update README and processing script

README.md

@@ -122,8 +122,7 @@ Original dataset:
     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.",
-}
+    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:
@@ -139,8 +138,7 @@ Shared task where version 2.0 was published:
    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 }
-
+   month=dec}
 ```
 
 ### Acknowledgements

process_viquad.py

@@ -46,8 +46,8 @@ for split in ["train", "dev", "test"]:
             question_set = set()
             for q in qas:
                 question = q["question"]
-                answers = q.get("answers", [])
-                plausible_answers = q.get("plausible_answers", [])
+                answers = q.get("answers", None)
+                plausible_answers = q.get("plausible_answers", None)
                 # Dedup answers
                 if answers:
                     answers = deduplicate_answers(answers)