bilstm-armenian-participle-punct

BiLSTM — Armenian Participle-Clause Punctuation

A word-level bidirectional LSTM tagger (frozen Armenian GloVe embeddings, 300-dim; 1 layer; hidden size 128) for punctuation of Eastern Armenian participle clauses, as 4-class token labeling. From the CODASSCA 2026 paper Sequence Labeling for Low-Resource Syntax.

Labels

0 O · 1 COMMA_AFTER · 2 BUTH_AFTER · 3 REMOVE_COMMA

Results (macro-F1)

This is a custom PyTorch model (not a transformers architecture). Files: bilstm_best.pt (checkpoint dict), armenian_embeddings.pt (GloVe matrix), armenian_vocab.json (token to id), modeling_bilstm.py (the model class), bilstm_results.json (metrics). Tokenization is word-level using the provided vocab.

Usage

import json, torch
from huggingface_hub import hf_hub_download

repo = "AlbertHakobyan/bilstm-armenian-participle-punct"
model_py = hf_hub_download(repo, "modeling_bilstm.py")
ckpt_pt  = hf_hub_download(repo, "bilstm_best.pt")
vocab    = json.load(open(hf_hub_download(repo, "armenian_vocab.json"), encoding="utf-8"))

exec(open(model_py, encoding="utf-8").read())          # defines BiLSTMPunctuator
ck = torch.load(ckpt_pt, map_location="cpu", weights_only=False)
hp = ck["hyperparameters"]
model = BiLSTMPunctuator(ck["vocab_size"], ck["embedding_dim"], hp["hidden_size"],
                         hp["num_layers"], hp["dropout"], ck["num_classes"])
model.load_state_dict(ck["model_state_dict"]); model.eval()

def tag(words):
    ids = torch.tensor([[vocab.get(w, vocab.get("<UNK>", 1)) for w in words]])
    with torch.no_grad():
        pred = model(ids).argmax(-1)[0].tolist()
    id2label = {0:"O",1:"COMMA_AFTER",2:"BUTH_AFTER",3:"REMOVE_COMMA"}
    return list(zip(words, [id2label[p] for p in pred]))

Ensemble

Soft-vote with mBERT at alpha=0.45 gives the paper's best macro-F1 (0.6745, Shtemaran).

• Code: https://github.com/AlbertHakobyan070/Armenian-participle-phrase-punctuation