[TypeError: Couldn't cast array of type] Cannot process dataset in v2.2.2

## Describe the bug
I am trying to process the [conll2012_ontonotesv5](https://huggingface.co/datasets/conll2012_ontonotesv5) dataset in `datasets` v2.2.2 and am running into a type error when casting the features.

## Steps to reproduce the bug
```python
import os
from typing import (
    List,
    Dict,
)
from collections import (
    defaultdict,
)
from dataclasses import (
    dataclass,
)
from datasets import (
    load_dataset,
)


@dataclass
class ConllConverter:

    path: str
    name: str
    cache_dir: str

    def __post_init__(
        self,
    ):
        self.dataset = load_dataset(
            path=self.path,
            name=self.name,
            cache_dir=self.cache_dir,
        )

    def convert(
        self,
    ):

        class_label = self.dataset["train"].features["sentences"][0]["named_entities"].feature
        # label_set = list(set([
        #     label.split("-")[1] if label != "O" else label for label in class_label.names
        # ]))

        def prepare_chunk(token, entity):
            assert len(token) == len(entity)
            # Sequence length
            length = len(token)
            # Variable used
            entity_chunk = defaultdict(list)
            idx = flag = 0
            # While loop
            while idx < length:
                if entity[idx] == "O":
                    flag += 1
                    idx += 1
                else:
                    iob_tp, lab_tp = entity[idx].split("-")
                    assert iob_tp == "B"
                    idx += 1
                    while idx < length and entity[idx].startswith("I-"):
                        idx += 1
                    entity_chunk[lab_tp].append(token[flag: idx])
                    flag = idx
            entity_chunk = dict(entity_chunk)
            # for label in label_set:
            #     if label != "O" and label not in entity_chunk.keys():
            #         entity_chunk[label] = None
            return entity_chunk

        def prepare_features(
            batch: Dict[str, List],
        ) -> Dict[str, List]:
            sentence = [
                sent for doc_sent in batch["sentences"] for sent in doc_sent
            ]
            feature = {
                "sentence": list(),
            }
            for sent in sentence:
                token = sent["words"]
                entity = class_label.int2str(sent["named_entities"])
                entity_chunk = prepare_chunk(token, entity)
                sent_feat = {
                    "token": token,
                    "entity": entity,
                    "entity_chunk": entity_chunk,
                }
                feature["sentence"].append(sent_feat)

            return feature

        column_names = self.dataset.column_names["train"]
        dataset = self.dataset.map(
            function=prepare_features,
            with_indices=False,
            batched=True,
            batch_size=3,
            remove_columns=column_names,
            num_proc=1,
        )
        dataset.save_to_disk(
            dataset_dict_path=os.path.join("data", self.path, self.name)
        )


if __name__ == "__main__":
    converter = ConllConverter(
        path="conll2012_ontonotesv5",
        name="english_v4",
        cache_dir="cache",
    )
    converter.convert()

```

## Expected results
I want to use the dataset to perform NER task and to change the label list into a {Entity Type: list of spans} format.

## Actual results
<details>
<summary>Traceback</summary>

```python
Traceback (most recent call last):                                                                                                                                                                            | 0/81 [00:00<?, ?ba/s]
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/multiprocess/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 532, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 499, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper
    out = func(self, *args, **kwargs)
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2751, in _map_single
    writer.write_batch(batch)
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 503, in write_batch
    arrays.append(pa.array(typed_sequence))
  File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
  File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
    out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1675, in wrapper
    return func(array, *args, **kwargs)
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1793, in cast_array_to_feature
    arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1793, in <listcomp>
    arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1675, in wrapper
    return func(array, *args, **kwargs)
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1844, in cast_array_to_feature
    raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
struct<CARDINAL: list<item: list<item: string>>, DATE: list<item: list<item: string>>, EVENT: list<item: list<item: string>>, FAC: list<item: list<item: string>>, GPE: list<item: list<item: string>>, LANGUAGE: list<item: list<item: string>>, LAW: list<item: list<item: string>>, LOC: list<item: list<item: string>>, MONEY: list<item: list<item: string>>, NORP: list<item: list<item: string>>, ORDINAL: list<item: list<item: string>>, ORG: list<item: list<item: string>>, PERCENT: list<item: list<item: string>>, PERSON: list<item: list<item: string>>, QUANTITY: list<item: list<item: string>>, TIME: list<item: list<item: string>>, WORK_OF_ART: list<item: list<item: string>>>
to
{'CARDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'DATE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'EVENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'FAC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'GPE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LAW': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LOC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'MONEY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'NORP': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORG': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERCENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERSON': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PRODUCT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'QUANTITY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'TIME': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'WORK_OF_ART': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None)}
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home2/jiangwangyi/workspace/work/Entity/dataconverter.py", line 110, in <module>
    converter.convert()
  File "/home2/jiangwangyi/workspace/work/Entity/dataconverter.py", line 91, in convert
    dataset = self.dataset.map(
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/dataset_dict.py", line 770, in map
    {
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/dataset_dict.py", line 771, in <dictcomp>
    k: dataset.map(
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2459, in map
    transformed_shards[index] = async_result.get()
  File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/multiprocess/pool.py", line 771, in get
    raise self._value
TypeError: Couldn't cast array of type
struct<CARDINAL: list<item: list<item: string>>, DATE: list<item: list<item: string>>, EVENT: list<item: list<item: string>>, FAC: list<item: list<item: string>>, GPE: list<item: list<item: string>>, LANGUAGE: list<item: list<item: string>>, LAW: list<item: list<item: string>>, LOC: list<item: list<item: string>>, MONEY: list<item: list<item: string>>, NORP: list<item: list<item: string>>, ORDINAL: list<item: list<item: string>>, ORG: list<item: list<item: string>>, PERCENT: list<item: list<item: string>>, PERSON: list<item: list<item: string>>, QUANTITY: list<item: list<item: string>>, TIME: list<item: list<item: string>>, WORK_OF_ART: list<item: list<item: string>>>
to
{'CARDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'DATE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'EVENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'FAC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'GPE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LAW': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LOC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'MONEY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'NORP': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORG': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERCENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERSON': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PRODUCT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'QUANTITY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'TIME': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'WORK_OF_ART': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None)}
```

</details>

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2
- Platform: Ubuntu 18.04
- Python version: 3.9.7
- PyArrow version: 7.0.0


And if the problem is that the way I am to construct the {Entity Type: list of spans} makes entity types without any spans hard to handle, is there a better way to meet the demand? Although I have verified that to make entity types without any spans to behave like `entity_chunk[label] = [[""]]` can perform normally, I still wonder if there is a more elegant way?