Argumentative Relationship Recognition Based on
End-to-End Multitask Learning
Jingyuan Huang

Quntian Fang

Sijie Wang

National University of Defense
Technology
jingyuanhuang@nudt.edu.cn

National University of Defense
Technology
fangquntian@nudt.edu.cn

National University of Defense
Technology
970831193@qq.com

Zhiliang Tian∗

Feng Liu∗

Zhen Huang

National University of Defense
Technology
tianzhiliang@nudt.edu.cn

National University of Defense
Technology
richardlf@nudt.edu.cn

National University of Defense
Technology
huangzhen@nudt.edu.cn

Dongsheng Li
National University of Defense
Technology
dsli@nudt.edu.cn

Abstract
In the realm of deep learning, many neural network-basedmodels have emerged for recognizing argument relationships. However, these approaches often focus on specificargument-mining tasks aligned with their research goals
and don’t address all argument-mining subtasks comprehensively. Moreover, previous studies tend to assess models ona limited set of datasets from a single domain, sometimes
relying on domain-specific external knowledge, which limits these methods’ practical applicability. In this paper, we
introduce LFAM, an end-to-end multi-task learning-based
argument mining model. This model identifies argumentcomponent boundaries by using BIO labels at first. Then
combines token-level and span-level features to capture robust contextual information. Finally, it employs the bi-affine
structure for dependency parsing to represent argument relationships and uses the Chu-Liu/Edmonds Algorithm topredict argument relationship existence with maximal tree
diagrams, while achieving argument relationship classification. We conducted experiments on six argument-mining
datasets across various domains, demonstrating the effectiveness of LFAM.

1

crucial step. For instance, when comparing Argument text
1 and Argument text 2, it’s evident that they lack semantic
correlation. On the other hand, Argument text 4 refines theideas presented in Argument text 3, indicating a supportive
relationship between them. Additionally, Argument text 6
directly counters the points made in Argument text 5, emphasizing the role of public transportation as a vital elementin transportation choices.

Introduction

Argument Relation Recognition (ARR) is to identify semantic
connections among key argumentative statements within argumentative texts, while simultaneously assessing their presence and categorizing them. ARR comprises two essential aspects in argument mining: Argument Relation Identification(ARI) and Argument Relation Classification (ARC). Thesetasks require a deep understanding of the complex semanticinteractions among the textual components. As shown in
Fig. 1, argumentation relationship recognition serves as a
∗ Corresponding Authors

Figure 1. Argumentative Relationship Recognition Task.
With the rapid rise of deep learning, numerous neuralnetwork-based models have emerged in academic discussions, focusing on recognizing argument relationships. Scholars have extensively explored the connections between thesetasks, proposing various multi-task learning approaches. For
instance, Liao et al. [12] have introduced a multi-task iterative training approach that encompasses three subtasks

within the argument mining domain. In parallel, Dutta etal. [7] have advanced a pioneering prompt-based argument
relation recognition framework, which seamlessly integrates
pertinent contextual information, thereby engendering amodel characterized by robust generalizability. These approaches often involve joint training of specific sub-tasks,
each contributing distinct capabilities in the field of argument mining, ultimately leading to enhanced recognition
performance.
Nonetheless, these methods exhibit inherent limitations.
The pursuit of constructing a comprehensive end-to-end
argument-mining model remains a challenging task because
it requires solving all argument-mining subtasks at once ina unified framework while maintaining consistent performance. Researchers[15, 16] often choose to focus on specificargument-mining subtasks that align with their researchgoals for joint learning. This approach frequently overlooks
a thorough examination of all argument-mining subtasks.
Similarly, prevailing studies[24] typically assess model effectiveness using a limited set of datasets in a specific domain,and some researchers incorporate domain-specific external
knowledge to improve the identification process. However,this practice may not be widely applicable in practical situations.
In light of these considerations, this paper introduces anovel argument-mining model grounded in an end-to-end
multi-task learning model (LFAM). The model marks argument component boundaries through the application of BIO
tags. Then it conducts distinct token-level and span-levelfeature interactions to capture richer contextual information.
Finally, the architecture employs the Bi-affine structure fordependency parsing, thereby obtaining the representationof argument relations, and leveraging the Chu-Liu/Edmonds
Algorithm to forecast the presence of argument relations
through maximal tree diagrams, while achieving argument
relation classification.
The main contributions of this paper are as follows: (1)We
propose LFAM, a comprehensive end-to-end argument mining model that handles all argument mining tasks while
ensuring stable and consistent performance. (2)We use thebi-affine structure and the Chu-Liu/Edmonds Algorithm for
dependency parsing to model argument relationships. Additionally, we employ maximal tree diagrams to predict thepresence and category of argument relationships. (3)We conducted experiments on six argument-mining datasets spanning various domains, illustrating the effectiveness of LFAM.

