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mdia.py

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+# coding=utf-8
+# Copyright 2022 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.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA,
+                                      Licenses, Tasks)
+
+_CITATION = """\
+@misc{zhang2022mdia,
+    title={MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages},
+    author={Qingyu Zhang and Xiaoyu Shen and Ernie Chang and Jidong Ge and Pengke Chen},
+    year={2022},
+    eprint={2208.13078},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+_DATASETNAME = "mdia"
+
+_DESCRIPTION = """\
+This is a multilingual benchmark for dialogue generation containing real-life
+Reddit conversations (parent and response comment pairs) in 46 languages,
+including Indonesian, Tagalog and Vietnamese. English translations are also
+provided for comments.
+"""
+
+_HOMEPAGE = "https://github.com/DoctorDream/mDIA"
+
+_LANGUAGES = ["ind", "tgl", "vie"]
+
+_LICENSE = Licenses.CC_BY_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    "raw": "https://github.com/DoctorDream/mDIA/raw/master/datasets/raw.zip",
+    "translated": "https://github.com/DoctorDream/mDIA/raw/master/datasets/translated.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.DIALOGUE_SYSTEM, Tasks.MACHINE_TRANSLATION]  # DS, MT
+_SEACROWD_SCHEMA = {task.value: f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS}  # t2t
+_SUBSETS = [
+    "ind_dialogue",
+    "ind_eng",
+    "tgl_dialogue",
+    "tgl_eng",
+    "vie_dialogue",
+    "vie_eng",
+]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class MdiaDataset(datasets.GeneratorBasedBuilder):
+    """Multilingual benchmark for dialogue generation containing real-life Reddit conversations"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = []
+    for subset in _SUBSETS:
+        if "dialogue" in subset:
+            BUILDER_CONFIGS += [
+                SEACrowdConfig(
+                    name=f"{_DATASETNAME}_{subset}_source",
+                    version=SOURCE_VERSION,
+                    description=f"{_DATASETNAME} {subset} source schema",
+                    schema="source",
+                    subset_id=subset,
+                ),
+                SEACrowdConfig(
+                    name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA['DS']}",
+                    version=SEACROWD_VERSION,
+                    description=f"{_DATASETNAME} {subset} SEACrowd schema",
+                    schema=_SEACROWD_SCHEMA["DS"],
+                    subset_id=subset,
+                ),
+            ]
+        else:
+            BUILDER_CONFIGS += [
+                SEACrowdConfig(
+                    name=f"{_DATASETNAME}_{subset}_source",
+                    version=SOURCE_VERSION,
+                    description=f"{_DATASETNAME} {subset} source schema",
+                    schema="source",
+                    subset_id=subset,
+                ),
+                SEACrowdConfig(
+                    name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA['MT']}",
+                    version=SEACROWD_VERSION,
+                    description=f"{_DATASETNAME} {subset} SEACrowd schema",
+                    schema=_SEACROWD_SCHEMA["MT"],
+                    subset_id=subset,
+                ),
+            ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{_SUBSETS[0]}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "lang": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "source_body": datasets.Value("string"),
+                    "target_body": datasets.Value("string"),
+                    "link_id": datasets.Value("string"),
+                    "source_id": datasets.Value("string"),
+                    "target_id": datasets.Value("string"),
+                    "translated_source_body": datasets.Value("string"),
+                    "translated_target_body": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA["DS"]:  # same schema with _SEACROWD_SCHEMA["MT"]
+            features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]]  # text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        lang_map = {"ind": "id", "tgl": "tl", "vie": "vi"}
+        lang = lang_map[self.config.subset_id.split("_")[0]]
+
+        data_url = _URLS["translated"]
+        data_dir = Path(dl_manager.download_and_extract(data_url)) / "translated"
+        data_path = "{split}_data/{lang}2en_{split}.csv"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_path": data_dir / data_path.format(split="train", lang=lang),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_path": data_dir / data_path.format(split="test", lang=lang),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_path": data_dir / data_path.format(split="eval", lang=lang),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_path: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        df = pd.read_csv(data_path)
+
+        # source schema
+        if self.config.schema == "source":
+            for i, row in df.iterrows():
+                yield i, {
+                    "lang": row["lang"],
+                    "title": row["title"],
+                    "source_body": row["source_body"],
+                    "target_body": row["target_body"],
+                    "link_id": row["link_id"],
+                    "source_id": row["source_id"],
+                    "target_id": row["target_id"],
+                    "translated_source_body": row["translated_source_body"],
+                    "translated_target_body": row["translated_target_body"],
+                }
+
+        # t2t schema for dialogue
+        elif "dialogue" in self.config.subset_id:
+            for i, row in df.iterrows():
+                yield i, {
+                    "id": str(i),
+                    "text_1": row["source_body"],
+                    "text_2": row["target_body"],
+                    "text_1_name": "source_body",
+                    "text_2_name": "target_body",
+                }
+
+        # t2t schema for machine translation
+        elif "eng" in self.config.subset_id:
+            for i, row in df.iterrows():
+                for j in range(2):
+                    idx = i * 2 + j
+                    if j == 0:
+                        yield idx, {
+                            "id": str(idx),
+                            "text_1": row["source_body"],
+                            "text_2": row["translated_source_body"],
+                            "text_1_name": "source_body",
+                            "text_2_name": "translated_source_body",
+                        }
+                    else:
+                        yield idx, {
+                            "id": str(idx),
+                            "text_1": row["target_body"],
+                            "text_2": row["translated_target_body"],
+                            "text_1_name": "target_body",
+                            "text_2_name": "translated_target_body",
+                        }