Semantic Parsing: Meaning Representation and Code Generation

Module: Natural Language Processing | Difficulty: Advanced

AMR (Abstract Meaning Representation)

Seq2Seq for Semantic Parsing

Code Generation

Evaluation

| Metric | AMR 2.0 | Code Generation | |--------|---------|-----------------| | BLEU | 45.2 | 78.3 | | Exact Match | - | 65.2 |

import torch
import torch.nn as nn

class SemanticParser(nn.Module):
    def __init__(self, encoder, decoder, vocab_size):
        super().__init__()
        self.encoder = encoder
        self.decoder = decoder
        self.output_proj = nn.Linear(decoder.d_model, vocab_size)
    def forward(self, src, tgt):
        enc_out = self.encoder(src)
        dec_out = self.decoder(tgt, enc_out)
        return self.output_proj(dec_out)

Research Insight: Code generation from natural language requires understanding both syntax and semantics. Pre-trained code models (CodeBERT, Codex) improved performance by 20-30% because they learn code structure from large corpora.