets.Value("bool_"),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "kb": [
+                        {
+                            "airline": datasets.Value("string"),
+                            "class": datasets.Value("string"),
+                            "departure_airport": datasets.Value("string"),
+                            "departure_day": datasets.Value("string"),
+                            "departure_month": datasets.Value("string"),
+                            "departure_time_num": datasets.Value("int32"),
+                            "flight_number": datasets.Value("int32"),
+                            "num_connections": datasets.Value("int32"),
+                            "price": datasets.Value("int32"),
+                            "return_airport": datasets.Value("string"),
+                            "return_day": datasets.Value("string"),
+                            "return_month": datasets.Value("string"),
+                            "return_time_num": datasets.Value("int32"),
+                        },
+                    ],
+                    "reservation": datasets.Value("int32"),
+                }
+            )
+        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."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        if self.config.name == "air_dialogue_data":
+            train = "airdialogue_data/airdialogue/train_data.json"
+            dev = "airdialogue_data/airdialogue/dev_data.json"
+        else:
+            train = "airdialogue_data/airdialogue/train_kb.json"
+            dev = "airdialogue_data/airdialogue/dev_kb.json"
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, train),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, dev),
+                    "split": "dev",
+                },
+            ),
+        ]
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
+        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+        # The key is not important, it's more here for legacy reason (legacy from tfds)
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "air_dialogue_data":
+                    intent = {
+                        "return_month": data["intent"]["return_month"],
+                        "return_day": data["intent"]["return_day"],
+                        "max_price": data["intent"]["max_price"],
+                        "departure_airport": data["intent"]["departure_airport"],
+                        "max_connections": data["intent"].get("max_connections", -1),
+                        "departure_day": data["intent"]["departure_day"],
+                        "goal": data["intent"]["goal"],
+                        "departure_month": data["intent"]["departure_month"],
+                        "name": data["intent"]["name"],
+                        "return_airport": data["intent"]["return_airport"],
+                    }
+                    search_info = (
+                        []
+                        if "search_info" not in data
+                        else [
+                            {
+                                "button_name": search_info.get("button_name", ""),
+                                "field_name": search_info.get("field_name", ""),
+                                "field_value": search_info.get("field_value", ""),
+                                "timestmamp": search_info["timestmamp"],
+                            }
+                            for search_info in data["search_info"]
+                        ]
+                    )
+                    yield id_, {
+                        "action": {key: data["action"][key] for key in data["action"]},
+                        "intent": intent,
+                        "timestamps": data["timestamps"],
+                        "dialogue": data["dialogue"],
+                        "expected_action": {key: data["expected_action"][key] for key in data["expected_action"]},
+                        "search_info": search_info,
+                        "correct_sample": data["correct_sample"],
+                    }
+                else:
+                    kb = [
+                        {
+                            "airline": kb["airline"],
+                            "class": kb["class"],
+                            "departure_airport": kb["departure_airport"],
+                            "departure_day": kb["departure_day"],
+                            "departure_month": kb["departure_month"],
+                            "departure_time_num": kb["departure_time_num"],
+                            "flight_number": kb["flight_number"],
+                            "num_connections": kb["num_connections"],
+                            "price": kb["price"],
+                            "return_airport": kb["return_airport"],
+                            "return_day": kb["return_day"],
+                            "return_month": kb["return_month"],
+                            "return_time_num": kb["return_time_num"],
+                        }
+                        for kb in data["kb"]
+                    ]
+                    yield id_, {
+                        "kb": kb,
+                        "reservation": data["reservation"],
+                    }