2

Related Work

The main goal of argument relation recognition is to findmeaningful connections between important statements in argumentative texts while checking if these connections exist
and sorting them into categories. Currently, the approaches

for this task can be grouped into three categories:(1) textual feature-based methods, (2) shallow feature extraction
methods, and (3) deep feature extraction methods.
2.1 Textual Feature-based Methods
Textual feature-based methods primarily rely on features
crafted through human expertise to facilitate recognition,with these features including lexical, semantic, contextual,and core idea constructs. Mochales et al. [14] applied humanconstructed, context-independent grammatical features topredict argument component relationships, aligning withgrammatical rules mirroring the rhetorical and structural
attributes commonly found in judicial texts. These textual
features have been employed diversely in the literature. For
instance, Persing et al. [22] extracted structural, grammatical,
and lexical features for each argument, using positional information for structural attributes and character analysis for
lexical features. Nguyen et al. [17] augmented their model’s
performance by combining topic-related information fromthe paper with features captured from a contextual windowof sentences surrounding the argument. Paul et al. [20] introduced an unsupervised graph-based ranking approach, incorporating multi-hop knowledge from external sources into
their model and utilizing a cross-attention mechanism [10]to enhance text and knowledge representations, leading to
improved argumentation relation recognition across datasets.
While these methods can improve performance in domainspecific contexts, they need manual feature extraction andsetup, limiting their generalizability.
2.2

Shallow Feature Extraction Methods

Shallow feature extraction methods primarily employ classical early machine learning models. Researchers choose thedifferent traditional machine-learning models based on thefeature patterns found in various datasets. Subsequently, labeled data is constructed using these selected feature patterns
to train the argument relation recognition model. Ghosh etal. [9] utilized supervised machine learning with ten-foldcross-validation for argument relation classification. They
introduced a novel approach based on logistic regression anddecision trees. Rosenthal et al. [25] conducted experimentswith methods involving plain bayes, logistic regression, anddecision trees, with their findings showing that the logistic
regression-based classifier outperformed the others. Carstens
et al. [5] proposed a relationship-based argument-mining
method that initially classifies sentences based on their mutual relationships, offering a new perspective on argumentmining relationships. Stab et al. [26] proposed a solution
using a traditional machine learning approach, employing abinary support vector machine for making predictions about
relationships and classifying argument relationship types.
Wang et al. [29] applied a Conditional Random Fields (CRF)based sequential model for sentence-level or paragraph-level
text prediction. However, methods relying on shallow featureextraction are typically structurally simple, lack generalizability across diverse datasets, and exhibit suboptimal overall
recognition performance, thereby limiting their widespread
adoption.
2.3

