CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining
Authors: Debela Gemechu, Chris Reed
Accepted at: acl25

CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining

Debela Gemechu and Chris Reed
Centre for Argument Technology (ARG-tech)
University of Dundee
Dundee DD1 4HN, United Kingdom
{d.t.z.gemechu, c.a.reed}@dundee.ac.uk

Abstract

Argument Mining (AM) involves the automatic identification of argument structure in natural language. Traditional AM methods rely on micro-structural features derived from the internal properties of individual Argumentative Discourse Units (ADUs). However, argument structure is shaped by a macro-structure capturing the functional interdependence among ADUs. This macro-structure consists of segments, where each segment contains ADUs that fulfill specific roles to maintain coherence within the segment (local coherence) and across segments (global coherence). This paper presents an approach that models macro-structure, capturing both local and global coherence to identify argument structures. Experiments on heterogeneous datasets demonstrate superior performance in both in-dataset and cross-dataset evaluations. The cross-dataset evaluation shows that macro-structure enhances transferability to unseen datasets.

1 Introduction

Argument Mining (AM), a Natural Language Processing (NLP) task, involves identifying and analysing argument structures within natural language (Persing and Ng, 2016; Stab and Gurevych, 2017; Eger et al., 2017; Potash et al., 2016; Lawrence and Reed, 2020). It involves argument component segmentation (ACS), argument component type classification (ACTC), argument relation (AR) identification (ARI), and AR type classification (ARTC) (Peldszus and Stede, 2015a; Eger et al., 2017; Lawrence and Reed, 2020). This study focuses on ACTC, ARI, and ARTC, assuming that ADUs are provided as input..

The identification of argument structures requires modeling the roles of ADUs and ARs as functions of a global structure, governing coherent arrangement of these components to fulfill the overarching Discourse Purpose (DP) (Grosz and Sidner, 1986; Freeman, 2011). The global structure is decomposed into local structures, each aligned with a specific Discourse Segment Purpose (DSP). These localised structures ensure segment-level coherence by organising ADUs and ARs into functional units, much like how words combine into phrases to convey meaning (Grosz and Sidner, 1986). For instance, Figure 1 illustrates four localised structures in a COVID-19 contact tracing argument: (1) government preparedness (2) the effectiveness of South Korea’s contact tracing, (3) non-app-based tracing, and (4) advancements in testing. In each local structure, the ADUs fulfill the DSP of that segment. For example, the ADUs in segment (3) address the DSP of non-app-based tracing.

The arrangement of ADUs and the ARs within the local structures is shaped by the intentions of the arguer and the sequential ordering of ADUs ensuring a natural flow for maintaining coherence (Travis, 1984; Freeman, 2011; Wang et al., 2019; Kazemnejad et al., 2024). The intentional structure captures the logical flow of ADUs and can extend beyond proximity to connect ADUs based on their roles and contributions to the DSP of the segment (Grosz and Sidner, 1986; Freeman, 2011). Figure 1 illustrates this interplay, demonstrating the sequential flow of arguments (e.g., ADU1 → ADU2 → ADU3 → ADU4 → ADU5) alongside logical relationships transcending proximity (e.g., A14 → A17, A13 → A19, or A20 → A23). This underscores the importance of modeling macro-structure governing the intentional and the sequential flow of ADUs and their ARs. See details in Appendix C and more examples in Figure 3.

However, most previous works focus on features derived from the internal structure of ADUs, often referred to as the micro-structure (Freeman, 2011), while overlooking the broader macro-structure. They frame AM tasks as either dependency parsing (Peldszus and Stede, 2015b), sequence tagging (Eger et al., 2017), sequence classification
Figure 1: An example of an argument structure composed of 21 argument relations, which together form the global structure, is shown decomposed into four local structures (labelled 1 to 4). It shows how ADUs and AR are shaped by the intentional structure, where consecutive ADUs may span different segments (local structures), and non-consecutive ADUs can share the same segment. The argument is taken from QT30, illustrating a dialog between two participants, highlighted in light blue and yellow (Hautli-Janisz et al., 2022).

(Reimers et al., 2019; Ruiz-Dolz et al., 2021), or decompositionality (Gemechu and Reed, 2019; Trautmann, 2020), concentrating primarily on isolated ADU pairs. End-to-end AM approaches model dependencies between tasks, employing various techniques, including biaffine operations for learning non-tree AM structures (Morio et al., 2020), a transition-based model for constructing both tree and non-tree argument graphs (Bao et al., 2021), and positional encodings in generative AM frameworks to mitigate order biases (Bao et al., 2022). Recent advancements in language models (LM) have enhanced their ability to capture higher-level structures for processing long documents by encoding document sections, hierarchies, and global contexts, resulting in notable improvements in NLP tasks like document classification and summarisation (He et al., 2024; Cao and Wang, 2022; Liu et al., 2022; Bai et al., 2021). However, in the domain of AM, aside from position-aware discourse self-attention for identifying discourse elements (Song et al., 2020) and end-to-end approaches focusing on capturing dependencies between tasks, there has been limited progress in addressing the critical aspect of modeling macro-structures, which often remains implicit. To our knowledge, no AM work has proposed a unified architecture that models coherence by integrating macro-structure encoding logical and sequential ADU flows at both local and global levels, while anchoring AM tasks to this coherence.

In this study, we propose CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining, an approach that anchors ACTC, ARI, and ARTC tasks to a unified macro-structure. Given a pair of ADUs and the entire argument as context, the model predicts the ADU types (ACTC) and the AR between them (ARI and ARTC), considering both local and global structural information. This is accomplished through a multi-task learning approach that jointly models ACTC, ARI, and ARTC as primary tasks while treating local and global structure learning as auxiliary tasks. The argument is represented as a graph, where ADUs are nodes and ARs are edges, capturing the complete argument structure. Local and global structures learning is achieved by classifying graph edges into their respective local or global categories. A self-attention layer attends to the graph’s nodes and edges to encode the local and global structures relevant to the ADU pair under consideration. To contextualise ADU type and AR predictions within these macro-structures, the self-attention layer’s output is fused with the ADU-pair representation via a cross-attention mechanism. Additionally, the sequential flow of the argument is modeled by incorporating ADU-level positional encodings into the ADU embeddings. These positional encodings are derived from the order of ADUs and discourse participant transitions (e.g., proponent-opponent shifts) (Freeman, 2011; Budzynska and Reed, 2011).

This paper makes the following contributions: (a) We propose a macro-structure to capture the coherent arrangement of ADUs. (b) We introduce an architecture combining a graph-based neural model with a dual attention mechanism to capture local and global argument structures. A multi-task learning framework anchors ACTC, ARI, and ARTC
to these macro-structures. (c) We achieve state-of-the-art (SOTA) results across multiple datasets, including a cross-dataset evaluation where previous SOTA models struggle to surpass random chance.

2 Related Work

2.1 Argument Mining

Argument Mining has been studied through diverse paradigms, emphasising the micro-structure of arguments. One common approach frames AM as a dependency parsing task (Peldszus and Stede, 2015b), leveraging discourse parsing techniques (Muller et al., 2012). Peldszus and Stede (2016) extend this by mapping Rhetorical Structure Theory (RST) trees (Taboada and Mann, 2006) to argument structures. Other works model AM as token-based sequence tagging (Eger et al., 2017), classifying tokens into argument components and AR types using the BIO tagging scheme. Gemechu and Reed (2019) decompose ADUs into fine-grained components, predicting ARs based on their interactions. Recent studies fine-tune pre-trained LMs, employing sequence-pair classification setups (Reimers et al., 2019; Ruiz-Dolz et al., 2021). These configurations focus on the internal structure of ADUs, while neglecting the broader macro-structure.

Efforts toward end-to-end AM have largely focused on leveraging task interdependencies. Pipeline architectures train independent models for sub-tasks, integrating global constraints through Integer Linear Programming (ILP) (Persing and Ng, 2016; Stab and Gurevych, 2017). Neural approaches adopt joint multi-task setups to model interdependencies across tasks (Eger et al., 2017). Morio et al. (2022) introduce a cross-corpus training strategy, while Bao et al. (2022) propose a generative framework incorporating constrained pointer mechanisms and reconstructed positional encodings into an end-to-end AM setup. Despite these advancements, these methods emphasize task-level dependencies, offering limited or no explicit modeling of the macro-structure.

2.2 Structural Encoding in Language Models

Recent advancements in LMs have improved the capacity to encode long texts and represent document structures (He et al., 2024; Cao and Wang, 2022; Liu et al., 2022; Bai et al., 2021; Zaheer et al., 2020; Beltagy et al., 2020). For instance, He et al. (2024) and Cao and Wang (2022) utilise section structures to encode document hierarchies, while Liu et al. (2022) employ hierarchical sparse attention and specialised tokens to capture local and global information within a document. Similarly, Bai et al. (2021) use positional encoding at various linguistic segments to capture hierarchies. Beltagy et al. (2020) introduces Longformer, which combines local windowed attention with global for long-document. Zaheer et al. (2020) propose BigBird, a model leveraging sparse attention mechanisms that integrate global, local, and random attention patterns to handle long sequences. Although these models provide avenues for encoding macro-structures, their effectiveness in addressing the unique challenges of argumentation’s macro-structure remains limited (see Tables 2 and 3), primarily due to their reliance on static document structural features, which fail to capture the distinct characteristics of argumentation—such as logical relationships, argumentative flows, and the interplay between ADUs.

