OVModelForSeq2SeqLM with Helsinki-NLP/opus-mt-es-en has slow inference times when exported to OpenVino

I'm having trouble exporting the `Helsinki-NLP/opus-mt-es-en` model for language translation into the optimised OpenVino IR format. Reading through the other issues within this repository highlighted this issue https://github.com/huggingface/optimum-intel/issues/188, which seems to suffer from similar effects.

In that case, it seemed to be an issue with the BigBird architecture and its lack of support by HuggingFace Optimum. However, the `Helsinki-NLP/opus-mt-es-en` model is of the [`MarianMT` class](https://huggingface.co/Helsinki-NLP/opus-mt-es-en/blob/main/config.json#L8), which is [documented](https://huggingface.co/docs/optimum/bettertransformer/overview#supported-models) as being supported.

Am I missing something here fundamental? Is the conversion of the `MarianMT` model into OpenVino IR format currently unsupported by this library in a similar way to the BigBird models as in the above issue? Or are there aspects of the conversion that I am not specifying correctly such that the export is sub-optimal? It would seem that this should be possible given the [documentation](https://huggingface.co/docs/optimum/intel/inference#export-and-inference-of-sequencetosequence-models).

I see the following during the build logs if it helps at all: `Asked a sequence length of 16, but a sequence length of 1 will be used with use_past == True for 'decoder_input_ids'.`

An MRE looks like:
```python
import os
from optimum.intel.openvino import OVModelForSeq2SeqLM
from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-es-en")
ov_model = OVModelForSeq2SeqLM.from_pretrained(
"Helsinki-NLP/opus-mt-es-en",
export=True,
use_cache=True,
)

def run(text: str):
pipe = pipeline("translation_es_to_en", model=ov_model, tokenizer=tokenizer)
return pipe(text)

def export_to_ov(save_dir: str):
ov_model.save_pretrained()

if __name__ == "__main__":
export_to_ov("./")
```

Operating `run("Hola, como estas?")` yields an inference time of `0.6323761940002441s` while using the exported OVIR binaries in an OVMS model pipeline yields an inference time of `45s`.

Any help on this one would be greatly appreciated, cheers!

P.S. I can post the `config.json` file being passed to the OVMS instance, but it's very long so I'll leave it until it's required!