Tasks alignment with models

I updated our `tasks.json` file with the new task taxonomy that is aligned with models.

The rule that defines a task is the following:

**Two tasks are different if and only if the steps of their pipelines** are different, i.e. if they can’t reasonably be implemented using the same coherent code (level of granularity/complexity of the code to be defined - ideally I’d like to say “HF user’s level”) - this is the same definition in `transformers` 

I will update the tags of all the datasets in this repository [in another PR](https://github.com/huggingface/datasets/pull/4067) for readability.

Main changes:
- conditional-text-generation is split between summarization, translation, text-generation and text2text-generation
- speech-processing is split into automatic-speech-recognition, audio-classification, etc.
- structure-prediction is renamed token-classification
- abstractive-qa now belongs to text2text-generation

Here is just a simplified YAML dump of `tasks.json`:

```yaml
audio-classification:
- keyword-spotting
- speaker-identification
- speaker-intent-classification
- emotion-recognition
- speaker-language-identification
audio-to-audio: []
automatic-speech-recognition: []
conversational:
- dialogue-generation
feature-extraction: []
fill-mask:
- slot-filling
- masked-language-modeling
image-classification:
- multi-label-image-classification
- multi-class-image-classification
image-segmentation:
- instance-segmentation
- semantic-segmentation
- panoptic-segmentation
image-to-text:
- image-captioning
multiple-choice:
- multiple-choice-qa
- multiple-choice-coreference-resolution
object-detection:
- face-detection
- vehicle-detection
question-answering:
- extractive-qa
- open-domain-qa
- closed-domain-qa
sentence-similarity: []
tabular-classification: []
tabular-to-text:
- rdf-to-text
summarization:
- news-articles-summarization
- news-articles-headline-generation
table-to-text: []
table-question-answering: []
text-classification:
- acceptability-classification
- entity-linking-classification
- fact-checking
- intent-classification
- multi-class-classification
- multi-label-classification
- natural-language-inference
- semantic-similarity-classification
- sentiment-classification
- topic-classification
- semantic-similarity-scoring
- sentiment-scoring
- sentiment-analysis
- hate-speech-detection
- text-scoring
text-generation:
- dialogue-modeling
- language-modeling
text-retrieval:
- document-retrieval
- utterance-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
text-to-image: []
text-to-tabular:
- relation-extraction
- semantic-role-labeling
text-to-speech: []
text2text-generation:
- text-simplification
- explanation-generation
- abstractive-qa
- open-domain-abstractive-qa
- closed-domain-qa
- open-book-qa
- closed-book-qa
time-series-forecasting:
- univariate-time-series-forecasting
- multivariate-time-series-forecasting
token-classification:
- named-entity-recognition
- part-of-speech-tagging
- parsing
- lemmatization
- word-sense-disambiguation
- coreference-resolution
translation: []
visual-question-answering: []
voice-activity-detection: []
zero-shot-classification: []
zero-shot-image-classification: []
reinforcement-learning: []
other: []
```

Feel free to comment and give suggestions, especially if you think we can also align this list with other projects

cc @julien-c @osanseviero @severo @lewtun @yjernite @albertvillanova @mariosasko @polinaeterna