3 Method

3.1 Data

Heterogeneous datasets encompassing various domains and genres are utilised, including student persuasive essay corpora (AAEC) (Stab and Gurevych, 2017), Consumer Debt Collection Practices (CDCP) (Park and Cardie, 2018), the US 2016 presidential debate corpus (US2016) (Visser et al., 2019), and a corpus of argument and conflict in broadcast debate (QT30) (Hautli-Janisz et al., 2022). The AAEC and CDCP, are monological, while the US2016 and QT30 are dialogical. The datasets CDCP, AAE, QT30, and US2016 employ different annotation standards for ADUs and ARs. CDCP defines five ADU types—Reference, Fact, Testimony, Value, and Policy—and two AR types—Reason and Evidence. AAE uses three ADU types—MajorClaim, Claim, and Premise—and four AR types—Support, Attack, For, and Against. In the QT30 and US2016 datasets, ADUs are not explicitly labeled; instead, their types are inferred from the direction of the ARs (premise to conclusion), resulting in two ADU types: Premise and Conclusion.

3.1.1 Global Structure

The global structure consists of all valid ARs within the argument structure, which are essential for achieving the DP. These valid ARs represent a subset of the possible permutations of connections
<table>
  <tr>
    <th>Data</th>
    <th>No_arg</th>
    <th>No_ADU</th>
    <th>No_AR</th>
    <th>No_LOC</th>
    <th>Dist_ARs</th>
  </tr>
  <tr>
    <td>AAEC</td>
    <td>402</td>
    <td>6089</td>
    <td>5338</td>
    <td>3.3</td>
    <td>2.6</td>
  </tr>
  <tr>
    <td>US2016</td>
    <td>499</td>
    <td>8610</td>
    <td>3772</td>
    <td>5.1</td>
    <td>3.2</td>
  </tr>
  <tr>
    <td>QT30</td>
    <td>724</td>
    <td>11266</td>
    <td>3314</td>
    <td>7</td>
    <td>4.8</td>
  </tr>
  <tr>
    <td>CDCP</td>
    <td>731</td>
    <td>4779</td>
    <td>1353</td>
    <td>5.6</td>
    <td>3.4</td>
  </tr>
</table>

Table 1: Dataset summary of argument counts (No_arg), average number of ADUs (No_ADU), ARs (No_AR), local structures (No_LOC), and ADU distance in ARs (Dist_ARs) per argument.

between the ADUs in the argument. For example, in Figure 1, the argument consists of 24 ADUs, which could potentially form up to 552 unique connections (\(24 \times 23\)), excluding self-links. However, only 21 of these connections are valid, forming the global structure that conveys the discourse purpose by meaningfully linking the ADUs.

3.1.2 Local Structure
An argument structure is represented as graphs where ADUs are nodes and ARs forming the edges. Local Structure identification involves upward and downward traversals of the graph from each AR node. The upward traversal identifies chains of ADUs leading to the AR, capturing the local structure that establishes its context. The downward traversal, traces the chain of ADUs following the AR, ensuring the continuity of the argument segment. The beginning of a local structure is identified by a node with no inward connections (start ADU), marking the segment’s starting point, while its end is defined by a node without successors (end ADU), indicating the segment’s conclusion. In cases where the start ADU involves multiple downward chains (divergent structures), all such chains are included. Furthermore, every sub-graph—whether serial, divergent, convergent, or linked—between the start and end ADUs is incorporated to ensure a complete and coherent segment. The argument shown in Figure 1 consists of four segments numbered 1 through 4. More details and examples are provided in Appendix D. Table 1 provides a summary of the dataset statistics. Among the argument structures, 73% involve more than one local structure, with 67% containing between 2 to 7 local structures. Additionally, 64% of ARs occur between ADUs positioned 1 to 5 apart, and 17% involve ADUs that are within a distance of 1.

3.2 Model
This section provides an overview of the task definition, model architecture, and baselines.

3.2.1 Task Definition
Given an argument \(A\) comprising a sequence of ADUs and a specific ADU pair (\(ADU_i, ADU_j\)), the model’s primary task is to predict the types of \(ADU_i, ADU_j\), and the AR between them, one pair at a time, within the context of the argument’s macro-structure (local and global structure). To achieve this, the model is trained on auxiliary tasks that predict local and global structures, anchoring the primary task to these macro-structures in a multi-task setting. During inference, only the primary task is used. See Section A.2 for input details.

3.2.2 Architecture
As shown in Figure 2, the model consists of five key components: (A) Unified ADU Representation, which combines ADU embeddings with positional information; (B) Argument Structure Encoder, which employs a graph network where ADUs are nodes and ARs between them are edges; (C) ADU-Pair Encoder, which encodes the specific pair of ADUs under consideration; (D) Macro-Attention Layer, which attends to the graph’s nodes and edges to capture the ADUs and ARs that constitute the local and global structures relevant to the ADU pair; and (E) Classification Layers, which predict ADU types, ARs, and classify graph edges as local, global, or none, in a multi-task setting.

(A) Unified ADU Representation (UAR)
ADU representation combines a pre-trained LM embedding, with two ADU-level positional embeddings capturing sequential argument flow. Given an argument \(A = \{ADU_1, ADU_2, \ldots, ADU_n\}\), the unified representation, \(ADU'_i\) is given by:

\[
ADU'_i = ADU_i \oplus O_i \oplus P_i
\]

where \(ADU_i\) is the sentence embedding (\(S\)) of the ADU obtained by mean pooling token embeddings from LM. \(O_i\) is the order-based positional embedding indicating the ADU’s sequential index, and \(P_i\) is the participant transition embedding, capturing participant shifts in multi-participant dialogues, with all ADUs assigned the same index in monologues. We experiment with sinusoidal absolute positional encodings (Vaswani et al., 2017) and relative positional embeddings (Shaw et al., 2018). Absolute positional embeddings are added to ADU embeddings, while relative embeddings are fused during attention computation (See Section B.2).
(B) Argument Structure Encoder (ASE)
A Graph Neural Network (GNN) (Brody et al., 2021) models the argument structure as a graph \( G = (V, E) \), where nodes \( V = \{v_1, v_2, \ldots, v_n\} \) represent ADUs, and edges \( E \subseteq V \times V \) capture ARs between ADUs, facilitating local and global structure predictions relevant to a given ADU pair. The graph is constructed using the unified ADU embeddings from Equation 1, where each node represents an ADU within the argument. Initially, the graph is fully connected—every node is linked to every other node—without any prior assumptions about the existence or direction of argument relations. During training, the model learns to infer the correct argument structure, as detailed in section (E). At each layer, node states are updated as:

\[
\mathbf{h}_v^{(k+1)} = \sigma \left( \mathbf{W}_k \mathbf{h}_v^{(k)} + \sum_{u \in \mathcal{N}(v)} \mathbf{W}'_k \mathbf{h}_u^{(k)} \right)
\]

where \( \mathbf{h}_v^{(k)} \) is the node state at layer \( k \), and \( \mathcal{N}(v) \) denotes neighboring ADUs. Each AR between ADUs is represented by the concatenation of their node embeddings.

(C) ADU-Pair Encoder (APE)
Encodes the relationship between the pair of ADUs (\( ADU_i, ADU_j \)) under consideration. A feedforward layer is applied to the unified embeddings of \( ADU_i \) and \( ADU_j \) to capture their interaction.

(D) Macro-Attention Layer (MAL)
Two self-attention layers attend to the argument graph from step B, learning the local and global structures relevant to the ADU pair from step C. The self-attentions are applied to the edge embeddings of the argument graph to capture the relationships between nodes (i.e., the AR between ADUs). The outputs from both attention layers pass through fully connected layers, and used to classify the edges into their respective macro-structures.

To contextualise the ADU type and AR predictions within the macro-structural context, the outputs of the self-attention layers are fused with the ADU-pair representation from step C using a cross-attention mechanism. The queries are derived from the ADU-pair encoder’s output, while the keys and values are projections of the summation of the self-attention layers. The final representation of the ADU pair, denoted as \( \mathbf{R}_{ADU-pair} \), is obtained by adding the cross-attention’s output to the original ADU-pair encoder’s output. This final representation combining both the structural context and the ADU pair representation is used to predict both the ADU types and the ARs between them.

(E) Classification Layers
Linear classifiers are used for predicting the ADU pair types (ACTC) and AR between them (ARI and ARTC), using the contextualised ADU-pair representations, \( \mathbf{R}_{ADU-pair} \). We jointly model ARI and ARTC, following the approach in (Bao et al., 2021), while also modeling ARI independently for comparison with studies treating them separately.