Deep Feature Extraction Methods

Deep feature extraction methods employ deep modelingtechniques based on pre-trained language models, often involving the utilization of pre-trained language models andfrequently adopting multi-task learning approaches[6, 31].
This strategy is employed to significantly enhance the performance of recognizing argument relationships. Opitz etal. [18] utilized the BERT model as the foundational word
vector and introduced relevant connectives based on various
argument relationships to enrich the contextual information
within the original argumentative text. They transform thetask from predicting argument relations to assessing the
logical coherence of sorted pairs of text. Experimental results underscored the method’s effectiveness, especially in
improving the accuracy of predicting objection-type argument relations. Liao et al. [12] proposed a multi-task iterative training method, addressing three sub-tasks related tothe argument mining domain. Dutta et al. [7] introduced anovel prompt-based argumenta relation recognition methodthat integrates contextual information. This model possesses
several attributes, including the ability to recognize argument relations in the domain of argument mining and highgeneralizability. Typically, these methods are jointly trainedwith specific subtasks within the argument mining domain
to enhance recognition outcomes through complementary
performance. However, it’s important to note that these approaches may not include all four subtasks of argument
mining, potentially limiting their broader applicability.

3

Problem Definition

LFAM is designed to tackle all fundamental subtasks within
the realm of argument mining: (1) boundary segmentation,(2) component recognition, (3) relation identification, and(4) relation classification. Firstly, we use the token sequence
𝐸 = {𝜔 1, ..., 𝜔𝑛 } denote the thesis text, where 𝜔𝑖 signifies
the 𝑖-th token in the thesis text. For the boundary segmentation task, we employ the notation < 𝑠, 𝑒 >∈ S to signify
the range of selected argument components, where 𝑠 and𝑒 represent the index of the starting and the ending token
position of the argument component respectively, and S
constitutes the set containing these argument components.
For the component recognition task, we employ the triple notation < 𝑠, 𝑒, 𝑐 >∈ C, where 𝑐 indicates the type of argumentcomponent associated with the < 𝑠, 𝑒 >, and C represents a
set of pre-defined argument component types. For the relation identification task, it can be expressed as a quintuple< 𝑠𝑠𝑟𝑐 , 𝑒𝑠𝑟𝑐 , 𝑠𝑡𝑎𝑟 , 𝑒𝑡𝑎𝑟 , 𝑏 >∈ B, where 𝑠𝑠𝑟𝑐 and 𝑒𝑠𝑟𝑐 indicate thesource argument component, 𝑠𝑡𝑎𝑟 and 𝑒𝑡𝑎𝑟 indicate the target

argument component, the variable 𝑏 denotes the presence orabsence of a relationship, and B represents a bicategorical set
that including the existence and non-existence of argumentative relations. Finally, the relation classification task is also
denoted as a quintuple < 𝑠𝑠𝑟𝑐 , 𝑒𝑠𝑟𝑐 , 𝑠𝑡𝑎𝑟 , 𝑒𝑡𝑎𝑟 , 𝑟 >∈ R, where𝑟 signifies the specific type of relationship between two argument components, and R denotes the set of predefinedargument relation types.

4

Model

The LFAM framework comprises four essential components:(1) a text embedding layer, (2) a feature extraction layer,(3) a bi-affine layer, and (4) a maximal tree map layer. The
text embedding layer leverages the semantic knowledge of apre-trained model to encode each token within the original
thesis text while employing BIO tags for boundary segmentation. The feature extraction layer employs Bidirectional
Long Short-Term Memory (BiLSTM) to boost interactions
at both the token level and span level, thus addressing thecomponent identification subtask. The bi-affine layer employs Bi-affine operations to calculate the probability of adirected edge connecting any two spans. Subsequently, themaximum tree graph layer utilizes the maximum tree graphalgorithm to construct an argument structure graph, forboth relationship identification and relationship classification. Fig. 2 illustrates the framework of LFAM.
4.1

