QuestionAnsweringPipeline returns full context in Japanese
### System Info

```shell
- `transformers` version: 4.19.4
- Platform: Linux-5.10.0-13-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- Huggingface_hub version: 0.1.0
- PyTorch version (GPU?): 1.11.0+cu102 (False)
```


### Who can help?

@Narsil @sgugger

### Information

- [X] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

QuestionAnsweringPipeline (almost always) returns full `context` in Japanese, for example:

```py
from transformers import AutoTokenizer, AutoModelForQuestionAnswering, QuestionAnsweringPipeline
tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-aozora-ud-head")
model = AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-base-japanese-aozora-ud-head")
qap = QuestionAnsweringPipeline(tokenizer=tokenizer, model=model)
print(qap(question="国語", context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
```

returns `{'score': 0.9999955892562866, 'start': 0, 'end': 30, 'answer': '全学年にわたって小学校の国語の教科書に挿し絵が用いられている'}`. On the other hand, directly with `torch.argmax`

```py
import torch
from transformers import AutoTokenizer,AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-aozora-ud-head")
model = AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-base-japanese-aozora-ud-head")
question = "国語"
context = "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
inputs = tokenizer(question,context, return_tensors="pt", return_offsets_mapping=True)
offsets = inputs.pop("offset_mapping").tolist()[0]
outputs = model(**inputs)
start, end = torch.argmax(outputs.start_logits), torch.argmax(outputs.end_logits)
print(context[offsets[start][0]:offsets[end][-1]])
```

the model returns the answer "教科書" correctly.

### Expected behavior

```shell
Return the right answer "教科書" instead of full context.
```

I suspect that "encoding" in Japanese models do not work at https://github.com/huggingface/transformers/blob/main/src/transformers/pipelines/question_answering.py#L452
but I'm vague how to fix.<|||||>Hi @KoichiYasuoka  👋 As per our [issues guidelines](https://github.com/huggingface/transformers/blob/main/ISSUES.md), we reserve GitHub issues for bugs in the repository and/or feature requests. For any other requests, we'd like to invite you to use our [forum](https://discuss.huggingface.co/) 🤗  

(Since the issue is about the quality of the output, it's probably model-related, and not a bug per se. In any case, if you suspect it is due to a bug in `transformers`, please add more information here)<|||||>Hi @KoichiYasuoka ,

This seems to be linked to the pipeline attempts to align on "words". The problem is that this japanese tokenizer does not ever cut on "words" so the whole context is a single word, so the realignment just forgets all about the actual answer, which is a bit sad.

I created a PR to include a new parameter to disable this so it can work on your use case (I personally think it should be the default but we cannot change this because of backward compatibility)

<|||||>Thank you @Narsil for creating new PR with `align_to_words=False` option. Well, can I use the option in the `widget` of [deberta-base-japanese-aozora-ud-head](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-aozora-ud-head) page?<|||||>Hi, the PR is not merged yet, and it will take a few days before it lands on the API (API doesn't run master).

Afterwards, while being undocumented and thus maybe deactivated at anytime (though we rarely do this), you could send `align_to_words: false` within the `parameters` part of your query to the API.

Unfortunately the widget itself will not use parameters.

Does that answer your question ?<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.