The local and global structures are learned by modeling the presence or absence of edges in the argument graph as binary classification tasks, using gold-standard labels prepared in Sections 3.1.1 and 3.1.2. For global structure learning, the model considers all possible edges between ADU pairs within the argument graph. Each edge is assigned a binary label: a positive label indicates the existence of a valid argumentative relation, while all remaining edges are labeled as negative. For local structure learning, the focus is restricted to a subgraph defined by a specific segment—bounded by the start and end ADUs of a local argument span as defined in Section 3.1.2. Only the edges that lie within this span and correspond to valid argumentative relations are labeled as positive; all others are labeled
negative. This dual-structure supervision guides the model to capture both the broader argumentative flow and the finer-grained local discourse dependencies. These macro-structures predictions are framed as auxiliary tasks within a multi-task setup.

The model trains using a loss function (\( L \)) that integrates task-specific losses and a regularisation term, \( L = L_0 + L_1 + L_2 + L_3 + R \), where \( L_0 \) is the loss for ADU type prediction, \( L_1 \) for AR classification, \( L_2 \) for global-structure prediction, \( L_3 \) for local-structure prediction, and \( R \) for the regularization term. Due to the significantly higher number of non-AR edges compared to AR edges, \( L_2 \) is computed exclusively for AR edges, excluding non-AR edges. Similarly, \( L_3 \) ignores both AR edges and non-AR edges outside the local structure. However, this approach leads to model overfitting on AR edges. To mitigate this issue, a regularisation term (\( R \)) based on the distance editing score is introduced to penalise deviations from the gold argument structure, encouraging correct classification of both AR and non-AR edges.

3.2.3 Baselines
We establish two baselines using RoBERTa (Liu et al., 2019), reportedly achieving strong performance in AM and BigBird (Zaheer et al., 2020), for its architecture in capturing global context. The first baseline, Vanilla Sequence-Pair Classification (V-SeqCIs), fine-tunes the LMs on concatenated ADU pairs. The second, Vanilla Argument Context (V-ArgC), includes the entire argument as context alongside ADU pairs for direct comparison to CU-MAM. Since both LMs are used in CU-MAM for ADU embeddings, evaluating them as standalone baselines ensures robust comparisons. BigBird serves as a strong baseline due to its global context modeling (see Appendix A.3 for more details).

4 Experiment

4.1 Training setup
The models are trained for six epochs with a batch size of 16 using Adam optimiser (Kingma and Ba, 2014) with a learning rate of \( 2 \times 10^{-5} \). The primary tasks use categorical cross-entropy, while auxiliary tasks for graph edge prediction use binary cross-entropy. Results are averaged over three runs with different random seeds. Additional experimental details are in Appendix A. The code and dataset are available at https://github.com/arg-tech/cumam.

4.2 Implementation Details
For the AAEC and CDPC, we use the provided training and test data, sampling 10% of the training set for validation. We split the US2016 and QT30 datasets into 70% training, 20% testing, and 10% validation. For AAEC, results are reported at the essay level (AAEC-E), with two paragraph-level results for comparison with related works. The AAEC-P* merges the Claim:Against and Claim:For labels into a single Claim label, while AAEC-P uses the original annotations. For cross-dataset evaluations, we use AAEC-P* since US2016 and QT30 do not include Major Claim as an argument component type. Similarly, we merge 'For' and 'Against' into Support and Attack, respectively in the AAEC, while the 'Rephrase' ARs in QT30 and US2016 are merged into Support relation.

ADU embeddings in the CU-MAM configurations are obtained from RoBERTa and BigBird.

4.3 Evaluation Setup
The models are evaluated using two setups: In-Dataset Evaluation (ID) and Cross-Dataset Evaluation (CD). For ID, the models are trained and evaluated on the same dataset using the provided training-test split. In CD, the models are trained on one dataset and evaluated on the remaining \( n - 1 \) datasets to assess their performance on unseen data. CDPC is excluded from the CD setup due to differences in ADU and AR type annotations. Across both setups, average macro F-scores (F) are reported for the test dataset. We also report the F1 score for comparison with related works.

4.4 Comparison Systems
CU-MAM is benchmarked against related works, including Bao et al. (2021), Morio et al. (2020), Ruiz-Dolz et al. (2021), Gemechu and Reed (2019), Potash et al. (2017), Kikteva et al. (2023) and GPT-4o (OpenAI, 2023). GPT-4o serves as a strong baseline due to its design for advanced reasoning tasks. It is evaluated using a few-shot prompting setup, with details of the prompting strategy provided in Section B.3. We also make indirect comparisons with Eger et al. (2017), Morio et al. (2022), and Bao et al. (2022), which combine argument segmentation with ACT, ARI, and ARTC.

4.5 Results
Tables 2 and 3 compare the performance of CU-MAM and the two baselines (Section 3.2.3) across
<table>
  <tr>
    <th rowspan="2">LM</th>
    <th rowspan="2">Model</th>
    <th colspan="4">ACTC</th>
    <th colspan="4">ARI</th>
    <th colspan="4">ARTC</th>
  </tr>
  <tr>
    <th>AAEC</th>
    <th>CDCP</th>
    <th>US2016</th>
    <th>QT30</th>
    <th>AAEC</th>
    <th>CDCP</th>
    <th>US2016</th>
    <th>QT30</th>
    <th>AAEC</th>
    <th>CDCP</th>
    <th>US2016</th>
    <th>QT30</th>
  </tr>
  <tr>
    <td rowspan="3">RoBERTa</td>
    <td>V-SeqCls (Baseline)</td>
    <td>69.4</td>
    <td>77.6</td>
    <td>69.7</td>
    <td>71.1</td>
    <td>56.6</td>
    <td>62.1</td>
    <td>72.5</td>
    <td>71.7</td>
    <td>50.1</td>
    <td>14.2</td>
    <td>67.1</td>
    <td>68.3</td>
  </tr>
  <tr>
    <td>V-ArgC (Baseline)</td>
    <td>66.4</td>
    <td>73.3</td>
    <td>65.0</td>
    <td>66.4</td>
    <td>54.4</td>
    <td>59.2</td>
    <td>68.5</td>
    <td>69.4</td>
    <td>49.3</td>
    <td>13.4</td>
    <td>64.8</td>
    <td>67.3</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>rd</sup> (ours)</td>
    <td>77.5</td>
    <td>83.1</td>
    <td>75.9</td>
    <td>75.5</td>
    <td>68.1</td>
    <td>70.4</td>
    <td>78.7</td>
    <td>77.1</td>
    <td>58.1</td>
    <td>30.6</td>
    <td>75.8</td>
    <td>76.6</td>
  </tr>
  <tr>
    <td rowspan="3">BigBird</td>
    <td>V-SeqCls (Baseline)</td>
    <td>69.2</td>
    <td>77.4</td>
    <td>68.4</td>
    <td>70.3</td>
    <td>57.8</td>
    <td>64.3</td>
    <td>69.2</td>
    <td>71.1</td>
    <td>50.1</td>
    <td>15.2</td>
    <td>67.4</td>
    <td>68.2</td>
  </tr>
  <tr>
    <td>V-ArgC (Baseline)</td>
    <td>70.7</td>
    <td>78.3</td>
    <td>70.3</td>
    <td>71.6</td>
    <td>60.9</td>
    <td>64.8</td>
    <td>74.2</td>
    <td>74.1</td>
    <td>49.4</td>
    <td>16.7</td>
    <td>68.9</td>
    <td>68.4</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>rd</sup> (ours)</td>
    <td>77.2</td>
    <td>84.6</td>
    <td>75.4</td>
    <td>76.8</td>
    <td>70.4</td>
    <td>72.3</td>
    <td>80.7</td>
    <td>78.4</td>
    <td>58.4</td>
    <td>31.4</td>
    <td>76.6</td>
    <td>75.2</td>
  </tr>
</table>

Table 2: In-dataset evaluation performance of CU-MAM and baseline models (V-SeqCls and V-ArgC).