dataset_infos.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"air_dialogue_data": {"description": "AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.\n", "citation": "@inproceedings{wei-etal-2018-airdialogue,\n title = \"{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research\",\n author = \"Wei, Wei and\n Le, Quoc and\n Dai, Andrew and\n Li, Jia\",\n booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\",\n month = oct # \"-\" # nov,\n year = \"2018\",\n address = \"Brussels, Belgium\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/D18-1419\",\n doi = \"10.18653/v1/D18-1419\",\n pages = \"3844--3854\",\n abstract = \"Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.\",\n}\n", "homepage": "https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59", "license": "cc-by-nc-4.0", "features": {"action": {"status": {"dtype": "string", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}, "flight": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "intent": {"return_month": {"dtype": "string", "id": null, "_type": "Value"}, "return_day": {"dtype": "string", "id": null, "_type": "Value"}, "max_price": {"dtype": "int32", "id": null, "_type": "Value"}, "departure_airport": {"dtype": "string", "id": null, "_type": "Value"}, "max_connections": {"dtype": "int32", "id": null, "_type": "Value"}, "departure_day": {"dtype": "string", "id": null, "_type": "Value"}, "goal": {"dtype": "string", "id": null, "_type": "Value"}, "departure_month": {"dtype": "string", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}, "return_airport": {"dtype": "string", "id": null, "_type": "Value"}}, "timestamps": {"feature": {"dtype": "int64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "dialogue": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "expected_action": {"status": {"dtype": "string", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}, "flight": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "search_info": [{"button_name": {"dtype": "string", "id": null, "_type": "Value"}, "field_name": {"dtype": "string", "id": null, "_type": "Value"}, "field_value": {"dtype": "string", "id": null, "_type": "Value"}, "timestmamp": {"dtype": "int64", "id": null, "_type": "Value"}}], "correct_sample": {"dtype": "bool_", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "air_dialogue", "config_name": "air_dialogue_data", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 353721137, "num_examples": 321459, "dataset_name": "air_dialogue"}, "validation": {"name": "validation", "num_bytes": 44442238, "num_examples": 40363, "dataset_name": "air_dialogue"}}, "download_checksums": {"https://storage.googleapis.com/airdialogue/airdialogue_data.tar.gz": {"num_bytes": 272898923, "checksum": "7d2130cdde73a59afd6ad6c463a25453d8ed677c1b3a4a4aaa2406db9c9712cb"}}, "download_size": 272898923, "post_processing_size": null, "dataset_size": 398163375, "size_in_bytes": 671062298}, "air_dialogue_kb": {"description": "AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.\n", "citation": "@inproceedings{wei-etal-2018-airdialogue,\n title = \"{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research\",\n author = \"Wei, Wei and\n Le, Quoc and\n Dai, Andrew and\n Li, Jia\",\n booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\",\n month = oct # \"-\" # nov,\n year = \"2018\",\n address = \"Brussels, Belgium\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/D18-1419\",\n doi = \"10.18653/v1/D18-1419\",\n pages = \"3844--3854\",\n abstract = \"Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.\",\n}\n", "homepage": "https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59", "license": "cc-by-nc-4.0", "features": {"kb": [{"airline": {"dtype": "string", "id": null, "_type": "Value"}, "class": {"dtype": "string", "id": null, "_type": "Value"}, "departure_airport": {"dtype": "string", "id": null, "_type": "Value"}, "departure_day": {"dtype": "string", "id": null, "_type": "Value"}, "departure_month": {"dtype": "string", "id": null, "_type": "Value"}, "departure_time_num": {"dtype": "int32", "id": null, "_type": "Value"}, "flight_number": {"dtype": "int32", "id": null, "_type": "Value"}, "num_connections": {"dtype": "int32", "id": null, "_type": "Value"}, "price": {"dtype": "int32", "id": null, "_type": "Value"}, "return_airport": {"dtype": "string", "id": null, "_type": "Value"}, "return_day": {"dtype": "string", "id": null, "_type": "Value"}, "return_month": {"dtype": "string", "id": null, "_type": "Value"}, "return_time_num": {"dtype": "int32", "id": null, "_type": "Value"}}], "reservation": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "air_dialogue", "config_name": "air_dialogue_kb", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 782592158, "num_examples": 321459, "dataset_name": "air_dialogue"}, "validation": {"name": "validation", "num_bytes": 98269789, "num_examples": 40363, "dataset_name": "air_dialogue"}}, "download_checksums": {"https://storage.googleapis.com/airdialogue/airdialogue_data.tar.gz": {"num_bytes": 272898923, "checksum": "7d2130cdde73a59afd6ad6c463a25453d8ed677c1b3a4a4aaa2406db9c9712cb"}}, "download_size": 272898923, "post_processing_size": null, "dataset_size": 880861947, "size_in_bytes": 1153760870}}

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+size 11287

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