{ "id": 671781, "title": "Strategy for Sentence Alignment and Normalization", "url": "/competitions/deep-past-initiative-machine-translation/discussion/671781", "author_name": "hikaggler", "post_date": "2026-02-04T02:00:54.356000", "votes": 2, "comment_count": 0, "views": 0, "content": "

Hi everyone! I’m really enjoying my first NLP competition. Tackling 4,000-year-old Akkadian business records is quite a unique challenge!

\n

I wanted to share my current strategy for handling the dataset inconsistencies and ask for your insights on a few points.

\n

1. Hybrid Sentence-level Alignment

\n

The mismatch between the document-level train.csv and the sentence-level test.csv is one of the biggest hurdles. I’ve developed a hybrid approach using the metadata provided by the host.

\n

Specifically, I’m using the first_word_number from Sentences_Oare_FirstWord_LinNum.csv to physically \"slice\" the transliteration text from published_texts.csv (or train.csv). By using word indices, I can align the Oare sentences with the raw transliterations more accurately than using simple regex-based splitters.

\n

Here is the core logical snippet of my alignment:

\n
def extract_transliteration_slice(full_trans, first_word_num, next_word_num):\n    \"\"\"\n    Slices the transliteration based on physical word indices.\n    \"\"\"\n    words = full_trans.split()\n    start_idx = max(0, first_word_num - 1)  # 1-indexed to 0-indexed\n\n    # Slice until the next sentence starts, or until the end of text\n    end_idx = min(len(words), next_word_num - 1) if next_word_num > 0 else len(words)\n\n    if start_idx >= len(words):\n        return \"\"\n\n    return ' '.join(words[start_idx:end_idx])\n
\n

Question: Has anyone found a more robust way to handle sentences where the translation might be split across lines or contains multiple Akkadian sentences?

\n

2. Normalization Pipeline

\n

To align the training data with the test set's transliteration style, I’m using the following normalization script:

\n
def normalize_transliteration(text):\n    text = text.replace('ḫ', 'h').replace('Ḫ', 'H')  # Align with test set style\n    text = text.replace('!', '').replace('?', '')    # Remove uncertainty markers\n    text = re.sub(r'\\[([^\\]]*)\\]', r'\\1', text)      # Simplify brackets\n    text = text.replace('˹', '').replace('˺', '')    # Remove damage markers\n\n    # Gap standardization\n    text = re.sub(r'x{2,}', '<big_gap>', text)\n    text = re.sub(r'\\bx\\b', '<gap>', text)\n    text = re.sub(r'\\.{3,}', '<big_gap>', text)\n    return re.sub(r'\\s+', ' ', text).strip()\n
\n

3. Current Stumbling Blocks

\n\n

I’m curious to learn about your strategies! What CSV files are you prioritizing in your training pipeline?

\n

Happy kaggling!

", "messages": [ { "id": 3401555, "postDate": "2026-02-04T02:00:54.357Z", "content": "

Hi everyone! I’m really enjoying my first NLP competition. Tackling 4,000-year-old Akkadian business records is quite a unique challenge!

\n

I wanted to share my current strategy for handling the dataset inconsistencies and ask for your insights on a few points.

\n

1. Hybrid Sentence-level Alignment

\n

The mismatch between the document-level train.csv and the sentence-level test.csv is one of the biggest hurdles. I’ve developed a hybrid approach using the metadata provided by the host.

\n

Specifically, I’m using the first_word_number from Sentences_Oare_FirstWord_LinNum.csv to physically \"slice\" the transliteration text from published_texts.csv (or train.csv). By using word indices, I can align the Oare sentences with the raw transliterations more accurately than using simple regex-based splitters.

\n

Here is the core logical snippet of my alignment:

\n
def extract_transliteration_slice(full_trans, first_word_num, next_word_num):\n    \"\"\"\n    Slices the transliteration based on physical word indices.\n    \"\"\"\n    words = full_trans.split()\n    start_idx = max(0, first_word_num - 1)  # 1-indexed to 0-indexed\n\n    # Slice until the next sentence starts, or until the end of text\n    end_idx = min(len(words), next_word_num - 1) if next_word_num > 0 else len(words)\n\n    if start_idx >= len(words):\n        return \"\"\n\n    return ' '.join(words[start_idx:end_idx])\n
\n

Question: Has anyone found a more robust way to handle sentences where the translation might be split across lines or contains multiple Akkadian sentences?

\n

2. Normalization Pipeline

\n

To align the training data with the test set's transliteration style, I’m using the following normalization script:

\n
def normalize_transliteration(text):\n    text = text.replace('ḫ', 'h').replace('Ḫ', 'H')  # Align with test set style\n    text = text.replace('!', '').replace('?', '')    # Remove uncertainty markers\n    text = re.sub(r'\\[([^\\]]*)\\]', r'\\1', text)      # Simplify brackets\n    text = text.replace('˹', '').replace('˺', '')    # Remove damage markers\n\n    # Gap standardization\n    text = re.sub(r'x{2,}', '<big_gap>', text)\n    text = re.sub(r'\\bx\\b', '<gap>', text)\n    text = re.sub(r'\\.{3,}', '<big_gap>', text)\n    return re.sub(r'\\s+', ' ', text).strip()\n
\n

3. Current Stumbling Blocks

\n\n

I’m curious to learn about your strategies! What CSV files are you prioritizing in your training pipeline?

\n

Happy kaggling!