<table>
  <tr>
    <th rowspan="2">LLM</th>
    <th rowspan="2">Model</th>
    <th colspan="3">ACTC</th>
    <th colspan="3">ARI</th>
    <th colspan="3">ARTC</th>
  </tr>
  <tr>
    <th>AAEC</th>
    <th>US2016</th>
    <th>QT30</th>
    <th>AAEC</th>
    <th>US2016</th>
    <th>QT30</th>
    <th>AAEC</th>
    <th>US2016</th>
    <th>QT30</th>
  </tr>
  <tr>
    <td rowspan="3">RoBERTa</td>
    <td>V-SeqCls (Baseline)</td>
    <td>52.1</td>
    <td>55.4</td>
    <td>48.9</td>
    <td>46.2</td>
    <td>48.2</td>
    <td>47.8</td>
    <td>38.9</td>
    <td>45.1</td>
    <td>44.4</td>
  </tr>
  <tr>
    <td>V-ArgC (Baseline)</td>
    <td>47.5</td>
    <td>52.4</td>
    <td>48.6</td>
    <td>38.9</td>
    <td>46.9</td>
    <td>47.5</td>
    <td>36.9</td>
    <td>44.2</td>
    <td>41.6</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>rd</sup> (ours)</td>
    <td>64.6</td>
    <td>66.1</td>
    <td><b>66.4</b></td>
    <td>56.2</td>
    <td>62.0</td>
    <td>60.5</td>
    <td>50.9</td>
    <td>58.5</td>
    <td>58.0</td>
  </tr>
  <tr>
    <td rowspan="3">BigBird</td>
    <td>V-SeqCls (Baseline)</td>
    <td>55.5</td>
    <td>51.4</td>
    <td>50.2</td>
    <td>43.9</td>
    <td>53.6</td>
    <td>54.3</td>
    <td>40.6</td>
    <td>45.4</td>
    <td>45.3</td>
  </tr>
  <tr>
    <td>V-ArgC (Baseline)</td>
    <td>56.6</td>
    <td>53.5</td>
    <td>55.5</td>
    <td>47.3</td>
    <td>56.7</td>
    <td>56.5</td>
    <td>43.6</td>
    <td>46.5</td>
    <td>47.1</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>rd</sup> (ours)</td>
    <td><b>65.7</b></td>
    <td><b>67.4</b></td>
    <td>66.3</td>
    <td><b>57.7</b></td>
    <td><b>66.0</b></td>
    <td><b>64.5</b></td>
    <td><b>51.8</b></td>
    <td><b>61.1</b></td>
    <td><b>60.5</b></td>
  </tr>
</table>

Table 3: Cross-dataset evaluation performance of CU-MAM and baseline models (V-SeqCls and V-ArgC).

the datasets in ID and CD setups. CU-MAM refers to full configuration with local and global structure prediction and Macro-Attention (unless specified). The main results are reported for CU-MAM with relative positional encoding (CU-MAM<sup>rd</sup>) due to its superiority over absolute encoding. Tables 2 and 3 clearly show that incorporating macro-structural features improves performance across all tasks in both ID and CD, as described below.

In-Dataset (ID) Evaluation
As can be seen in Table 2, CU-MAM consistently outperforms the two baseline methods that rely solely on fine-tuning LMs across all tasks. In average, for ACTC, CU-MAM improves by 6.6% over V-SeqCls and 8% over V-ArgC. For ARI, the improvements are 8.9% and 9%, respectively, while for ARTC, CU-MAM shows 10.9% and 11.3% improvements over V-SeqCls and V-ArgC. These results highlight the effectiveness of macro-structural features, leading to superior performance compared to the baselines. To calculate the improvement over V-SeqCls, we subtract the average performance of BigBird and RoBERTa V-SeqCls from the average performance of BigBird and RoBERTa CU-MAM. Similar approach is used for calculating the improvements over V-ArgC.

Cross-Dataset (CD) Evaluation
Similarly, Table 3 shows that CU-MAM excels in CD evaluations. For ACTC, CU-MAM improves by 13.8% over V-SeqCls and 15.2% over V-ArgC. For ARI, the improvements are 12.4% and 12.1%, respectively. For ARTC, CU-MAM shows improvements of 13.5% and 14.4%. Notably, CU-MAM consistently surpasses the baselines, often achieving cross-dataset performance that is comparable to in-dataset evaluations. For example, when trained on the QT30 dataset and tested on US2016, the BigBird-based CU-MAM matches in-dataset performance. In contrast, baseline models perform near-random chance, showcasing CU-MAM’s strong cross-dataset generalisation.

Comparison Systems
As can be seen in Table 4, CU-MAM outperforms all comparison systems, including the indirect comparison approaches that combine argument segmentation with ACT, ARI, and ARTC. The indirect comparisons should be interpreted cautiously due to differences in task setups. BigBird-based CU-MAM outperforms RoBERTa-based CU-MAM configurations in both ID and CD, highlighting its strength in capturing global contexts. GPT-4 exhibits performance variation consistent with other comparison works.

4.6 Error Analysis
We categorise the error types in CU-MAM versus the baselines as "Jump-to-Conclusion," "Reversed Connection," and "Non-relational ADU Link" errors, all affecting sequential and logical coherence (see examples in Appendix E). The "Jump-to-Conclusion" Error occurs, when an ADU \( A \) is linked directly to ADU \( C \) bypassing the interme-
<table>
  <tr>
    <th rowspan="2">Dataset</th>
    <th rowspan="2">Model</th>
    <th colspan="3">ACTC</th>
    <th colspan="3">ARTC</th>
  </tr>
  <tr>
    <th>F1</th>
    <th>Macro</th>
    <th>ARI</th>
    <th>F1</th>
    <th>Macro</th>
    <th>ARI</th>
  </tr>
  <tr>
    <td rowspan="4">AAEC-E</td>
    <td>Eger et al. (2017)</td>
    <td>66.2</td>
    <td>-</td>
    <td>-</td>
    <td>34.5</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Morio et al. (2022)</td>
    <td>76.6</td>
    <td>-</td>
    <td>-</td>
    <td>48.7</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>61.4</td>
    <td>59.7</td>
    <td>54.6</td>
    <td>56.4</td>
    <td>51.1</td>
    <td>49.3</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>d</sup></td>
    <td><b>79.3</b></td>
    <td>77.2</td>
    <td>62.6</td>
    <td>70.4</td>
    <td><b>60.1</b></td>
    <td><b>58.6</b></td>
  </tr>
  <tr>
    <td rowspan="4">AAEC-P</td>
    <td>Bao et al. (2022)</td>
    <td>75.9</td>
    <td>-</td>
    <td>-</td>
    <td>50.1</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Eger et al. (2017)</td>
    <td>70.8</td>
    <td>-</td>
    <td>-</td>
    <td>45.5</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Morio et al. (2022)</td>
    <td>76.5</td>
    <td>-</td>
    <td>-</td>
    <td>59.6</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>63.1</td>
    <td>63.3</td>
    <td>54.9</td>
    <td>60.2</td>
    <td>52.3</td>
    <td>50.4</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>d</sup></td>
    <td><b>79.8</b></td>
    <td>78.4</td>
    <td>66.3</td>
    <td>81.2</td>
    <td><b>64.4</b></td>
    <td><b>63.1</b></td>
  </tr>
  <tr>
    <td rowspan="4">AAEC-P*</td>
    <td>Potash et al. (2017)</td>
    <td>-</td>
    <td>84.9</td>
    <td>60.8</td>
    <td>76.7</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Morio et al. (2022)</td>
    <td>88.4</td>
    <td>86.8</td>
    <td>69.3</td>
    <td>-</td>
    <td>68.1</td>
    <td>57.1</td>
  </tr>
  <tr>
    <td>Bao et al. (2021)</td>
    <td><b>88.4</b></td>
    <td>70.6</td>
    <td>82.5</td>
    <td>-</td>
    <td>81</td>
    <td>-</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>66.4</td>
    <td>65.7</td>
    <td>58.2</td>
    <td>67.7</td>
    <td>56.5</td>
    <td>54.2</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>d</sup></td>
    <td><b>88.7</b></td>
    <td>87.1</td>
    <td>75.4</td>
    <td><b>85.4</b></td>
    <td><b>73.1</b></td>
    <td><b>82.7</b></td>
  </tr>
  <tr>
    <td rowspan="4">CDCP</td>
    <td>Bao et al. (2022)</td>
    <td>57.7</td>
    <td>-</td>
    <td>-</td>
    <td>16.6</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Morio et al. (2022)</td>
    <td>81.0</td>
    <td>82.3</td>
    <td>40.2</td>
    <td>-</td>
    <td>40.1</td>
    <td>20.4</td>
  </tr>
  <tr>
    <td>Bao et al. (2021)</td>
    <td>-</td>
    <td>82.5</td>
    <td>37.3</td>
    <td>67.8</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Morio et al. (2020)</td>
    <td>58.5</td>
    <td>78.4</td>
    <td>34.0</td>
    <td>-</td>
    <td>61.3</td>
    <td>32.2</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>58.5</td>
    <td>84.6</td>
    <td>44.8</td>
    <td>72.3</td>
    <td>45.1</td>
    <td>31.4</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>d</sup></td>
    <td><b>83.4</b></td>
    <td><b>84.6</b></td>
    <td><b>44.8</b></td>
    <td><b>72.3</b></td>
    <td><b>45.1</b></td>
    <td><b>31.4</b></td>
  </tr>
  <tr>
    <td rowspan="4">US2016</td>
    <td>Ruiz-Dole et al. (2021)</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Genemich and Reed (2019)</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>64</td>
    <td>-</td>
    <td>62</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>64.9</td>
    <td>62.3</td>
    <td>52.6</td>
    <td>56.7</td>
    <td>58.4</td>
    <td>54.6</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>d</sup></td>
    <td><b>77.3</b></td>
    <td><b>75.4</b></td>
    <td><b>63.8</b></td>
    <td><b>80.7</b></td>
    <td><b>79.8</b></td>
    <td><b>76.6</b></td>
  </tr>
  <tr>
    <td rowspan="4">QT30</td>
    <td>Kikteva et al. (2023)</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>65.1</td>
    <td>64.6</td>
    <td>51.8</td>
    <td>57.1</td>
    <td>58.1</td>
    <td>56.0</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>d</sup></td>
    <td><b>75.8</b></td>
    <td><b>76.8</b></td>
    <td><b>62.5</b></td>
    <td><b>78.4</b></td>
    <td><b>77.8</b></td>
    <td><b>75.2</b></td>
  </tr>
</table>

Table 4: CU-MAM performance and comparison approaches.

diary ADU B. The "Reversed Connection" error happens when the direction of an AR is incorrect, and the "Non-relational ADU Link" error arises when ARs are formed between unrelated ADUs.

