Natural Language Explanation: Generating Explanations

Module: Natural Language Processing | Difficulty: Advanced

Rationale Extraction

Explanation Generation

Evaluation

Faithfulness

import torch
import torch.nn as nn

class ExplanationGenerator(nn.Module):
    def __init__(self, encoder, decoder):
        super().__init__()
        self.encoder = encoder
        self.decoder = decoder
    def forward(self, input_text, label=None):
        encoded = self.encoder(input_text)
        if label is not None:
            output = self.decoder(label, encoded)
            return output
        return self.decoder.generate(encoded)

Research Insight: Generated explanations are often not faithful to the model's reasoning process. The key challenge is ensuring that explanations reflect the model's actual decision-making, not post-hoc rationalization. Contrastive explanations are more faithful than additive ones.