Argument Mining: Structure and Quality of Arguments

Module: Natural Language Processing | Difficulty: Advanced

Argument Components

• Claim: Statement to be supported
• Premise: Evidence supporting claim
• Warrant: Assumption connecting premise to claim

Relation Classification

Argument Quality

Evaluation

import torch
import torch.nn as nn

class ArgumentMiner(nn.Module):
    def __init__(self, bert_model, n_components, n_relations):
        super().__init__()
        self.bert = bert_model
        self.component_detector = nn.Linear(768, n_components)
        self.relation_classifier = nn.Linear(768*2, n_relations)
    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids, attention_mask=attention_mask)
        cls_output = outputs.last_hidden_state[:, 0]
        return self.component_detector(cls_output)

Research Insight: Argument mining is crucial for understanding persuasive text and automated reasoning. The key challenge is that arguments are often implicit and require world knowledge to understand. Pre-trained language models improve argument mining by 15-20%.