", "rawMarkdown": "Hi everyone! I’m really enjoying my first NLP competition. Tackling 4,000-year-old Akkadian business records is quite a unique challenge!\n\nI wanted to share my current strategy for handling the dataset inconsistencies and ask for your insights on a few points.\n\n### 1. Hybrid Sentence-level Alignment\nThe mismatch between the document-level `train.csv` and the sentence-level `test.csv` is one of the biggest hurdles. I’ve developed a hybrid approach using the metadata provided by the host.\n\nSpecifically, I’m using the `first_word_number` from `Sentences_Oare_FirstWord_LinNum.csv` to physically \"slice\" the transliteration text from `published_texts.csv` (or `train.csv`). By using word indices, I can align the Oare sentences with the raw transliterations more accurately than using simple regex-based splitters.\n\nHere is the core logical snippet of my alignment:\n\n```python\ndef extract_transliteration_slice(full_trans, first_word_num, next_word_num):\n \"\"\"\n Slices the transliteration based on physical word indices.\n \"\"\"\n words = full_trans.split()\n start_idx = max(0, first_word_num - 1) # 1-indexed to 0-indexed\n \n # Slice until the next sentence starts, or until the end of text\n end_idx = min(len(words), next_word_num - 1) if next_word_num > 0 else len(words)\n \n if start_idx >= len(words):\n return \"\"\n \n return ' '.join(words[start_idx:end_idx])\n```\n\n**Question:** Has anyone found a more robust way to handle sentences where the translation might be split across lines or contains multiple Akkadian sentences?\n\n### 2. Normalization Pipeline\nTo align the training data with the test set's transliteration style, I’m using the following normalization script:\n\n```python\ndef normalize_transliteration(text):\n text = text.replace('ḫ', 'h').replace('Ḫ', 'H') # Align with test set style\n text = text.replace('!', '').replace('?', '') # Remove uncertainty markers\n text = re.sub(r'\\[([^\\]]*)\\]', r'\\1', text) # Simplify brackets\n text = text.replace('˹', '').replace('˺', '') # Remove damage markers\n \n # Gap standardization\n text = re.sub(r'x{2,}', '', text)\n text = re.sub(r'\\bx\\b', '', text)\n text = re.sub(r'\\.{3,}', '', text)\n return re.sub(r'\\s+', ' ', text).strip()\n```\n\n### 3. Current Stumbling Blocks\n* **Source Quality:** I'm seeing a significant gap in translation quality between `train.csv` (gold standard) and entries sourced solely from `Sentences_Oare`.\n* **Vocab:** Has anyone tried using `OA_Lexicon_eBL.csv` to \"normalize\" the vocabulary (e.g., mapping variant spellings to a canonical form) before training?\n\nI’m curious to learn about your strategies! What CSV files are you prioritizing in your training pipeline?\n\nHappy kaggling!\n", "votes": 2 } ], "comments": [], "raw_markdown_by_id": { "3401555": "Hi everyone! I’m really enjoying my first NLP competition. Tackling 4,000-year-old Akkadian business records is quite a unique challenge!\n\nI wanted to share my current strategy for handling the dataset inconsistencies and ask for your insights on a few points.\n\n### 1. Hybrid Sentence-level Alignment\nThe mismatch between the document-level `train.csv` and the sentence-level `test.csv` is one of the biggest hurdles. I’ve developed a hybrid approach using the metadata provided by the host.\n\nSpecifically, I’m using the `first_word_number` from `Sentences_Oare_FirstWord_LinNum.csv` to physically \"slice\" the transliteration text from `published_texts.csv` (or `train.csv`). By using word indices, I can align the Oare sentences with the raw transliterations more accurately than using simple regex-based splitters.\n\nHere is the core logical snippet of my alignment:\n\n```python\ndef extract_transliteration_slice(full_trans, first_word_num, next_word_num):\n \"\"\"\n Slices the transliteration based on physical word indices.\n \"\"\"\n words = full_trans.split()\n start_idx = max(0, first_word_num - 1) # 1-indexed to 0-indexed\n \n # Slice until the next sentence starts, or until the end of text\n end_idx = min(len(words), next_word_num - 1) if next_word_num > 0 else len(words)\n \n if start_idx >= len(words):\n return \"\"\n \n return ' '.join(words[start_idx:end_idx])\n```\n\n**Question:** Has anyone found a more robust way to handle sentences where the translation might be split across lines or contains multiple Akkadian sentences?\n\n### 2. Normalization Pipeline\nTo align the training data with the test set's transliteration style, I’m using the following normalization script:\n\n```python\ndef normalize_transliteration(text):\n text = text.replace('ḫ', 'h').replace('Ḫ', 'H') # Align with test set style\n text = text.replace('!', '').replace('?', '') # Remove uncertainty markers\n text = re.sub(r'\\[([^\\]]*)\\]', r'\\1', text) # Simplify brackets\n text = text.replace('˹', '').replace('˺', '') # Remove damage markers\n \n # Gap standardization\n text = re.sub(r'x{2,}', '', text)\n text = re.sub(r'\\bx\\b', '', text)\n text = re.sub(r'\\.{3,}', '', text)\n return re.sub(r'\\s+', ' ', text).strip()\n```\n\n### 3. Current Stumbling Blocks\n* **Source Quality:** I'm seeing a significant gap in translation quality between `train.csv` (gold standard) and entries sourced solely from `Sentences_Oare`.\n* **Vocab:** Has anyone tried using `OA_Lexicon_eBL.csv` to \"normalize\" the vocabulary (e.g., mapping variant spellings to a canonical form) before training?\n\nI’m curious to learn about your strategies! What CSV files are you prioritizing in your training pipeline?\n\nHappy kaggling!\n" } }