update readme and processing script

README.md

@@ -46,4 +46,92 @@ configs:
     path: data/validation-*
   - split: test
     path: data/test-*
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+- found
+language:
+- vi
+license:
+-
+multilinguality:
+- monolingual
+paperswithcode_id: null
+pretty_name: "JaQuAD: Japanese Question Answering Dataset"
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
 ---
+# Vietnamese Question Answering Dataset
+
+## Dataset Card for JaQuAD
+### Dataset Summary
+The HF version for Vietnamese QA dataset created by [Nguyen et al. (2020)](https://aclanthology.org/2020.coling-main.233/) and released in the [shared task](https://arxiv.org/abs/2203.11400).
+
+The original UIT-ViQuAD contains over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles. UIT-ViQuAD2.0 adds over 12K unanswerable questions for the same passage.
+
+Processed: The dataset has been processed to remove a few duplicated questions and answers.
+
+Questions about the private test set or the dataset should be directed to the authors.
+
+### Languages
+
+Vietnamese (`vi`)
+
+
+## Dataset Creation
+
+### Source Data
+
+Vietnamese Wikipedia
+
+### Annotations
+Human annotators
+
+### Citation Information
+Original dataset:
+
+```bibtex
+@inproceedings{nguyen-etal-2020-vietnamese,
+    title = "A {V}ietnamese Dataset for Evaluating Machine Reading Comprehension",
+    author = "Nguyen, Kiet  and
+      Nguyen, Vu  and
+      Nguyen, Anh  and
+      Nguyen, Ngan",
+    editor = "Scott, Donia  and
+      Bel, Nuria  and
+      Zong, Chengqing",
+    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
+    month = dec,
+    year = "2020",
+    address = "Barcelona, Spain (Online)",
+    publisher = "International Committee on Computational Linguistics",
+    url = "https://aclanthology.org/2020.coling-main.233",
+    doi = "10.18653/v1/2020.coling-main.233",
+    pages = "2595--2605",
+    abstract = "Over 97 million inhabitants speak Vietnamese as the native language in the world. However, there are few research studies on machine reading comprehension (MRC) in Vietnamese, the task of understanding a document or text, and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands complicate reasoning such as single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods in English and Chinese as the first experimental models on UIT-ViQuAD, which will be compared to further models. We also estimate human performances on the dataset and compare it to the experimental results of several powerful machine models. As a result, the substantial differences between humans and the best model performances on the dataset indicate that improvements can be explored on UIT-ViQuAD through future research. Our dataset is freely available to encourage the research community to overcome challenges in Vietnamese MRC.",
+}
+```
+
+Shared task where version 2.0 was published:
+```bibtex
+@article{Nguyen_2022,
+   title={VLSP 2021-ViMRC Challenge: Vietnamese Machine Reading Comprehension},
+   volume={38},
+   ISSN={2615-9260},
+   url={http://dx.doi.org/10.25073/2588-1086/vnucsce.340},
+   DOI={10.25073/2588-1086/vnucsce.340},
+   number={2},
+   journal={VNU Journal of Science: Computer Science and Communication Engineering},
+   publisher={Vietnam National University Journal of Science},
+   author={Nguyen, Kiet and Tran, Son Quoc and Nguyen, Luan Thanh and Huynh, Tin Van and Luu, Son Thanh and Nguyen, Ngan Luu-Thuy},
+   year={2022},
+   month=dec }
+
+```
+
+### Acknowledgements
+
+We thank the authors of ViQuAD for releasing this dataset to the community.

process_viquad.py

@@ -0,0 +1,77 @@
+"""
+Script used to process UIT-ViQuAD 2.0.
+Source: https://github.com/tuanbc88/ai_question_answering/tree/master/machine_reading_comprehension/02_datasets
+"""
+import os
+import json
+import pandas as pd
+from itertools import groupby
+from datasets import Dataset, DatasetDict
+
+def deduplicate_answers(answers):
+    answers_sorted = sorted(answers, key=lambda x: (x['text'], x['answer_start']))
+    unique_answers = [next(group) for _, group in groupby(answers_sorted, key=lambda x: (x['text'], x['answer_start']))]
+    return unique_answers
+
+data_dir = "UIT-ViQuAD 2.0"
+dataset_dict = {}
+
+for split in ["train", "dev", "test"]:
+    fname = os.path.join(data_dir, f"{split}.json")
+    data = json.load(open(fname))
+    rows = []
+    title_i = 0
+
+    for title_data in data["data"]:
+        title = title_data["title"]
+        ctx_i = 0
+        title_i += 1
+
+        for ctx_and_qs in title_data["paragraphs"]:
+            questions = ctx_and_qs["qas"]
+            context = ctx_and_qs["context"]
+            q_i = 0
+            ctx_i += 1
+            question_set = set()
+            # define default wherever answer is empty
+            answer_default: list = [{'answer_start': -1, 'text': ''}]
+            for q in questions:
+                question = q["question"]
+                answers = q["answers"] if "answers" in q else answer_default
+                plausible_answers = q["plausible_answers"] if "plausible_answers" in q else answer_default
+                # Dedup answers
+                answers = deduplicate_answers(answers)
+                plausible_answers = deduplicate_answers(plausible_answers)
+                uit_id = q["id"]
+                is_impossible = q["is_impossible"] if "is_impossible" in q else False
+
+                # Check duplicate questions
+                if question in question_set:
+                    print("---Found duplicate question: ", question, "---")
+                    print("Answer: ", answers)
+                    print("Answer plaus: ", plausible_answers)
+                    print("Impossible: ", is_impossible)
+                    continue
+
+                q_i += 1
+                overall_id = f"{title_i:04d}-{ctx_i:04d}-{q_i:04d}"
+                rows.append({
+                    "id": overall_id,
+                    "uit_id": uit_id,
+                    "title": title,
+                    "context": context,
+                    "question": question,
+                    "answers": answers,
+                    "is_impossible": is_impossible,
+                    "plausible_answers": plausible_answers
+                })
+                question_set.add(question)
+    # Convert to Dataset
+    df = pd.DataFrame(rows)
+    dataset_dict[split if split!="dev" else "validation"] = Dataset.from_pandas(df)
+
+print(dataset_dict)
+hf_dataset = DatasetDict(dataset_dict)
+hf_name = "UIT-ViQuAD2.0"
+hf_dataset.push_to_hub(f"taidng/{hf_name}")
+print("Dataset uploaded successfully!")