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README.md

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+---
+language:
+- en
+pretty_name: DiPlomat
+license: cc-by-nc-sa-4.0
+---
+# The Dataset Presented Here is NOT Ready, please download from the [website](https://diplomat-dataset.github.io)
+# DiPlomat
+
+<!-- Provide a quick summary of the dataset. -->
+
+Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life 
+conversations and is essential for the development of communicative social agents. 
+In this paper, we introduce a novel challenge, **DiPlomat**, aiming at benchmarking machines’ capabilities 
+on pragmatic reasoning and situated conversational understanding. 
+Compared with previous works that treat different figurative expressions 
+(e.g. metaphor, sarcasm) as individual tasks, **DiPlomat** provides a cohesive framework 
+towards general pragmatic understanding.
+## Dataset Details
+The **DiPlomat** dataset owns 4,177 data and covers a vocabulary of 48,900 words. 
+More than that, human-annotated answers reach an amount of 6,494, 
+hold a vocabulary size of 20,000, and cover 5 types of reasoning. 
+Along with the dataset, we propose two tasks:  
+Pragmatic Identification and Reasoning (PIR) and Conversational Question Answering. Furthermore, we provide the
+data that we use for zero-NLI in the [paper](https://arxiv.org/abs/2306.09030).
+
+
+- **Language(s) (NLP):** [English]
+- **License:** [CC BY-NC-SA (Attribution-NonCommercial-ShareAlike)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+### Dataset Sources 
+
+<!-- Provide the basic links for the dataset. -->
+
+- **[Repository](https://github.com/diplomat-dataset/diplomat)** 
+- **Paper:** [DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning](https://arxiv.org/abs/2306.09030)
+
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+We provide 5 fields. ``PIR_first_subtask_dataset``, ``PIR_second_subtask_dataset``is for the pragmatic 
+identification and reasoning (PIR) task, and ``CQA_task_dataset`` is for the conversational question answering (CQA)
+task. As for ``NLI_task_dataset`` and ``NLI_dataset_without_context``, they are data for zero-NLI, while the latter
+is a context-removed version.
+
+
+
+
+## Dataset Creation
+
+
+### Source Data
+We leverage the data of [INTERVIEW dataset](https://www.kaggle.com/datasets/shuyangli94/interview-npr-media-dialog-transcripts) collected by 
+Majumder et al as our source.
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+### Annotating Process
+
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+#### Step I. Automatic Selection:
+The extensive size of the source dataset introduces redundancy, 
+and thus requires automatic measures to alleviate the burden of human annotation. 
+Therefore, we employ algorithms and models to perform an initial filtering process.
+#### Step II. Fine-grained Annotation:
+We leverage Amazon Mechanical Turk to conduct detailed annotations of pragmatic turns within our dialogues. 
+Workers participating in the annotation task are instructed to select 
+all turns that exhibit a divergence between their literal meaning and their intended meaning. 
+Due to the subjective nature of pragmatic reasoning, we request the workers to provide confidence scores 
+along with  reasons for their choices.
+#### Step III. Human Refinement:
+In this process, tasks for workers are formulated as multiple-choice questions. 
+Previously collected human-annotated reasons are transformed into choices, utilizing a template format: 
+[turn {turn_id}: {reason}]. In addition, to mitigate the impact of careless workers, 
+we introduce a distractor choice for each gold choice.
+
+
+
+
+## Citation 
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+```
+@inproceedings{li2023diplomat,
+    title={DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning},
+    author={Hengli Li, Song-Chun Zhu, Zilong Zheng},
+    booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
+    year={2023}
+}
+```
+
+## Dataset Card Contact
+
+If there is any problem with the dataset, please email [lihengli@stu.pku.edu.cn](mailto: 2000017754@stu.pku.edu.cn).