Misclassification errors are reduced in both local and global structures, highlighting CU-MAM’s effectiveness in maintaining coherence at both levels. Analysis of 50 argument maps shows that 61% of the baseline’s misclassifications occur within the same local structure, compared to just 12% for CU-MAM, resulting in a 77.4% reduction in errors. While CU-MAM is more effective at reducing local structure errors, it also reduces global misclassifications, though most errors still arise from global structure issues, such as incorrect connections between cross-local structures or ADUs outside the argument graph. Moreover, CU-MAM reduces "Jump-to-Conclusion" errors to 16% (down from 56% in the baseline) and cuts "Reversed Connection" errors by 32%, demonstrating its ability to preserve logical and sequential flow.

4.7 Ablation study

We analyse the contributions of local-structure prediction and global-structure prediction by evaluating CU-MAM without local-structure prediction (CU-MAM<sup>L</sup>) and without global-structure prediction (CU-MAM<sup>G</sup>). We also analyse the impact of Macro-Attention by evaluating CU-MAM without the Macro-Attention (CU-MAM<sup>Att</sup>). These configurations are compared against: (a) the baseline performance, computed as the average of V-SeqCls and V-ArgC, and (b) the full CU-MAM (CU-MAM<sup>Full</sup>), which includes local-structure prediction, global-structure prediction and Macro-Attention. These configurations of CU-MAM are based on BigBird and absolute positional embeddings, since it achieved the highest performance (See Section F.1 for more details). We also assess the impacts of the two types of positional information and the effectiveness of absolute versus relative embeddings (see Section F.2 for more details).

Local vs. Global Structure Prediction. As shown in Table 5, both CU-MAM<sup>L</sup> and CU-MAM<sup>G</sup> outperform the baseline, highlighting the effectiveness of each structure prediction on its own. However, either local-structure or global-structure prediction can not achieve the performance level of CU-MAM<sup>Full</sup> individually, confirming their complementary benefits. CU-MAM<sup>L</sup> performs worse than CU-MAM<sup>G</sup>, suggesting that local-structure prediction has a greater impact than global-structure prediction.

Macro-Attention Layer (MAL). In CU-MAM<sup>Att</sup> configuration, local and global structures are predicted using a stack of feedforward layers instead of MAL, applied to the output of argument structure encoder, ensuring the same parameter count for a fair comparison with CU-MAM<sup>Full</sup>. We also evaluate CU-MAM with the MAL but without the auxiliary tasks (CU-MAM<sup>Att-only</sup>), to isolate the effect of MAL. In CU-MAM<sup>Att-only</sup>, the MAL is used only for computing the cross-attention that contextualises the ADU-pair. As shown in Table 5, ablating MAL results in a performance drop com-

<table>
  <tr>
    <th>Config</th>
    <th colspan="2">ACTC</th>
    <th colspan="2">ARTC</th>
  </tr>
  <tr>
    <th></th>
    <th>ID</th>
    <th>CD</th>
    <th>ID</th>
    <th>CD</th>
  </tr>
  <tr>
    <td>Baseline</td>
    <td>72.1</td>
    <td>53.7</td>
    <td>50.5</td>
    <td>44.7</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>G</sup></td>
    <td>76.7</td>
    <td>63.5</td>
    <td>58.7</td>
    <td>54.5</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>L</sup></td>
    <td>74.8</td>
    <td>60.5</td>
    <td>56.2</td>
    <td>52.1</td>
  </tr>
  <tr>
    <td>CU-MAM-Att</td>
    <td>75.2</td>
    <td>64.2</td>
    <td>60.1</td>
    <td>53.1</td>
  </tr>
  <tr>
    <td>CU-MAM-Att-only</td>
    <td>73.5</td>
    <td>59.8</td>
    <td>56.5</td>
    <td>50.8</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>Full</sup></td>
    <td><b>78.5</b></td>
    <td><b>66.5</b></td>
    <td><b>60.3</b></td>
    <td><b>57.8</b></td>
  </tr>
</table>

Table 5: Ablation study of CU-MAM configurations, reporting F1-scores under ID and CD settings. Baseline results are included for comparison. The F1 scores are averaged across the dataset for the ACTC and ARTC tasks.
pared to CU-MAM^{Full}, although it still outperforms the baseline. However, CU-MAM^{Att-only} performs worse than CU-MAM^{Att}, emphasising MAL is more effective when combined with auxiliary tasks.

Positional Encoding: We evaluate the impact of order embedding (\( O \)) and participant transition embedding (\( P \)) individually using absolute (Abs) and relative (Rel) positional encodings. Table 6 shows the performance with each positional feature ablated, highlighting the reduction in performance when each feature is removed (e.g., \( P^- \) represents CU-MAM without \( P \)). As can be seen from the Table, ablating \( O \) results in a greater performance drop compared to ablating \( P \) on both monological and dialogical datasets, with \( P \) showing no impact on the monological dataset. In average, Rel based configurations outperform Abs configurations.

<table>
  <tr>
    <th>Config</th>
    <th>Monologue</th>
    <th>Dialogue</th>
  </tr>
  <tr>
    <td>Full (Abs)</td>
    <td>43.3</td>
    <td>74.4</td>
  </tr>
  <tr>
    <td>Full (Rel)</td>
    <td>44.5</td>
    <td>76.1</td>
  </tr>
  <tr>
    <td>\( O^- \) (Abs)</td>
    <td>42.1</td>
    <td>71.3</td>
  </tr>
  <tr>
    <td>\( O^- \) (Rel)</td>
    <td>42.4</td>
    <td>71.6</td>
  </tr>
  <tr>
    <td>\( P^- \) (Abs)</td>
    <td>43.1</td>
    <td>72.4</td>
  </tr>
  <tr>
    <td>\( P^- \) (Rel)</td>
    <td>44.3</td>
    <td>72.7</td>
  </tr>
</table>

Table 6: F1-scores for CU-MAM configurations without \( P, O \) on monological and dialogical datasets in the ID evaluation. The results are averaged across the dataset on ARTC.

5 Conclusion

This work introduces CU-MAM, the first approach modeling AM tasks as a function of macro-structure to capture coherence. By leveraging structural representations, it models logical and sequential argument flow, capturing local and global dependencies. CU-MAM achieves significant performance gains over baselines and comparison approaches, setting new SOTA results across datasets. Its strong cross-dataset adaptability overcomes domain adaptation challenges where existing SOTA models struggle, demonstrating its ability to generalise across diverse argumentation structures.

Acknowledgments

This work is funded in part by the ‘AI for Citizen Intelligence Coaching against Disinformation (TITAN)’ project, funded by the EU Horizon 2020 research and innovation programme under grant agreement 101070658, and by UK Research and Innovation under the UK governments Horizon funding guarantee grant numbers 10040483 and 10055990; in part by Volkswagen Stiftung Foundation under grant 98 543, "Deliberation Laboratory"; and in part by the Swiss National Science Foundation under grant 10001FM_200857, "Mining argumentative patterns in context".

Limitations

Despite its merits, the CU-MAM approach has the following limitations:

Limited Applicability to Other NLP Tasks: The participants transitions features and local-structure encoding are specifically designed for argumentation tasks. As such, their applicability to other NLP tasks that do not involve argumentative structures is limited.

Pre-Training Objectives Not Addressed: Although the evaluation focuses on fine-tuning for leveraging macro-structural features, it does not address the training objectives that could be employed during the pre-training phase of LLMs to better integrate these features.

Interpretability and Explainability: The explanations for the model’s performance are based on empirical results, ablation studies, and error analysis. While these analyses are valuable, additional techniques such as attention mechanism analysis could provide a more comprehensive understanding of model behavior.

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A Experiment Setup

A.1 Training Procedure

Hyper-parameters: We employ Adam optimisation (Kingma and Ba, 2014) to minimise the cost function, using a learning rate of \( 2 \times 10^{-5} \) and categorical cross-entropy loss and a batch size of 16. Experimental results are reported based on the average of three runs with different random seeds.

Gradient Clipping: To prevent exploding gradients during training, we applied gradient clipping. We used a maximum gradient norm (max_grad_norm) parameter to determine the threshold for gradient clipping.

Warm-up and Learning Rate Schedule: We employ a linear warm-up strategy for the learning rate. The number of warm-up steps is set to 10% of the total training steps. Following the warm-up phase, the learning rate schedule is determined by a lambda function. This function linearly increases the learning rate during the warm-up phase and decreases it linearly thereafter.

