dgabri3le/english-ipa-dep-treebank

English IPA Dependency Treebank

A large-scale dataset of 10.4 million English sentences paired with IPA (International Phonetic Alphabet) transcriptions and Universal Dependencies syntactic annotations.

Each sentence includes its full dependency parse — head indices, relation labels, and a linearized tagged-IPA representation that interleaves syntactic roles with phonetic content.

Dataset Structure

Each sample contains:

Field	Type	Description
raw_english	string	Original English text before contraction expansion (e.g. "don't", "it's")
english	string	English sentence with contractions expanded (e.g. "do not", "it is"). Lowercased, filtered. For sentences without contractions, identical to raw_english.
ipa	string	IPA transcription (word-aligned with english)
dep_labels	list[string]	Universal Dependencies relation label per word
dep_heads	list[int]	Head word index per word (1-indexed, 0 = ROOT)
tagged_ipa	string	Linearized format: [role] ipa_word [role] ipa_word ...

Examples

Example 1: Simple transitive sentence

English: the cat sat on the table

IPA: ðʌ kæt sæt ɑn ðʌ teɪbʌl

Dependency parse:

  idx  word     role      head  head_word
  ───  ───────  ────────  ────  ─────────
    1  the      det          2  cat
    2  cat      nsubj        3  sat
    3  sat      root         0  ROOT
    4  on       case         6  table
    5  the      det          6  table
    6  table    obl          3  sat

Tree diagram:

            sat (root)
           ╱         ╲
      cat (nsubj)   table (obl)
       │            ╱     ╲
    the (det)    on (case)  the (det)

Tagged IPA: [det] ðʌ [nsubj] kæt [root] sæt [case] ɑn [det] ðʌ [obl] teɪbʌl

Example 2: Complex sentence with subordinate clause

English: the researchers found that the compound reduces inflammation effectively

IPA: ðʌ ɹisɝtʃɝz faʊnd ðæt ðʌ kɑmpaʊnd ɹɪdusɪz ɪnflʌmeɪʃʌn ɪfɛktɪvli

Dependency parse:

  idx  word            role      head  head_word
  ───  ──────────────  ────────  ────  ──────────────
    1  the             det          2  researchers
    2  researchers     nsubj        3  found
    3  found           root         0  ROOT
    4  that            mark         7  reduces
    5  the             det          6  compound
    6  compound        nsubj        7  reduces
    7  reduces         ccomp        3  found
    8  inflammation    obj          7  reduces
    9  effectively     advmod       7  reduces

Tree diagram:

                found (root)
               ╱             ╲
  researchers (nsubj)     reduces (ccomp)
       │                 ╱    │       ╲
    the (det)    compound  inflam-  effectively
                 (nsubj)   mation    (advmod)
                ╱    ╲     (obj)
          the (det)  that (mark)

Tagged IPA: [det] ðʌ [nsubj] ɹisɝtʃɝz [root] faʊnd [mark] ðæt [det] ðʌ [nsubj] kɑmpaʊnd [ccomp] ɹɪdusɪz [obj] ɪnflʌmeɪʃʌn [advmod] ɪfɛktɪvli

How Dependency Trees Work

A dependency tree represents the syntactic structure of a sentence as directed links between words. Every word points to its head (the word it depends on) via a labeled relation.

Key concepts

• Root: The main predicate of the sentence (usually the main verb). Its head index is 0 (no parent).
• Head: Each non-root word has exactly one head — the word that governs it syntactically.
• Relation: The label on the link describes the grammatical function: nsubj (subject), obj (object), det (determiner), amod (adjective modifier), etc.

Reading the dep_heads array

dep_heads is a list of integers, one per word, using 1-based indexing:

• dep_heads[i] = j means word i depends on word j
• dep_heads[i] = 0 means word i is the root

For example, in [2, 3, 0, 6, 6, 3]:

• Word 1 → depends on word 2
• Word 2 → depends on word 3
• Word 3 → ROOT (head = 0)
• Word 4 → depends on word 6
• Word 5 → depends on word 6
• Word 6 → depends on word 3

The tagged_ipa format

The tagged_ipa field provides a linearized representation that interleaves each word's dependency role with its IPA transcription:

[det] ðʌ [nsubj] kæt [root] sæt [case] ɑn [det] ðʌ [obl] teɪbʌl

This format preserves the original word order while annotating each word's syntactic function. It can be used directly as input to sequence models that need both phonetic and syntactic information.

Common Universal Dependencies relations

Relation	Meaning	Example
root	Main predicate	sat in "the cat sat"
nsubj	Nominal subject	cat in "the cat sat"
obj	Direct object	fish in "the cat ate fish"
det	Determiner	the in "the cat"
amod	Adjective modifier	big in "the big cat"
advmod	Adverb modifier	quickly in "ran quickly"
case	Case marker / preposition	on in "sat on the table"
obl	Oblique nominal	table in "sat on the table"
nmod	Nominal modifier	wood in "table of wood"
conj	Conjunct	dogs in "cats and dogs"
cc	Coordinating conjunction	and in "cats and dogs
mark	Subordinating marker	that in "said that he left"
ccomp	Clausal complement	left in "said that he left"
xcomp	Open clausal complement	run in "wants to run"
aux	Auxiliary verb	has in "has eaten"

For the full set of relations, see the Universal Dependencies documentation.

Data Sources

Sentences were drawn from multiple registers for linguistic diversity:

• News articles (multiple years)
• Wikipedia (encyclopedic)
• Parliamentary proceedings (Europarl)

Filtering

Sentences were filtered to ensure quality:

• 5–30 words per sentence
• Must begin with a letter and end with sentence-ending punctuation
• No URLs, email addresses, or quoted text
• Limited digit content (≤2 number sequences, ≤15% digit characters)
• No bullet points, list markers, or section headers
• Deduplicated by exact English text match

Processing Pipeline

1. Sentence extraction from source corpora
2. Quality filtering (see above)
3. IPA transcription via epitran with CMU pronunciation dictionary fallback
4. Word alignment verification — English and IPA word counts must match exactly
5. Dependency parsing via Stanza with tokenize_pretokenized=True to preserve word-level alignment
6. Deduplication by English text

Usage

from datasets import load_dataset

ds = load_dataset("dgabri3le/english-ipa-dep-treebank", split="train", streaming=True)

for sample in ds:
    print(sample["english"])
    print(sample["tagged_ipa"])
    print(sample["dep_labels"])
    break

Citation

If you use this dataset, please cite:

@dataset{english_ipa_dep_treebank_2026,
  title={English IPA Dependency Treebank},
  author={Gabriele, Daniel},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/dgabri3le/english-ipa-dep-treebank}
}