Text Embedding Layer

We employ the Longformer-base[3] pre-trained language
model to encode the thesis texts, which is a suitable choicefor processing token-level argumentative content. Given theinitial input thesis text 𝐸 = {𝜔 1, 𝜔 2, ..., 𝜔𝑛 }, we transform it
into a textual representation E = {𝑡𝑜𝑘𝑒𝑛 1, 𝑡𝑜𝑘𝑒𝑛 2, ..., 𝑡𝑜𝑘𝑒𝑛𝑛 }.
To capture richer semantic knowledge, it is worth notingthat the pre-trained language model predominantly stores
semantic information in its higher-level network layers. Consequently, we concatenate the feature information from thelast four layers of the model to create a representation foreach token 𝑡𝑜𝑘𝑒𝑛𝑖 = [𝐿𝐹𝑖(9) , 𝐿𝐹𝑖(10) , 𝐿𝐹𝑖(11) , 𝐿𝐹𝑖(12) ] ∈ R (4ℎ) .
Subsequently, we apply an MLP layer to reduce the dimensionality of each token to the standard dimension ℎ. Additionally, an essential token "[𝐶𝐿𝑆]" is the beginning of all tokens,serving as a global attention focal point for the Longformer
model to capture comprehensive argumentation knowledge
from the entire text.
Next, the boundary segmentation is addressed throughthe utilization of BIO labels, where "B" signifies the "Begin"of a span, "I" denotes an "Intermediate" position within thespan, and "O" indicates other characters, typically with thefirst "O" adjacent to an "I" indicating the end of the span. To
enhance the appropriateness of boundary segmentation, we
refine BIO annotations by introducing the following rules: (1)if the initial label in the sequence is "I," it is modified to "B";

Figure 2. The overview of our LFAM framework.
(2) when the sequence begins with a non-labeled position
and the current label is "I" while the previous one is "O," thecurrent label is updated to "B." As illustrated in Fig. 3, in thecase of the label "I" at ordinal number 4, it adheres to the
optimization rule as the previous label is "O" and this "I" isthe first one immediately following an "O", so it is modified
to "B". Finally, three valid span divisions are obtained as aresult of this process.

Consequently, from the vectorized textual representation
E, we obtain the subsequent representation for the argument
span denoted as 𝑆𝑝𝑎𝑛𝑖 𝑗 = (𝑖, 𝑗)|𝑖, 𝑗 ∈ [0, 𝑛], 𝑖 ≤ 𝑗 ∈ S, with
S representing the set encompassing all possible spans.
4.2

Feature Extraction Layer

The feature extraction layer captures feature information
at both token and span levels. Initially, token-level feature

Figure 3. Optimized BIO annotation diagram.
interaction is accomplished using BiLSTM. The outputs fromthe bidirectional hidden layers are concatenated to form thefeature representation for the input vectors at each time step.
Subsequently, we compute the average of all token feature
vectors within the span to obtain the feature representationfor each span. The formula is as follows:→←

𝑡𝑜𝑘𝑒𝑛ℎ𝑡 = 𝑡𝑜𝑘𝑒𝑛ℎ𝑡 ⊕ 𝑡𝑜𝑘𝑒𝑛ℎ𝑡(1)

𝑡𝑜𝑘𝑒𝑛𝑖 + ... + 𝑡𝑜𝑘𝑒𝑛 𝑗,𝑗 −𝑖 +1(2)

span𝑖 𝑗 =→←where 𝑡𝑜𝑘𝑒𝑛ℎ𝑡 and 𝑡𝑜𝑘𝑒𝑛ℎ𝑡 represent the feature output of
the forward and backward hidden layers respectively, ⊕denotes two square splice operations on two vector vectors,
𝑡𝑜𝑘𝑒𝑛ℎ𝑡 denotes the hidden layer feature output of the token
at each time step 𝑡, and 𝑠𝑝𝑎𝑛𝑖 𝑗 denotes the span feature
representation obtained after averaging each token feature
vector within the span.
Subsequently, we conduct span-level feature interactions
by employing BiLSTM as the foundational structure. We
then reduce the interacted span features dimension to alignwith the number of argument component types present in
the corresponding argument dataset and proceed to predict
the output. The formula is as follows:𝑀
span𝑖𝐵𝑖𝐿𝑆𝑇= 𝐵𝑖𝐿𝑆𝑇 𝑀 (span𝑖 𝑗 )
𝑗

(3)