A.2 Input Setup

Except the V-SeqClas configurations, the entire argument along with the pair of ADUs is provided to the model.
The Input Format:“{Argument} [EG] {premise} [SEP] {conclusion}”, where Argument = {ADU1 [SEP] ADU2 [SEP] ... ADUn}, with n representing the number of ADUs in the argument.

Extracting Relevant Argument: When the entire argument exceeds the maximum sequence length allowed by the underlying LM, a relevant span of the argument is extracted that includes both the premise and conclusion while staying within the length limit. This process is carried out as follows:

1. Length Calculation: The argument, premise, and conclusion are tokenized using the model’s tokenizer. The total length is then calculated by summing the tokens for the premise, conclusion, argument, and special tokens ([CLS] and [SEP]).

2. Span Selection:
   • If the total length is within the model’s maximum sequence limit, the entire argument is concatenated with the premise and conclusion.
   • If the total length exceeds the limit:
     – The positions of the premise and conclusion within the argument are identified, and a span is selected that includes both, along with additional surrounding context, ensuring the total length fits within the limit.
     – If including the span involving both the premise and conclusion exceed the maximum limit, start with the premise, expand the span towards the conclusion until the size constraint is met, and append the conclusion to the argument span.

Maximum Number of ADUs in an Argument: We set the maximum number of ADUs to 128 for computational efficiency.

A.3 Base LM
Both for the baselines and the CU-MAM configurations, we utilise the HuggingFace implementation of RoBERTa¹, BigBird ². In the baseline setup (both with and without argument context), we fine-tune the models based on the output of the [CLS] token from the final output layer. Similarly, ADU embeddings in the CU-MAM configurations are obtained from RoBERTa and BigBird.

B CU-MAM Architecture

B.1 ADU Embedding
We utilise pre-trained LMs (Liu et al., 2019; Radford et al., 2019; Zhang et al., 2020) to obtain contextualised token embeddings \( \mathbf{H} \in R^{n \times d} \) for the entire input where \( n \) is the input length and \( d \) is the hidden size of the model. ADUs are identified within the sequence using the special separator token ([SEP]). To obtain embeddings for each ADU, we apply mean pooling over the token embeddings within each ADU. Let \( \mathbf{H}_i \in R^{l_i \times d} \) represent the token embeddings for the \( i \)-th ADU, where \( l_i \) is the length of the \( i \)-th ADU. The ADU embedding \( \mathbf{ADU}_i \in R^d \) is computed as:

\[
\mathbf{ADU}_i = \frac{1}{l_i} \sum_{j=1}^{l_i} \mathbf{H}_{i,j}
\]

The resulting set of ADU embeddings forms a matrix \( \mathbf{A} \in R^{m \times d} \), where \( m \) is the number of ADUs.

B.2 Positional Encoding
We experiment with both fixed and relative positional embeddings. For absolute positional embeddings, we employ the sinusoidal position signal, following the approach introduced by the Transformer model (Vaswani et al., 2017). For relative positional embeddings, we adopt the method proposed by Shaw et al. (2018), which encodes the relative distances between ADU in the argument, \( a_{ij} = e_{j-i} \), where \( e \) represents the learnable embeddings and \( j - i \) indicates the relative distance between ADU \( j \) and ADU \( i \). We leverage dual positional embeddings to incorporate the two types of positional information: the index representing the order of each ADUs within the argument (ADU order embedding) and the participant transition embedding. Both approaches are further explained below.

Absolute Positional Encoding. The embedding of an ADU, denoted as \( \mathbf{ADU}_i \), is enhanced with absolute positional information by incorporating both order embeddings and participant transition embeddings. This process involves the following steps:

¹https://huggingface.co/docs/transformers/en/model_doc/roberta
²https://huggingface.co/docs/transformers/en/model_doc/big_bird
1. Sinusoidal Function for Embeddings: Consistent with the approach used in standard Transformers, sinusoidal functions are employed to generate embeddings for argument flow (\( \mathbf{T}_i \)) based on both ADU order (\( \mathbf{O}_i \)) and proponent-opponent transitions (\( \mathbf{P}_i \)):

\[
\mathbf{T}_{(index,2i)} = \sin \left( \frac{index}{10000^{2i/d_{model}}} \right)
\]
\[
\mathbf{T}_{(index,2i+1)} = \cos \left( \frac{index}{10000^{2i/d_{model}}} \right)
\]

where \( index \) denotes the position of the ADU and \( d_{model} \) is the dimensionality of the model. This method applies to both ADU order and participant transition embeddings, providing a unified approach for incorporating positional information.

Each ADU is represented by fusing its ADU embedding (\( \mathbf{ADU}_i \)), order embedding (\( \mathbf{O}_i \)), and participant transition embedding (\( \mathbf{P}_i \)) to form a unified representation of ADU (\( \mathbf{ADU}_i^{\text{abs}} \)). A matrix \( \mathbf{A}_{\text{abs}} \) of size \( n \times d \) is formed, where \( n \) is the number of ADUs in the argument and \( d \) is the embedding dimension:

\[
\mathbf{ADU}_{i,j}^{\text{abs}} = \mathbf{ADU}_i + \mathbf{O}_i + \mathbf{P}_i
\]

2. Relative Positional Encoding. The attention mechanism adjusts the attention scores \( \mathbf{A}'_{i,j} \) to integrate relative distances on the fly:

\[
\mathbf{ADU}_{i,j}^{\text{rel}} = \operatorname{softmax} \left( \frac{\mathbf{Q}_i \mathbf{K}_j^T}{\sqrt{d_k}} + \mathbf{R}_{i,j}^O + \mathbf{R}_{i,j}^P \right)
\]

where \( \mathbf{Q} \), \( \mathbf{K} \), and \( \mathbf{V} \) are the query, key, and value matrices, respectively, derived from the ADUs embeddings. \( \mathbf{R}_{i,j}^O \) represents the embeddings of the relative order information and is given by, \( \mathbf{R}_{i,j}^O = \mathbf{W}^O(\text{pos}_i - \text{pos}_j) \). \( \mathbf{W}^O \) is the learnable weight matrix for ADU positions, and \( O_i \) and \( O_j \) are the index reflecting the order of the ADUs \( i \) and \( j \) within the argument. \( \mathbf{R}_{i,j}^P \) represents the relative embeddings for participant transition and is given by, \( \mathbf{R}_{i,j}^P = \mathbf{W}^P(P_i - P_j) \). \( \mathbf{W}^P \) is the learnable weight matrix for turn number, and \( P_i \) and \( P_j \) are the transition numbers of ADUs \( i \) and \( j \) within the argument.

B.3 GPT for AR Prediction

B.3.1 Experimental Settings

We utilise the chat completion configuration of GPT-4o for the three tasks.

1. Configurations: We use GPT-4o and set a maximum token limit of 2048, a temperature of 0.7, a top-p probability of 0.9.

2. Prompts Strategy: We employ few-shot prompts, where specific examples are provided as part of the instruction. We create prompt templates that include instructions and two examples randomly selected from a list of examples. An example of a prompt template for the ARTC task is shown below.

You are a 3-class classifier model tasked with assigning a label to the argument relation between two argument units (argument 1 and argument 2).
Classify the following pair of arguments,
argument 1: {ADU_1}
argument 2: {ADU_2},
into:
"support" (if argument 1 supports argument 2),
"contradict" (if argument 1 attacks argument 2),
and "None" (if no argument relation exists between argument 1 and argument 2).
Please enter:
1 - for support,
2 - for contradict,
0 - for None relation.
Examples from each argument relation types are provided below:
Example 1: the argument relation between the argument "people feel, when they have been voicing opinions on different matters, that they have been not listened to", and the argument "people feel that they have been treated disrespectfully on all sides of the different arguments and disputes going on"
is support, and hence prediction label is 1.
Example 2: The argument relation between "there would be no non-tariff barriers with the deal done with the EU" and the argument "there are lots of non-tariff barriers with the deal done with the EU"
is contradiction, and hence prediction label is 2.

Note: We use the actual examples to show support and contradiction relations, which should be a
placeholder variable in the final prompt template.

C Macro-Structure

An argument is a coherent arrangement of utterances organised in a specific order (Grosz and Sidner, 1986; Toulmin, 1958; Freeman, 2011). Freeman (2011) propose a framework describing how these utterances collectively contribute to natural language argumentation, particularly focusing on their supportive roles and structural patterns, termed as “macro-structure”. This framework encompasses techniques such as divergent, convergent, linked, and serial reasoning, which illustrate how reasons combine to support conclusions. It underscores the significance of understanding the entire sequence of ideas within an argument, including claims, challenges, responses, and counter-responses, to establish coherent structure.

