format adjustment + add readme

README.md

@@ -0,0 +1,223 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+license:
+- cc-by-4.0
+multilinguality:
+- ar
+- de
+- ja
+- hi
+- pt
+- en
+- es
+- it
+- fr
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+paperswithcode_id: mintaka
+pretty_name: Mintaka
+language_bcp47:
+- ar-SA
+- de-DE
+- ja-JP
+- hi-HI
+- pt-PT
+- en-EN
+- es-ES
+- it-IT
+- fr-FR
+---
+
+# MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+- **Homepage:** https://github.com/alexa/massive
+- **Repository:** https://github.com/alexa/massive
+- **Paper:** https://aclanthology.org/2022.coling-1.138/
+- **Point of Contact:** [GitHub](https://github.com/alexa/massive/issues)
+
+### Dataset Summary
+
+Mintaka is a complex, natural, and multilingual question answering (QA) dataset composed of 20,000 question-answer pairs elicited from MTurk workers and annotated with Wikidata question and answer entities. Full details on the Mintaka dataset can be found in our paper: https://aclanthology.org/2022.coling-1.138/
+
+To build Mintaka, we explicitly collected questions in 8 complexity types, as well as generic questions:
+
+- Count (e.g., Q: How many astronauts have been elected to Congress? A: 4)
+- Comparative (e.g., Q: Is Mont Blanc taller than Mount Rainier? A: Yes)
+- Superlative (e.g., Q: Who was the youngest tribute in the Hunger Games? A: Rue)
+- Ordinal (e.g., Q: Who was the last Ptolemaic ruler of Egypt? A: Cleopatra)
+- Multi-hop (e.g., Q: Who was the quarterback of the team that won Super Bowl 50? A: Peyton Manning)
+- Intersection (e.g., Q: Which movie was directed by Denis Villeneuve and stars Timothee Chalamet? A: Dune)
+- Difference (e.g., Q: Which Mario Kart game did Yoshi not appear in? A: Mario Kart Live: Home Circuit)
+- Yes/No (e.g., Q: Has Lady Gaga ever made a song with Ariana Grande? A: Yes.)
+- Generic (e.g., Q: Where was Michael Phelps born? A: Baltimore, Maryland)
+- We collected questions about 8 categories: Movies, Music, Sports, Books, Geography, Politics, Video Games, and History
+
+Mintaka is one of the first large-scale complex, natural, and multilingual datasets that can be used for end-to-end question-answering models.
+
+### Supported Tasks and Leaderboards
+
+The dataset can be used to train a model for question answering.
+To ensure comparability, please refer to our evaluation script here: https://github.com/amazon-science/mintaka#evaluation
+
+### Languages
+
+All questions were written in English and translated into 8 additional languages: Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish.
+
+## Dataset Structure
+
+### Data Instances
+
+An example of 'train' looks as follows.
+
+```json
+{
+  "id": "a9011ddf",
+  "lang": "en",
+  "question": "What is the seventh tallest mountain in North America?",
+  "answerText": "Mount Lucania",
+  "category": "geography",
+  "complexityType": "ordinal",
+  "questionEntity":
+  [
+      {
+          "name": "Q49",
+          "entityType": "entity",
+          "label": "North America",
+          "mention": "North America",
+          "span": [40, 53]
+      },
+      {
+          "name": 7,
+          "entityType": "ordinal",
+          "mention": "seventh",
+          "span": [12, 19]
+      }
+  ],
+  "answerEntity":
+  [
+      {
+          "name": "Q1153188",
+          "label": "Mount Lucania",
+      }
+  ],
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+`id`: a unique ID for the given sample.
+
+`lang`: the language of the question. 
+
+`question`: the original question elicited in the corresponding language.
+
+`answerText`: the original answer text elicited in English.
+
+`category`: the category of the question. Options are: geography, movies, history, books, politics, music, videogames, or sports
+
+`complexityType`: the complexity type of the question. Options are: ordinal, intersection, count, superlative, yesno comparative, multihop, difference, or generic
+
+`questionEntity`:  a list of annotated question entities identified by crowd workers.
+```
+{
+     "name": The Wikidata Q-code or numerical value of the entity
+     "entityType": The type of the entity. Options are:
+             entity, cardinal, ordinal, date, time, percent, quantity, or money
+     "label": The label of the Wikidata Q-code
+     "mention": The entity as it appears in the English question text. Will be empty for non-English samples.
+     "span": The start and end characters of the mention in the English question text. Will be empty for non-English samples.
+}
+```
+`answerEntity`:  a list of annotated answer entities identified by crowd workers.
+```
+{
+     "name": The Wikidata Q-code or numerical value of the entity
+     "label": The label of the Wikidata Q-code
+}
+```
+
+### Data Splits
+
+For each language, we split into train (14,000 samples), dev (2,000 samples), and test (4,000 samples) sets.
+
+### Personal and Sensitive Information
+
+The corpora is free of personal or sensitive information.
+
+## Considerations for Using the Data
+### Social Impact of Dataset
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Discussion of Biases
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Other Known Limitations
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+Amazon Alexa AI.
+
+### Licensing Information
+
+This project is licensed under the CC-BY-4.0 License.
+
+### Citation Information
+
+Please cite the following papers when using this dataset.
+
+```latex
+@inproceedings{sen-etal-2022-mintaka,
+    title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
+    author = "Sen, Priyanka  and
+      Aji, Alham Fikri  and
+      Saffari, Amir",
+    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "International Committee on Computational Linguistics",
+    url = "https://aclanthology.org/2022.coling-1.138",
+    pages = "1604--1619"
+}
+```
+
+### Contributions
+
+Thanks to [@afaji](https://github.com/afaji) for adding this dataset.

mintaka.py

@@ -19,9 +19,7 @@ _DESCRIPTION = """\
 _CITATION = """\
         @inproceedings{sen-etal-2022-mintaka,
             title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
-            author = "Sen, Priyanka  and
-              Aji, Alham Fikri  and
-              Saffari, Amir",
+            author = "Sen, Priyanka and Aji, Alham Fikri and Saffari, Amir",
             booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
             month = oct,
             year = "2022",
@@ -131,15 +129,16 @@ class Mintaka(datasets.GeneratorBasedBuilder):
 
         with open(file, encoding='utf-8') as json_file:
             data = json.load(json_file)
-            for sample in data:
-                for lang in langs:
+            for lang in langs:
+                for sample in data:
                     questionEntity = [
                         {
                             "name": str(qe["name"]),
                             "entityType": qe["entityType"],
                             "label": qe["label"] if "label" in qe else "",
-                            "mention": qe["mention"],
-                            "span": qe["span"],
+                            # span only applies for English question
+                            "mention": qe["mention"] if lang == "en" else None,
+                            "span": qe["span"] if lang == "en" else [],
                         } for qe in sample["questionEntity"]
                     ]
 
@@ -149,12 +148,13 @@ class Mintaka(datasets.GeneratorBasedBuilder):
                     elif sample['answer']["answerType"] == "numerical" and "supportingEnt" in sample["answer"]:
                         answers = sample['answer']['supportingEnt']
 
+                    # helper to get language for the corresponding language
                     def get_label(labels, lang):
                         if lang in labels:
                             return labels[lang]
                         if 'en' in labels:
                             return labels['en']
-                        return ""
+                        return None
 
                     answerEntity = [
                         {
@@ -172,7 +172,6 @@ class Mintaka(datasets.GeneratorBasedBuilder):
                         "complexityType": sample["complexityType"],
                         "questionEntity": questionEntity,
                         "answerEntity": answerEntity,
-
                     }
 
                     key_ += 1