𝑀
𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑝𝑟𝑒𝑑 = 𝑠𝑜 𝑓 𝑡𝑚𝑎𝑥 (𝑀𝐿𝑃 (span𝑖𝐵𝑖𝐿𝑆𝑇
)),
(4)
𝑗
𝐵𝑖𝐿𝑆𝑇
𝑀
where span𝑖 𝑗
represents the span feature vector fromthe span-level feature interaction, and 𝑀𝐿𝑃 denotes the function of dimensionality transformation. Finally, we obtain
the probability of the corresponding argument components
𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑝𝑟𝑒𝑑 by means of the softmax function.
4.3

recurrent and convolutional neural networks. We apply thebi-affine structure to graph-based dependency syntactic analysis, which effectively addresses dependency issues between
two nodes. It can predict the presence or absence of edges
between two nodes and determine dependency edge labels,
including the dependency relation and the dependency label. Typically, syntactic dependency parsing occurs within asentence, but in argument text parsing, each argument spanis treated as a node in the graph.
The initial step involves determining the presence of edges
connecting nodes. If we consider an argument text with 𝑛argument component nodes, including a virtual head node,
the total number of nodes is 𝑑 = 𝑛 + 1. For each node, we
generate a score vector denoted as 𝑠𝑖 ∈ R𝑑 ×1 . All argumentcomponent nodes can be organized into a scoring matrixof size R𝑑 ×𝑑 . Then we employ an MLP to reconfigure the𝑀 , which isargument component feature vector span𝑖𝐵𝑖𝐿𝑆𝑇
𝑗obtained in the preceding section. The operation results ina vector ℎ𝑖(𝑠𝑟𝑐 −ℎ𝑒𝑎𝑑 ) ∈ R𝑘 ×1 , where typically, 𝑘 is of a small
magnitude. Subsequently, this vector is inputted into theaffine layer. In this way, we can effectively mitigate the riskof overfitting. The formula is as follows:

Bi-affine Layer

The bi-affine layer employs a bi-affine structure primarily forparsing dependencies within argument information. Dependency syntactic parsing is centered on capturing the syntactic structure of text by examining the relationships among
different components within a linguistic unit, providing amore precise understanding of text segment connections.
Importantly, the bi-affine Layer offers faster training and inference speeds compared to conventional models relying on(𝑠𝑟𝑐 −ℎ𝑒𝑎𝑑 )= 𝑀𝐿𝑃 (𝑠𝑟𝑐 −ℎ𝑒𝑎𝑑 ) (𝑠𝑖 )(5)(𝑡𝑎𝑟 −𝑡𝑎𝑖𝑙 )= 𝑀𝐿𝑃 (𝑡𝑎𝑟 −𝑡𝑎𝑖𝑙 ) (𝑠 𝑗 )

(6)

ℎ𝑖

ℎ𝑗

𝑒𝑑𝑔𝑒

𝑠𝑖 𝑗

(𝑠𝑟𝑐 −ℎ𝑒𝑎𝑑 )= 𝐻𝑖(𝑡𝑎𝑟 −𝑡𝑎𝑖𝑙 )𝑊𝑠 ℎ 𝑗

𝑃𝑖 𝑗 = 𝑓 (𝑠𝑖 𝑗 ),

+ 𝑏𝑠

(7)

(8)

where two 𝑀𝐿𝑃s are dedicated to the source node and thetarget node respectively, 𝐻𝑖(𝑠𝑟𝑐 −ℎ𝑒𝑎𝑑 ) ∈ R𝑑𝑥𝑘 represents amatrix of feature vectors ℎ, while these vectors are obtainedby stacking the features from 𝑑 argument components after
applying quadratic encoding through an 𝑀𝐿𝑃, 𝑊𝑠 is the(𝑒𝑑𝑔𝑒 )
weight matrix, 𝑏𝑠 is the bias vector, 𝑠𝑖 𝑗
is the score of
the dependency edge between argument component 𝑖 andargument component 𝑗. It is necessary to pass the dependentedge score 𝑠𝑖 𝑗 into the nonlinear function 𝑓 to obtain theprobability distribution of dependent edges. It should beclarified that 𝑓 is a 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 function for determining theexistence of dependent edges and 𝑓 is a 𝑠𝑜 𝑓 𝑡𝑚𝑎𝑥 function
for determining dependent labels.
This results in a probability distribution of dependency𝑑edges 𝑃𝑒𝑥𝑖𝑠𝑡𝑠, where 𝑑 represents the number of nodes. Whenassessing dependency labels, the quadratic encoding through
MLP scales down to match the number of relationship types
in the corresponding dataset and the subsequent steps remain consistent with the prior operations to obtain the dis𝑑
tribution of dependency labels 𝑃𝑙𝑎𝑏𝑒𝑙.

