This Datawhale NLP challenge focuses on enhancing machine translation through terminology dictionary integration. The goal is to improve translation accuracy for domain-specific termss by leveraging curated term mappings between source and target languages.

Key NLP Task Categories

• Sequence Labeling: Tasks like tokenization, POS tagging, and named entitty recognition
• Text Classification: Sentiment analysis, topic categorization, and intent detection
• Sentence Pair Analysis: Question answering, paraphrase detection, and logical reasoning
• Text Generation: Machine translation, summarization, and creative writing

Data Partitioning Strategy

Partition	Purpose	Usage
Training Set	Model parameter optimization	Learning input-output mapping patterns
Validation Set	Hyperparameter tuning	Preventing overfitting through early stopping
Test Set	Final performance evaluation	Assessing generalization capabilities

Evaluation Metrics

The competition uses BLEU-4 score calculated with sacrebleu library. This metric evaluates n-gram precision (n=4) between generated translations and reference texts. Key characteristics:

• Advantages: Fast computation, lenguage-agnostic, correlates well with human judgment
• Limitations: Ignores grammatical correctness, favors frequent words, doesn't account for synonyms

Implementation Framework

Modified Dataset Class

class TermAwareDataset(Dataset):
    def __init__(self, file_path, term_map):
        self.term_map = term_map
        # Load and process parallel corpus
        # Build vocabulary with term prioritization
        
    def __getitem__(self, idx):
        # Return tokenized source/target pairs with special tokens
        # Apply term substitution during tokenization

Neural Network Architecture

class SequenceEncoder(nn.Module):
    def __init__(self, input_size, embedding_dim, hidden_size):
        super().__init__()
        self.embedding = nn.Embedding(input_size, embedding_dim)
        self.rnn = nn.GRU(embedding_dim, hidden_size, bidirectional=True)
        
    def forward(self, input_seq):
        # Embedding + bidirectional GRU processing
        return encoded_context, final_hidden_state

class SequenceDecoder(nn.Module):
    def __init__(self, output_size, embedding_dim, hidden_size):
        super().__init__()
        self.embedding = nn.Embedding(output_size, embedding_dim)
        self.rnn = nn.GRU(embedding_dim + hidden_size, hidden_size)
        self.output_layer = nn.Linear(hidden_size, output_size)
        
    def forward(self, input_token, hidden_state):
        # Decoder with attention mechanism
        return predicted_token, updated_hidden_state

Training Pipeline

def train_model(model, dataloader, optimizer, criterion):
    model.train()
    total_loss = 0
    
    for src, tgt in dataloader:
        optimizer.zero_grad()
        outputs = model(src, tgt)
        loss = criterion(outputs.view(-1, outputs.size(2)), tgt.view(-1))
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
        optimizer.step()
        total_loss += loss.item()
    
    return total_loss / len(dataloader)

BLEU Evaluation

def calculate_bleu(model, test_data, reference_data):
    predictions = [translate(model, src) for src in test_data]
    return BLEU().corpus_score(predictions, [reference_data]).score

Production Inference

def generate_translations(model, input_file, output_path):
    with open(input_file, 'r') as f:
        sources = [line.strip() for line in f]
    
    results = [translate(model, src) for src in sources]
    
    with open(output_path, 'w') as f:
        f.write('\n'.join(results))