Coherence within discourse can be viewed at two levels: local coherence and global coherence. Local coherence refers to coherence among the utterances in a segment of an argument, while global coherence refers to the coherence spanning segments (Grosz and Sidner, 1986; Grosz et al., 1995). Grosz and Sidner argue that the coherence depends on the intentional structure of discourse addressed via the overall DP and DSP (Grosz and Sidner, 1986; Grosz et al., 1995). These intentions are reflective of the speaker’s goals, akin to Gricean conversational implicatures (Grice, 1975). In a multi-party discourse, the DSP for a given segment aligns with the intention of the conversational participant initiating that segment (Lochbaum, 1994). Freeman (2011) models these interactions as the interplay between the proponents and opponents, showing how proponents assert and address opponents’ challenges, forming a chain of reasoning and highlighting the importance of tracing these transitions for understanding the argument.

IAT (Budzynska and Reed, 2011) offers a framework representing how argument structure is linked to the intentional structure and the dynamics within dialogue structure. In essence, IAT offers a macro-structural analysis by representing the intentional structure and illocutionary dynamics within argumentative discourse, by linking dialogical moves to their communicative intentions and illocutionary forces. For example, Figure (1b) illustrates participant interactions alongside argument structures, showcasing diverse dialogue moves such as “Asserting”, “Arguing”, “Questioning”, “Illocuting”, and “Restating” (Budzynska and Reed, 2011). Annotated corpora, such as the corpus of US presidential debate 2016 (Visser et al., 2019) annotated following such framework, exemplify how dialogical interactions unfold as a series of moves, each mapped to a structural element within the argument graph. Although these dynamics are common in dialogue, similar conceptualisations apply to monologue, where a speaker delivers multiple utterances to an audience (Grosz et al., 1995).

The macro-structure is formally defined below to clarify how it contrasts with micro-structures. For discourse segments \( l_0, \ldots, l_n \), define the function:

\[
\mathrm{ADU}(l_i) = \begin{cases}
A_j & \text{if } l_i \in A_j \\
\emptyset & \text{if } l_i \notin \text{any ADU}
\end{cases}
\]

Let the set of ADUs be \( A = \{A_0, \ldots, A_k\} \).
Define the argument graph (AG):

\[
AG = \langle A, E \rangle, \quad E \subseteq A \times A
\]

To identify the argument relation (AR) between \( A_i \) and \( A_j \):

**Micro-Structure:**

\[
f_{mic}\left(\mathrm{ADU}^{-1}(A_i), \mathrm{ADU}^{-1}(A_j)\right)
\]

**Macro-Structure:**

\[
f_{mac}\left( \bigcup_{A_x \in G(A_i)} \mathrm{ADU}^{-1}(A_x), \bigcup_{A_y \in G(A_j)} \mathrm{ADU}^{-1}(A_y) \right)
\]

Where \( G(A_i) \) is the set of ADUs reachable from \( A_i \):

\[
G(A_i) = \{A_j \mid \text{Path}(A_i, A_j)\}
\]

Path is defined recursively:

\[
\text{Path}(A_i, A_j) \Leftrightarrow \begin{cases}
(A_i, A_j) \in E, \\
\exists A_x : (A_i, A_x) \in E \land \text{Path}(A_x, A_j)
\end{cases}
\]

D Local Structures Extraction from Argument Map

We navigate through argument following an upward traversal to identify the chain of ADUs leading to the AR node and a downward traversal to identify the chain of ADUs following the AR node. The algorithm marks the end of each local-structure in the upward traversal by identifying nodes without inward connections and in the downward traversal by identifying nodes without successors. It includes all chains of ADUs that end at the same node to form the local-structure.

Local-structures are segments of the argument map that represent coherent chains of ADUs leading to and following an AR. We present Algorithm 1 to outline the procedure for extracting local-structures from a global argument map. The algorithm takes as input the argument map represented
Algorithm 1 Extract Local-Structures from Argument Map

Require: Argument map represented as nodes and edges, with each node categorised as ADU, and AR
Ensure: List of local-structures
    Initialise an empty list to store local-structures: local_structures
    Identify nodes corresponding to AR Nodes in the argument map
for each ADU Node in the argument map do
    Perform an upward traversal to identify the chain of ADUs leading to the AR
    Perform a downward traversal to identify the chain of ADUs following the AR Node
    Mark the start of each local-structure in the upward traversal by identifying nodes without inward connections
    Mark the end of each local-structure in the downward traversal by identifying nodes without successors
    Include all chains of ADUs between the start and end node
    Add the identified local-structure to local_structures
end for
return local_structures

![An argument structure with 5 segments (A,B,C,D and E)](page_246_370_489_312.png)
(a) An argument structure with 5 segments (A,B,C,D and E)

![An argument structure with 3 segments (A,B and C)](page_774_370_489_312.png)
(b) An argument structure with 3 segments (A,B and C)

![An argument structure with 3 segments (A,B and C)](page_246_728_489_312.png)
(c) An argument structure with 3 segments (A,B and C)

![An argument structure with 5 segments (A,B,C,D and E)](page_774_728_489_312.png)
(d) An argument structure with 5 segments (A,B,C,D and E)

Figure 3: An example of argument structures involving multiple segments. ADUs are logically interconnected via AR to form coherent argument structure. Figure (a) and (b) are taken from AAEC, while (c) and (d) are taken from QT30. As can be seen from the figure, (a) and (b) forms one complete graph while (c) and (d) are scattered into multiple disconnected graphs forming islands of argument segments.
as nodes and edges, where each node represents ADUs and the ARs. The relations between ADUs are presented based on the edges between the ADU and AR nodes. The algorithm generates a list of local-structures that are pertinent to the respective ARs within the argument map. To evaluate the correctness of local structures, two annotators assessed each local structure produced by the algorithm as either correct or incorrect. A local structure is considered correct if it aligns with the expected argument segment. The annotators are provided with a guideline that describes an argument segment. The inter-annotator agreement, measured with the Kappa statistic, was 0.78, indicating substantial agreement.

For illustrative purposes, Figure 3 presents several examples showcasing argument maps that feature multiple local-structures. In these examples, the local-structures are annotated with numerical labels. Each number used for annotation corresponds to a distinct local-structure. ARs that share the same numerical label are part of the same local-structure.

E Error Analysis

Figure 4 presents an example of an argument map generated by the baseline model. In this map, argument relations are labeled with numbers, and incorrect AR predictions are highlighted with an (x) symbol. The figure provides a visual representation of the errors made by the baseline model, allowing for a clearer understanding of the error types in AR predictions.

F Ablation Study Setup

In this section, we outline the setup for the ablation study, which aims to assess the impact of different components within the CU-MAM architecture. By systematically removing specific components or features, we compare the resulting configurations against both the complete CU-MAM model (upper-bound performance) and the baseline (lower-bound performance). The study evaluates the effect of key components, including local-structure prediction, global-structure prediction, the Macro-Attention Layer (MAL), and positional information in various configurations of CU-MAM.

F.1 Local and Global Structure prediction and MAL Ablation

We evaluate several configurations of the CU-MAM architecture, each of which targets the ablation of a specific component:

• CU-MAM^{L}: This configuration removes the local-structure prediction, retaining only the global-structure prediction and the Macro-Attention Layer (MAL).

• CU-MAM^{G}: This configuration removes the global-structure prediction, retaining the local-structure prediction and MAL.

• CU-MAM^{Att}: This configuration removes the MAL and replaces it with a stack of feed-forward layers (with a comparable number of parameters to MAL) for local and global structure predictions. This substitution allows a fair comparison of model size between the two configurations.

• CU-MAM^{Att-only}: This configuration isolates the effect of MAL by using it only to compute the cross-attention between the pair of ADUs under consideration, without involving any auxiliary tasks. In this setup, the two classifier layers predicting the local and global structures are removed, allowing for an evaluation of MAL’s impact without the auxiliary tasks.

• CU-MAM^{Full}: This configuration incorporates all components: local-structure prediction, global-structure prediction, and the Macro-Attention Layer, representing the full CU-MAM model.

For Simplicity, the following points outline the setup for the local and global structure prediction and MAL ablation study:

• we consider ACTS and ARTC task.

• The study is based on BigBird based CU-MAM configuration with relative positional encoding since it achieve the highest performance.

• For each configuration an average performance across the dataset is reported for both ID and CD to provide a single performance value for each configuration.
Figure 4: Example of error analysis. The argument map displays relations with arbitrary numbering, where incorrect predictions are marked with an (x) symbol.

The baseline performance is calculated by averaging the F1-scores of the two baseline, V-SeqCls and V-ArgC, across the entire dataset for each task. Specifically, the baseline for each task is calculated as follows:

\[
\text{Baseline}_{\text{ACTC}} = \text{avg}(\text{V-SeqCls}, \text{V-ArgC})_{\text{ACTC}}
\]

\[
\text{Baseline}_{\text{ARTC}} = \text{avg}(\text{V-SeqCls}, \text{V-ArgC})_{\text{ARTC}}
\]

This average is computed over the complete dataset for each respective task.

