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FaithDial.py

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-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor (Nouha Dziri).
-#
-# 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.
-"""FaithDial: A Faithful Benchmark for Information-Seeking Dialogue"""
-
-
-import json
-
-import datasets
-
-
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@article{dziri2022faithdial,
-  title={FaithDial: A Faithful Benchmark for Information-Seeking Dialogue},
-  author={Dziri, Nouha and Kamalloo, Ehsan and Milton, Sivan and Zaiane, Osmar and Yu, Mo and Ponti, Edoardo and Reddy, Siva},
-  journal={arXiv preprint, arXiv:2204.10757},
-  year={2022},
-  url={https://arxiv.org/abs/2204.10757}
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-FaithDial is a new benchmark for hallucination-free dialogues, created by manually editing hallucinated and uncooperative responses in Wizard of Wikipedia.
-"""
-
-_HOMEPAGE = "https://mcgill-nlp.github.io/FaithDial/"
-
-_LICENSE = "MIT"
-
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {
-    "train": "data/train.json",
-    "valid": "data/valid.json",
-    "valid_random_split": "data/valid_random_split.json",
-    "valid_topic_split": "data/valid_topic_split.json",
-    "test": "data/test.json",
-    "test_random_split": "data/test_random_split.json",
-    "test_topic_split": "data/test_topic_split.json",
-}
-
-
-class FaithDialDataset(datasets.GeneratorBasedBuilder):
-    """FaithDial is a new benchmark for hallucination-free dialogues."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    # 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="plain_text", version=VERSION, description="Plain text"),
-    ]
-
-    DEFAULT_CONFIG_NAME = (
-        "plain_text"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-    )
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "dialog_idx": datasets.Value("int32"),
-                "response": datasets.Value("string"),
-                "original_response": datasets.Value("string"),
-                "history": datasets.features.Sequence(datasets.Value("string")),
-                "knowledge": datasets.Value("string"),
-                "BEGIN": datasets.features.Sequence(datasets.Value("string")),
-                "VRM": datasets.features.Sequence(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, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # 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):
-        # 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
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-
-        split_dict = {
-            "train": datasets.Split.TRAIN,
-            "valid": datasets.Split.VALIDATION,
-            "test": datasets.Split.TEST,
-        }
-
-        return [
-            datasets.SplitGenerator(
-                name=split_dict.get(split, split),
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": downloaded_file,
-                    "split": split,
-                },
-            )
-            for split, downloaded_file in sorted(downloaded_files.items(), key=lambda x: x[0])
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            key = 0
-            for dialogue in data:
-                for utterance in dialogue["utterances"]:
-                    yield key, {
-                        "dialog_idx": dialogue["dialog_idx"],
-                        "response": utterance["response"],
-                        "original_response": utterance["original_response"],
-                        "history": utterance["history"],
-                        "knowledge": utterance["knowledge"],
-                        "BEGIN": utterance["BEGIN"],
-                        "VRM": utterance["VRM"],
-                    }
-                    key += 1

