Create README.md

README.md

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+</h1>
+
+<div class="section">
+<h2>1. Dataset Summary</h2>
+
+<p>
+This dataset provides parallel corpora for pivot-based neural machine translation research.
+It contains Polish–Czech, Czech–German, and Polish–German parallel sentence pairs,
+derived from the JRC-Acquis corpus and preprocessed for reproducible experiments.
+</p>
+
+<p>
+The dataset was created to support research on pivot translation,
+particularly investigating Czech as an intermediate language for Polish–German translation.
+</p>
+</div>
+
+<div class="section">
+<h2>2. Languages</h2>
+
+<ul>
+<li>Polish (pl)</li>
+<li>Czech (cs)</li>
+<li>German (de)</li>
+</ul>
+
+<p><strong>Language Pairs:</strong></p>
+<ul>
+<li>pl–cs</li>
+<li>cs–de</li>
+<li>pl–de</li>
+</ul>
+</div>
+
+<div class="section">
+<h2>3. Dataset Structure</h2>
+
+<p>
+Each language pair is split into training, validation, and test sets
+with the following ratio:
+</p>
+
+<pre>9 : 0.5 : 0.5</pre>
+
+<p>
+Directory structure:
+</p>
+
+<pre>
+./
+  ├ JRC-Acquis.cs-de.*
+  ├ JRC-Acquis.cs-pl.*
+  └ JRC-Acquis.de-pl.*
+</pre>
+
+<p>
+Each file contains aligned parallel sentence pairs.
+</p>
+</div>
+
+<div class="section">
+<h2>4. Data Source</h2>
+
+<p>
+The dataset is derived from the <strong>JRC-Acquis Parallel Corpus</strong>,
+which consists of European Union legislative texts.
+</p>
+
+<p>
+The original corpus is widely used in multilingual NLP research.
+</p>
+</div>
+
+<div class="section">
+<h2>5. Preprocessing</h2>
+
+<p>The following preprocessing steps were applied:</p>
+
+<ul>
+<li>Sentence alignment verification</li>
+<li>Basic normalization</li>
+<li>Train/validation/test splitting (9:0.5:0.5)</li>
+</ul>
+
+<p>
+No subword segmentation (e.g., BPE or SentencePiece) is applied in this dataset version.
+</p>
+</div>
+
+<div class="section">
+<h2>6. Intended Use</h2>
+
+<p>This dataset is intended for:</p>
+
+<ul>
+<li>Pivot-based neural machine translation research</li>
+<li>Low-resource translation experiments</li>
+<li>Comparative evaluation of pivot language selection</li>
+<li>Evaluation using BLEU, GLEU, and CHRF metrics</li>
+</ul>
+
+<p>
+It is not specifically designed for commercial deployment.
+</p>
+</div>
+
+<div class="section">
+<h2>7. Reproducibility</h2>
+
+<p>
+The dataset includes pre-split files identical to those used in the original experiments.
+</p>
+
+<p>
+In the original study:
+</p>
+
+<ul>
+<li>60,000 sentence pairs were used for training per translation model</li>
+<li>10,000 sentences were used for testing</li>
+<li>Transformer-based models were applied</li>
+</ul>
+</div>
+
+<div class="section">
+<h2>8. Evaluation Metrics</h2>
+
+<ul>
+<li>BLEU</li>
+<li>GLEU</li>
+<li>CHRF</li>
+</ul>
+</div>
+
+<div class="section">
+<h2>9. Ethical Considerations</h2>
+
+<p>
+The dataset is based on publicly available legislative texts from the European Union.
+No personally identifiable information (PII) is intentionally included.
+</p>
+
+<p>
+Users should verify compliance with the original JRC-Acquis license before use.
+</p>
+</div>
+
+<div class="section">
+<h2>10. Citation</h2>
+
+<p>If you use this dataset, please cite:</p>
+
+<pre>
+Tokunaga, N., Ninomiya, T., & Tamura, A. (2020).
+Neural Machine Translation for Polish–German via Czech as a Pivot Language.
+</pre>
+
+<p>Dataset citation:</p>
+
+<pre>
+Tokunaga, N., Ninomiya, T., & Tamura, A. (2020).
+TransformerPivotTranslation Dataset.
+Hugging Face.
+https://huggingface.co/datasets/SHSK0118/TransformerPivotTranslation
+</pre>
+
+</div>
+
+<div class="section">
+<h2>11. License</h2>
+
+<p>
+This dataset inherits the licensing conditions of the original JRC-Acquis corpus.
+Users must ensure compliance with the original terms of use.
+</p>
+</div>
+
+<div class="section">
+<h2>12. Contact</h2>
+
+<p>
+For academic inquiries, please contact the dataset authors.
+</p>
+</div>