F.2 Positional Encoding Ablation
We investigate the impact of different positional encoding strategies through an ablation study, where each positional feature is removed individually to assess its effect on model performance. Specifically, we evaluate the use of two types of positional information:

• Order embedding (\( O \))

• Participant transition embedding (\( P \))

For each type of positional encoding, we test both absolute (Abs) and relative (Rel) encodings. The ablation study is conducted by systematically removing each of these positional features and comparing the resulting performance against the full configuration, which includes both \( O \) and \( P \) embeddings. A drop in performance after removing a feature highlights its contribution to the overall model’s effectiveness. By comparing the performance of each ablated configuration to the full model, we isolate and quantify the impact of each positional feature. The configurations for ablating the positional information are as follows:

• Full (Abs): The model uses both order and participant transition embeddings with absolute positional encoding.
• Full (Rel): The model uses both order and participant transition embeddings with relative positional encoding.

• \( O^- \) (Abs): The model is ablated by removing the order embedding with absolute positional encoding.

• \( O^- \) (Rel): The model is ablated by removing the order embedding with relative positional encoding.

• \( P^- \) (Abs): The model is ablated by removing the participant transition embedding with absolute positional encoding.

• \( P^- \) (Rel): The model is ablated by removing the participant transition embedding with relative positional encoding.

For simplicity, the following points outline the setup for the positional encoding ablation study:

• The study is conducted on the ARTC task.

• The ablation study is based on the BigBird-based CU-MAM configuration, as it achieved the highest performance in previous experiments.

• For each configuration, the average performance across the dataset is reported for CD evaluations on monologue and dialogue datasets separately, providing a single performance value for each configuration and comparing their effectiveness with respect to the dataset nature.

G complexity Analysis

The overall complexity of the proposed model is dominated by the Argument Structure Encoder (ASE) and Macro-Attention Layer (MAL), which involve quadratic operations with respect to the number of ADUs, \( n \), and the embedding dimension, \( d \). Specifically, the total complexity is \( O(n^2 \cdot d) \), due to the graph-based and attention-based interactions among ADUs.
H  Glossary of Abbreviations

<table>
  <tr>
    <th>Abbreviation</th>
    <th>Full Form / Definition</th>
  </tr>
  <tr>
    <td>AAEC</td>
    <td>Argumentative Essays Corpus (Student Persuasive Essays)</td>
  </tr>
  <tr>
    <td>ACTC</td>
    <td>Argument Component Type Classification / ADU Classification Task (ADU Type Classification)</td>
  </tr>
  <tr>
    <td>ADU</td>
    <td>Argumentative Discourse Unit</td>
  </tr>
  <tr>
    <td>APE</td>
    <td>ADU-Pair Encoder</td>
  </tr>
  <tr>
    <td>AR</td>
    <td>Argument Relation / Argumentative Relation</td>
  </tr>
  <tr>
    <td>ARI</td>
    <td>Argument Relation Identification</td>
  </tr>
  <tr>
    <td>ARTC</td>
    <td>Argument Relation Type Classification</td>
  </tr>
  <tr>
    <td>ASE</td>
    <td>Argument Structure Encoder</td>
  </tr>
  <tr>
    <td>BigBird</td>
    <td>BigBird Transformer Model (efficient transformer for long sequences)</td>
  </tr>
  <tr>
    <td>BIO</td>
    <td>Beginning, Inside, Outside (tagging scheme)</td>
  </tr>
  <tr>
    <td>CD</td>
    <td>Cross-Dataset evaluation</td>
  </tr>
  <tr>
    <td>CDCP</td>
    <td>Consumer Debt Collection Practices dataset</td>
  </tr>
  <tr>
    <td>CU-MAM</td>
    <td>Coherence-Driven Unified Macro-Structures for Argument Mining</td>
  </tr>
  <tr>
    <td>CU-MAM<sup>rel</sup></td>
    <td>CU-MAM with relative positional encoding</td>
  </tr>
  <tr>
    <td>DP</td>
    <td>Discourse Purpose</td>
  </tr>
  <tr>
    <td>Dist_ARs</td>
    <td>Distance of Argumentative Relations within Arguments</td>
  </tr>
  <tr>
    <td>GNN</td>
    <td>Graph Neural Network</td>
  </tr>
  <tr>
    <td>ID</td>
    <td>In-Dataset evaluation</td>
  </tr>
  <tr>
    <td>ILP</td>
    <td>Integer Linear Programming</td>
  </tr>
  <tr>
    <td>LM</td>
    <td>Language Model</td>
  </tr>
  <tr>
    <td>MAL</td>
    <td>Macro-Attention Layer</td>
  </tr>
  <tr>
    <td>No_ADU</td>
    <td>Number of Argumentative Discourse Units</td>
  </tr>
  <tr>
    <td>No_AR</td>
    <td>Number of Argumentative Relations</td>
  </tr>
  <tr>
    <td>No_LOC</td>
    <td>Number of Local Structures</td>
  </tr>
  <tr>
    <td>No_arg</td>
    <td>Number of Arguments</td>
  </tr>
  <tr>
    <td>O</td>
    <td>Order embedding (positional feature encoding sequence order)</td>
  </tr>
  <tr>
    <td>P</td>
    <td>Participant transition embedding (positional feature encoding speaker turns or participants)</td>
  </tr>
  <tr>
    <td>QT30</td>
    <td>Corpus of Argument and Conflict in Broadcast Debate</td>
  </tr>
  <tr>
    <td>RST</td>
    <td>Rhetorical Structure Theory</td>
  </tr>
  <tr>
    <td>RoBERTa</td>
    <td>Robustly Optimized BERT Pretraining Approach</td>
  </tr>
  <tr>
    <td>SOTA</td>
    <td>State Of The Art</td>
  </tr>
  <tr>
    <td>UAR</td>
    <td>Unified ADU Representation</td>
  </tr>
  <tr>
    <td>US2016</td>
    <td>US 2016 Presidential Debate Corpus</td>
  </tr>
  <tr>
    <td>V-ArgC</td>
    <td>Baseline model: Vanilla Argument Context</td>
  </tr>
  <tr>
    <td>V-SeqCls</td>
    <td>Baseline model: Vanilla Sequence-Pair Classification</td>
  </tr>
  <tr>
    <td>Abs</td>
    <td>Absolute positional encoding</td>
  </tr>
  <tr>
    <td>Rel</td>
    <td>Relative positional encoding</td>
  </tr>
</table>

Reviewer paper summaries: The paper introduces CU-MAM, an argument mining architecture that is said to be useful for examining argument macrostructures. The authors explore the architecture for three argument mining tasks: argument component type classification (ACTC), argument relation identification (ARI), and argument relation type classification (ARTC). Four argument mining datasets are used to compare the method with baselines and other works. An ablation study is also included.

This paper introduced a macro-structure-based approach to integrate both local and global coherence and unify the identification of argument components and relations. Improved argument-mining performance and stronger cross-dataset generalizability.
Summary of strengths: The proposed architecture is interesting and specifically tailored to address the argument mining tasks; an ablation study is included to show the merits of each model component.
The model obtains good results, in comparison with baselines, also in a cross-dataset setting, which shows the model is more robust.

It introduced coherence-driven macro-structures in argument mining, a unified approach for multiple AM tasks, and an effective graph-based attention mechanism.
Summary of weaknesses: The distinction between global and local structure should be clarified. 
The comparison with LLMs is limited to a single model (GPT-4o) and employing a zero-shot setting.

Please check the Comments Suggestions And Typos
Comments, suggestions and typos: The distinction between global and local structure is not always clear. In particular, it is not clear what is being predicted in global-structure prediction.

In section 3.1.2: isn't the graph composed of ADUs as nodes and ARs as edges? If so, correct the sentence in lines 248-250.

In lines 446-447: why are rephrase ARs merged into support relations?

In Table 4, what is AAEC-E?

Typos:
- line 228: US2026 -> US2016
- lines 229 and 234: AAE -> AAEC
- line 348: Two self-attention layers attends -> Two self-attention layers attend
- line 357: there is a reference to the self section
- line 467: check how the citations to Potash and Kikteva are being made (\cite instead of \citet)
- line 479: MAcro-Attention -> Macro-Attention
- line 487: Tables 2 -> Table 2

Add a brief, formal definition of “macro-structure” and how it differs from micro-structures such as references to earlier rhetorical or coherence theories.

Identity how your “macro-structure” approach improves or diverges from classical multi-level or hierarchical approaches such as a mention of “macro-propositions” or “macro-level discourse” from older theories.

The introduction mentioned “argument component segmentation,” but the final approach focuses on the classification parts. Please explain that you assume ADUs are given, or if the approach can be adapted to handle segmentation.

Do you consider all possible subpaths from a start node to any end node? What about subgraphs with branching merges (convergent structures)?

The paper mentioned that ARI is to be predicted. So if ARs are not known yet, how is the initial graph built? Or are you using a “partial graph” from gold references during training?

List the training-time usage and inference-time usage.

Whether the auxiliary tasks help build the “macro-structure.” are all predicted for every ADU pair or predicted once for the entire argument.

Adding a short example or pseudo-code snippet that clarifies how to combine the two attention outputs for the cross-attention to the ADU pair.

Add a short complexity analysis.

Add a detailed table or explanation of how the paper unifies different ADU definitions.

Add a small analysis of whether GPT-4 struggles with specialized definitions or small data.
Overall assessment scores: 4

2.5