Add validation and test splits (#2)

- added details for downloading the dev and test sets (58e2235ff5d3b3eb35349affefa46ffdedcda2f1)
- Fix bug in script (883841d3a6af249c43ab0d93a0d601c8d12b68cb)
- Fix dataset_info metadata (cde229ddd27c8d336b6d70e409bfa3bf3bfb7647)
- Revert update of JSON metadata (db96be98c0d2bc8c4ded26262c50820ec2ea7654)
- Update dataset card (7c71f7f42df2ba2b81ed226d20c9778985090b73)

README.md

@@ -8,7 +8,7 @@ language:
 - en
 - yo
 license:
-- cc-by-4.0
+- cc-by-nc-4.0
 multilinguality:
 - translation
 size_categories:
@@ -18,7 +18,7 @@ source_datasets:
 task_categories:
 - translation
 task_ids: []
-paperswithcode_id: null
+paperswithcode_id: menyo-20k
 pretty_name: MENYO-20k
 dataset_info:
   features:
@@ -31,10 +31,16 @@ dataset_info:
   config_name: menyo20k_mt
   splits:
   - name: train
-    num_bytes: 2551273
+    num_bytes: 2551345
     num_examples: 10070
-  download_size: 2490852
-  dataset_size: 2551273
+  - name: validation
+    num_bytes: 870011
+    num_examples: 3397
+  - name: test
+    num_bytes: 1905432
+    num_examples: 6633
+  download_size: 5206234
+  dataset_size: 5326788
 ---
 
 # Dataset Card for MENYO-20k
@@ -65,15 +71,15 @@ dataset_info:
 
 ## Dataset Description
 
-- **Homepage:** [Homepage for Menyo-20k](https://zenodo.org/record/4297448#.X81G7s0zZPY)
-- **Repository:**[Github Repo](https://github.com/dadelani/menyo-20k_MT)
-- **Paper:**
+- **Homepage:**
+- **Repository:** https://github.com/uds-lsv/menyo-20k_MT/
+- **Paper:** [The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation](https://arxiv.org/abs/2103.08647)
 - **Leaderboard:**
 - **Point of Contact:**
 
 ### Dataset Summary
 
-MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain)
+MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain).
 
 ### Supported Tasks and Leaderboards
 
@@ -81,32 +87,32 @@ MENYO-20k is a multi-domain parallel dataset with texts obtained from news artic
 
 ### Languages
 
-Languages are English and YOruba
+Languages are English and Yoruba.
 
 ## Dataset Structure
 
 ### Data Instances
 
-The data consists of tab seperated entries
+An instance example:
 
 ```
-{'translation': 
+{'translation':
   {'en': 'Unit 1: What is Creative Commons?',
   'yo': 'Ìdá 1: Kín ni Creative Commons?'
   }
 }
-
 ```
 
 ### Data Fields
 
-- `en`: English sentence
-- `yo`: Yoruba sentence
+- `translation`:
+  - `en`: English sentence.
+  - `yo`: Yoruba sentence.
 
 
 ### Data Splits
 
-Only training dataset available
+Training, validation and test splits are available.
 
 ## Dataset Creation
 
@@ -160,27 +166,34 @@ Only training dataset available
 
 ### Licensing Information
 
-The dataset is open but for non-commercial use because some of the data sources like Ted talks and JW news requires permission for commercial use.
+The dataset is open but for non-commercial use because some data sources like Ted talks and JW news require permission for commercial use.
+
+The dataset is licensed under Creative Commons [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) License: https://github.com/uds-lsv/menyo-20k_MT/blob/master/LICENSE
 
 ### Citation Information
+
+If you use this dataset, please cite this paper:
 ```
-@dataset{david_ifeoluwa_adelani_2020_4297448,
-  author       = {David Ifeoluwa Adelani and
-                  Jesujoba O. Alabi and
-                  Damilola Adebonojo and
-                  Adesina Ayeni and
-                  Mofe Adeyemi and
-                  Ayodele Awokoya},
-  title        = {{MENYO-20k: A Multi-domain English - Yorùbá Corpus 
-                   for Machine Translation}},
-  month        = nov,
-  year         = 2020,
-  publisher    = {Zenodo},
-  version      = {1.0},
-  doi          = {10.5281/zenodo.4297448},
-  url          = {https://doi.org/10.5281/zenodo.4297448}
+@inproceedings{adelani-etal-2021-effect,
+    title = "The Effect of Domain and Diacritics in {Y}oruba{--}{E}nglish Neural Machine Translation",
+    author = "Adelani, David  and
+      Ruiter, Dana  and
+      Alabi, Jesujoba  and
+      Adebonojo, Damilola  and
+      Ayeni, Adesina  and
+      Adeyemi, Mofe  and
+      Awokoya, Ayodele Esther  and
+      Espa{\~n}a-Bonet, Cristina",
+    booktitle = "Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track)",
+    month = aug,
+    year = "2021",
+    address = "Virtual",
+    publisher = "Association for Machine Translation in the Americas",
+    url = "https://aclanthology.org/2021.mtsummit-research.6",
+    pages = "61--75",
+    abstract = "Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs. However and these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper and we present MENYO-20k and the first multi-domain parallel corpus with a especially curated orthography for Yoruba{--}English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test set and its subdomains. Since these pre-trained models use huge amounts of data with uncertain quality and we also analyze the effect of diacritics and a major characteristic of Yoruba and in the training data. We investigate how and when this training condition affects the final quality of a translation and its understandability.Our models outperform massively multilingual models such as Google ($+8.7$ BLEU) and Facebook M2M ($+9.1$) when translating to Yoruba and setting a high quality benchmark for future research.",
 }
 ```
 ### Contributions
 
-Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
+Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.

menyo20k_mt.py

@@ -55,13 +55,19 @@ _LICENSE = "For non-commercial use because some of the data sources like Ted tal
 
 # 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)
-_URL = "https://raw.githubusercontent.com/uds-lsv/menyo-20k_MT/master/data/train.tsv"
+_URLS = {
+        "train": "https://raw.githubusercontent.com/uds-lsv/menyo-20k_MT/master/data/train.tsv",
+        "dev": "https://raw.githubusercontent.com/uds-lsv/menyo-20k_MT/master/data/dev.tsv",
+        "test": "https://raw.githubusercontent.com/uds-lsv/menyo-20k_MT/master/data/test.tsv",
+    }
+
+
 
 
 class Menyo20kMt(datasets.GeneratorBasedBuilder):
     """MENYO-20k: A Multi-domain English - Yorùbá Corpus for Machine Translations"""
 
-    VERSION = datasets.Version("1.0.0")
+    VERSION = datasets.Version("1.1.0")
 
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
@@ -89,10 +95,11 @@ class Menyo20kMt(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        train_path = dl_manager.download_and_extract(_URL)
-
+        data_files = dl_manager.download(_URLS)
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
         ]
 
     def _generate_examples(self, filepath):