BART-base fine-tuned for English -> ASL gloss (ASLG-PC12)

facebook/bart-base fine-tuned on the training split of the ASLG-PC12 English-ASL gloss parallel corpus. Input is a lowercased English sentence; output is an upper-case ASL gloss sequence in the corpus's conventions (X- pronouns, DESC- modifiers).

This model is one of the systems in a small project comparing grammar-constrained decoding (xgrammar) with unconstrained decoding for gloss generation. Code, grammars and evaluation scripts: https://github.com/TheRealGioviok/asl-gloss-nlp

Training

Base model facebook/bart-base
Data training split of the Kaggle redistribution of ASLG-PC12 (87,710 pairs, no official split; deduplicated and re-split 80/10/10 with seed 42 in the project)
Epochs 3
Effective batch size 32
Learning rate 3e-5
Max length 128 tokens (source and target)
Steps 6,084

Weights are stored in fp16 (the evaluation in the project also ran in fp16).

Results (1,000-sentence test sample, greedy decoding)

Decoding BLEU chrF Exact match Sequence validity
Unconstrained 76.7 93.3 44.5% 48.6%
Constrained (grammar v3) 73.2 85.9 42.8% 100%
Constrained + copy rule (v3) 74.1 86.3 44.5% 100%

"Sequence validity" is the share of outputs whose every token is in the closed gloss vocabulary.

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

repo = "425GMM/bart-base-aslg-gloss"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSeq2SeqLM.from_pretrained(repo)

src = "we should behave outside european waters in just the same way as we do at home ."
ids = tok(src, return_tensors="pt")
out = model.generate(**ids, max_new_tokens=128)
print(tok.decode(out[0], skip_special_tokens=True))

Source sentences in ASLG-PC12 are lowercased with spaces before punctuation; feed the model text in the same form.

Limitations

ASLG-PC12 glosses are rule-generated from Europarl text, not produced by signers. The model learns those rules; it is not a general English-to-ASL translator and scores do not transfer to human-annotated gloss corpora.

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