morpht5

This package contains the source code for the MorphT5 models used in the Low Resource Interlinear Translation paper.

Installation

pip install morpht5

Components

Models

The package includes three model variants, incorporating morphological features through dedicated embedding layers:

• MorphT5SumModel: Model using positional summation for combining text and morphological tag embeddings
• MorphT5AutoModel: Base model with autoencoder-style morphological tag embeddings
• MorphT5ConcatModel: Model using concatenation for combining text and morphological tag embeddings

Tokenizer

The package comes with a tokenizer that allows for encoding text and morphological tags into tokens:

>>> from morpht5 import MorphT5Tokenizer
>>> text = ["Λέγει", "αὐτῷ", "ὁ", "Ἰησοῦς", "Ἔγειρε", "ἆρον", "τὸν", "κράβαττόν", "σου", "καὶ", "περιπάτει"]
>>> tags = [
...     "V-PIA-3S",
...     "PPro-DM3S",
...     "Art-NMS",
...     "N-NMS",
...     "V-PMA-2S",
...     "V-AMA-2S",
...     "Art-AMS",
...     "N-AMS",
...     "PPro-G2S",
...     "Conj",
...     "V-PMA-2S",
... ]
>>> tokenizer = MorphT5Tokenizer.from_pretrained("mrapacz/interlinear-en-philta-emb-auto-diacritics-bh")
>>> inputs = tokenizer(text=text, morph_tags=tags, return_tensors="pt")
>>> inputs.keys()
dict_keys(['input_ids', 'attention_mask', 'input_morphs'])

Tagsets

The package comes with Enum classes for two morphological tagsets - one compiled from BibleHub and one compiled from Oblubienica:

from morpht5 import BibleHubTag, OblubienicaTag

Both tagsets cover comprehensive morphological features:

• Parts of Speech (Verb, Noun, Adjective, etc.)
• Person (1st, 2nd, 3rd)
• Tense (Present, Imperfect, Future, Aorist, Perfect, Pluperfect)
• Mood (Indicative, Imperative, Subjunctive, Optative, Infinitive, Participle)
• Voice (Active, Middle, Passive)
• Case (Nominative, Vocative, Accusative, Genitive, Dative)
• Number (Singular, Plural)
• Gender (Masculine, Feminine, Neuter)
• Degree (Positive, Comparative, Superlative)

The tagsets differ in their annotation style:

• BibleHub: Compact format (e.g., V-PIA-3S for "Verb - Present Indicative Active - 3rd Person Singular")
• Oblubienica: Verbose format (e.g., vi Pres Act 3 Sg for the same morphological information)

>>> from morpht5 import BibleHubTag, OblubienicaTag
>>> len(BibleHubTag)
684
>>> len(OblubienicaTag)
1070

Formatting

There's also a utility function for formatting interlinear translations:

>>> from morpht5.utils.formatting import format_interlinear
>>> text = ['Λέγει', 'αὐτῷ', 'ὁ', 'Ἰησοῦς', 'Ἔγειρε', 'ἆρον', 'τὸν', 'κράβαττόν', 'σου', 'καὶ', 'περιπάτει']
>>> tags = ['V-PIA-3S', 'PPro-DM3S', 'Art-NMS', 'N-NMS', 'V-PMA-2S', 'V-AMA-2S', 'Art-AMS', 'N-AMS', 'PPro-G2S', 'Conj', 'V-PMA-2S']
>>> trans_pl = ['Mówi', 'mu', '-', 'Jezus', 'wstawaj', 'weź', '-', 'matę', 'swoją', 'i', 'chodź']
>>> trans_en = ['says', 'to him', '-', 'jesus', 'arise', 'take up', '-', 'mat', 'of you', 'and', 'walk']
>>> print(format_interlinear(text, tags, trans_en, trans_pl))
 Λέγει   |    αὐτῷ   |    ὁ    | Ἰησοῦς |  Ἔγειρε  |   ἆρον   |   τὸν   | κράβαττόν |   σου    | καὶ  | περιπάτει
V-PIA-3S | PPro-DM3S | Art-NMS | N-NMS  | V-PMA-2S | V-AMA-2S | Art-AMS |   N-AMS   | PPro-G2S | Conj |  V-PMA-2S
  says   |   to him  |    -    | jesus  |  arise   | take up  |   the   |    mat    |  of you  | and  |    walk
  Mówi   |     mu    |    -    | Jezus  | wstawaj  |   weź    |    -    |    matę   |  swoją   |  i   |   chodź

License

This project is licensed under the MIT License. See the LICENSE file for details.