README.md

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----
-license:
-- mit
-annotations_creators:
-- crowdsourced
-language:
-- en
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100k
-task_categories:
-- text-generation
-- conversational
-task_ids:
-- dialogue-modeling
-- faithful-dialogue-modeling
-- trustworthy-dialogue-modeling
-pretty_name: A Faithful Benchmark for Information-Seeking Dialogue
----
-
-## Dataset Summary
-FaithDial is a faithful knowledge-grounded dialogue benchmark, composed of **50,761** turns spanning **5649** conversations. It was curated through Amazon Mechanical Turk by asking annotators to amend hallucinated utterances in [Wizard of Wikipedia](https://parl.ai/projects/wizard_of_wikipedia/) (WoW). In our dialogue setting, we simulate interactions between two speakers: **an information seeker** and **a bot wizard**.  The seeker has a large degree of freedom as opposed to the wizard bot which is more restricted on what it can communicate. In fact, it must abide by the following rules: 
-- **First**, it should be truthful by providing information that is attributable to the source knowledge *K*. 
-- **Second**, it should provide information conversationally, i.e., use naturalistic phrasing of *K*, support follow-on discussion with questions, and prompt user's opinions. 
-- **Third**, it should acknowledge its ignorance of the answer in those cases where *K* does not include it while still moving the conversation forward using *K*.
-
-## Dataset Description
-
-- **Homepage:** [FaithDial](https://mcgill-nlp.github.io/FaithDial/)
-- **Repository:** [GitHub](https://github.com/McGill-NLP/FaithDial)
-- **Point of Contact:** [Nouha Dziri](mailto:dziri@ualberta.ca)
-
-
-## Language
-English
-
-## Data Instance
-
-An example of 'train' looks as follows: 
-```text
-[
-  {
-    "utterances": [
-      ... // prior utterances, 
-      {
-        "history": [
-          "Have you ever been to a concert? They're so fun!",
-          "No I cannot as a bot. However, have you been to Madonna's? Her 10th concert was used to help her 13th album called \"Rebel Heart\".",
-          "Yeah I've heard of it but never went or what it was for. Can you tell me more about it?"
-        ],
-        "speaker": "Wizard",
-        "knowledge": "It began on September 9, 2015, in Montreal, Canada, at the Bell Centre and concluded on March 20, 2016, in Sydney, Australia at Allphones Arena.",
-        "original_response": "It started in September of 2015 and ran all the way through March of 2016. Can you imagine being on the road that long?",
-        "response": "Sure. The concert started in September 9th of 2015 at Montreal, Canada. It continued till 20th of March of 2016, where it ended at Sydney, Australia.",
-        "BEGIN": [
-          "Hallucination",
-          "Entailment"
-        ],
-        "VRM": [
-          "Disclosure",
-          "Question"
-        ]
-      }, 
-      ... // more utterances
-    ]
-  }, 
-  ... // more dialogues
-]
-```
-If the `original_response` is empty, it means that the response is faithful to the source and we consider it as a FaithDial response. Faithful responses in WoW are also edited slightly if they are found to have some grammatical issues or typos. 
-## Data Fields
-
- - `history`: `List[string]`. The dialogue history.
- - `knowledge`: `string`. The source knowkedge on which the bot wizard should ground its response.
- - `speaker`:  `string`.  The current speaker.
- - `original response`:  `string`.  The WoW original response before editing it.
- - `response`:   `string`.  The new Wizard response.
- - `BEGIN`:   `List[string]`. The BEGIN labels for the Wizard response.
- - `VRM`:  `List[string]`. The VRM labels for the wizard response.
-
-
-## Data Splits
-
-- `Train`: 36809 turns
-- `Valid`: 6851 turns
-- `Test`: 7101 turns
-
-`Valid` includes both the `seen` and the `unseen` data splits from WoW. The same applies to `Test`. We also include those splits for FaithDial valid and test data. 
-
-## Annotations
-Following the guidelines for ethical crowdsourcing outlined in [Sheehan. 2018](https://www.tandfonline.com/doi/abs/10.1080/03637751.2017.1342043), 
-we hire Amazon Mechanical Turk (AMT) workers to edit utterances in WoW dialogues that were found to exhibit unfaithful responses. To ensure clarity in the task definition, we provided detailed examples for our terminology. Moreover, we performed several staging rounds over the course of several months.
-
-# Who are the annotators?
-
-To be eligible for the task, workers have to be located in the United States and Canada and have to answer successfully 20 questions as part of a qualification test.  Before launching the main annotation task,  we perform a small pilot round (60 HITS) to check the performance of the workers. We email workers who commit errors, providing them with examples on how to fix their mistakes in future HITS. 
-
-## Personal and Sensitive Information
-Seeker utterances in FaithDial may contain personal and sensitive information. 
-
-## Social Impact of Dataset
- In recent years, the conversational AI market has seen
-a proliferation of a variety of applications—which are powered by large pre-trained LMs—that span
-across a broad range of domains, such as customer
-support, education, e-commerce, health, entertainment, etc. Ensuring that
-these systems are trustworthy is key to deploy systems safely at a large scale in real-world application, especially in high-stake domain. FaithDial holds promise to encourage faithfulness in information-seeking dialogue and make virtual assistants both safer and more reliable. 
-
-## Licensing Information
-
-MIT
-
-## Citation Information
-
-```bibtex
-@article{dziri2022faithdial,
-  title={FaithDial: A Faithful Benchmark for Information-Seeking Dialogue},
-  author={Dziri, Nouha and Kamalloo, Ehsan and Milton, Sivan and Zaiane, Osmar and Yu, Mo and Ponti, Edoardo and Reddy, Siva},
-  journal={arXiv preprint, arXiv:2204.10757},
-  year={2022},
-  url={https://arxiv.org/abs/2204.10757}
-}
-```
-

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