diplomat-dataset.py

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+# 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: Address all TODOs and remove all explanatory comments
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{li2023diplomat,
+title={DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning},
+author={Hengli Li, Song-Chun Zhu, Zilong Zheng},
+booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
+year={2023}
+}
+
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations and is essential for the development of communicative social agents. In this paper, we introduce a novel challenge, DiPlomat, aiming at benchmarking machines’ capabilities on pragmatic reasoning and situated conversational understanding. Compared with previous works that treat different figurative expressions (e.g. metaphor, sarcasm) as individual tasks, DiPlomat provides a cohesive framework towards general pragmatic understanding.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://diplomat-dataset.github.io"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "CC BY-NC-SA (Attribution-NonCommercial-ShareAlike)"
+
+# TODO: Add link to the official dataset URLs here
+# 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 = {
+    "PIR_first": "https://huggingface.co/great-new-dataset-first_domain.zip",
+    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class Diplomat(datasets.GeneratorBasedBuilder):
+    """This is the DiPlomat Dataset focusing on pragmatic reasoning."""
+
+    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="PIR_first", version=VERSION, description="This part of dataset covers the Pragmatic Identification and Reasoning Task Subtask 1"),
+        datasets.BuilderConfig(name="PIR_second", version=VERSION, description="This part of dataset covers the Pragmatic Identification and Reasoning Task Subtask 2"),
+        datasets.BuilderConfig(name="CQA", version=VERSION, description="This part of dataset covers the Conversational Question Answering Task"),
+        datasets.BuilderConfig(name="NLI_without_context", version=VERSION, description="This part of  dataset covers the Zero-Shot Natural Language Inference Task"),
+        datasets.BuilderConfig(name="NLI_with_context", version=VERSION, description="This part of dataset covers the Zero-Shot Natural Language Inference Task")
+        ]
+    DEFAULT_CONFIG_NAME = "PIR_first"  # 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
+        # This is the name of the configuration selected in BUILDER_CONFIGS above
+        print("======================================")
+        print(os.getcwd())
+        if self.config.name == "PIR_first":
+            features = datasets.Features(
+                {
+                    "text": datasets.Sequence(datasets.Value("string")),
+                    "speaker": datasets.Sequence(datasets.Value("string")),
+                    "correct_turn_number":datasets.Sequence(datasets.Value("int64")), 
+                }
+            )
+        elif self.config.name == "PIR_second":
+            features = datasets.Features(
+                {
+                    "text": datasets.Sequence(datasets.Value("string")),
+                    "speaker": datasets.Sequence(datasets.Value("string")),
+                    "correct_turn_number":datasets.Value("int64"), 
+                    "label": datasets.Value("int64"),
+                    "choice": datasets.Sequence(datasets.Value("string")),
+                }
+            )
+        elif self.config.name == "CQA":  
+            features = datasets.Features(
+                {
+                    "text": datasets.Sequence(datasets.Value("string")),
+                    "speaker": datasets.Sequence(datasets.Value("string")),
+                    "gold_statement": datasets.Value("string"),
+                    "questions": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "NLI_without_context":
+            features = datasets.Features(
+                    {
+                        "text": datasets.Value("string"),
+                        "hypothesis": datasets.Value("string"),
+                    }
+                )
+
+        elif self.config.name == "NLI_with_context":
+            features = datasets.Features(
+                {
+                    "dialogue": datasets.Sequence(datasets.Value("string")),
+                    "speaker": datasets.Sequence(datasets.Value("string")),
+                    "human answer": datasets.Value("string"),
+                }
+            )
+        else:
+            raise ValueError("Unknown configuration name selected")
+
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,  # Here we define them above because they are different between the two configurations
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+
+        if self.config.name == "PIR_first":
+            data_dir = "./diplomat-dataset/PIR_first_subtask_dataset"
+        elif self.config.name == "PIR_second":
+            data_dir = "./diplomat-dataset/PIR_second_subtask_dataset"
+        elif self.config.name == "CQA":
+            data_dir = "./diplomat-dataset/CQA_task_dataset"
+        elif self.config.name == "NLI_without_context":
+            data_dir = "./diplomat-dataset/NLI_dataset_without_context"
+        elif self.config.name == "NLI_with_context":
+            data_dir = "./diplomat-dataset/NLI_task_dataset"
+        else:
+            raise ValueError("Unknown configuration name selected")
+
+        if "NLI" not in self.config.name:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "train.json"),
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "val.json"),
+                        "split": "val",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "test.json"),
+                        "split": "test"
+                    },
+                ),
+            ]
+        else:
+            return [
+                    datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "dataset.json"),
+                        "split": "train",
+                    },
+                ),
+                ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        print(os.getcwd())
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                if self.config.name == "PIR_first":
+                    yield key,{
+                            "text": data['text'],
+                            "speaker": data['speaker'],
+                            "correct_turn_number":data['correct_turn_number'],
+                    }
+                elif self.config.name == "PIR_second":
+                    yield key, {
+                            "text": data['text'],
+                            "speaker": data['speaker'],
+                            "correct_turn_number":data['correct_turn_number'],
+                            "label": data['label'],
+                            "choice": data['choice'],
+                            }
+                elif self.config.name == "CQA":
+                    yield key,{
+                            "text": data['text'],
+                            "speaker": data['speaker'],
+                            "gold_statement": data['gold_statement'],
+                            "questions": data['questions'],
+                            "answer": data['answer'],
+                    }
+                elif self.config.name == "NLI_without_context":
+                    yield key,{
+                            "text": data['text'],
+                            "hypothesis": data['hypothesis'],
+                    }
+                elif self.config.name == "NLI_with_context":
+                    yield key,{
+                            "dialogue": data['dialogue'],
+                            "speaker": data['speaker'],
+                            "human answer": data['human answer'],
+                    }
+                else:
+                    raise ValueError("Unknown configuration name selected")
+
+