4.4

Maximum Tree Layer

The maximal tree graph layer is designed to decode thegraph representing the probability distribution of dependency edges obtained in the preceding subsection. This approach is inspired by the minimum tree graph method, whichaims to identify a spanning tree within a directed weighted
graph while designating a particular node as the root node
to minimize the total sum of edge weights.
In the argument relations identification task, we represent the directionality of the argument relations through a
directed graph. Therefore, each argument component serves
as a node within the directed graph, and the directed edges
between nodes represent argument relations. To keep semantics consistent, each argument node has a single directededge pointing to other argument nodes. However, multiple
argument nodes can point to the same argument node, meaning each node has only one outgoing edge but can receive
multiple incoming edges.

argument relations. These predictions are based on the distri𝑑bution of dependent labels 𝑃𝑙𝑎𝑏𝑒𝑙𝑠, which select the argument
relationship with the highest probability.
4.5

Loss Function

For the four subtasks of argument mining, we use a combination of multiple task loss functions for model parameter
optimization, the loss function is defined as follows:
L = 𝜆𝑠 L𝑠 + 𝜆𝑐 L𝑐 + 𝜆𝑏 L𝑏 + 𝜆𝑟 L𝑟(9)where L𝑖 (𝑖 ∈ 𝑠, 𝑐, 𝑏, 𝑟 ) are the loss functions corresponding to the boundary segmentation, component recognition,
relation identification, and relation classification subtasks,
respectively. 𝜆𝑖 (𝑖 ∈ 𝑠, 𝑐, 𝑏, 𝑟 ) are the hyperparameters corresponding to the loss function for each subtask.

5

Experiment

5.1

Datesets

In order to validate the adequacy of LFAM, we conduct experiments in five benchmark datasets from multi-domain:
AAEC-Essay[27], AAEC-Para[8], AASD[1], MTC[21], CDCP[19]and AbstRCT[13]. The detail of the datasets in App. A andthe detail about the experiment setup in App. B.
5.2

Baselines

To evaluate the performance of LFAM, we compare LFAM
with the following methods:

Figure 4. Maximum tree diagram.
Different from the conventional concept of minimal tree
graphs based on original edge weights, where the smallest
edge weights are selected to minimize usage costs. In ourwork, we extent incorporate the probability distribution of
dependent edges. Consequently, we retain the directed edges
with larger edge weights within the argument graph. As aresult, the thesis text can be transformed into a tree graph.
Fig. 4 shows an example of a directed graph producing amaximal tree graph. The five nodes in the graph and thedark edges between them form a basic maximal tree graph,of which node 𝑉1 is the root node. There is only one pathto the other nodes from 𝑉1 and the sum of the numbers on
all the dark-colored paths is also the maximum that can begenerated for the entire graph.
We apply the Chu–Liu/Edmonds algorithm[28], known
for its time complexity of 𝑂 (𝑉 𝐸) and its status as the fastest
minimal tree graph algorithm available. Utilizing the maximum tree diagram algorithm, we analyze the dependency𝑑edge probability distribution 𝑃𝑒𝑥𝑖𝑠𝑡
to derive the correspond𝑡𝑟𝑒𝑒 . Subsequently, we discard connecting edges thating 𝐺𝑒𝑥𝑖𝑠𝑡
start or end at the virtual root node to obtain the predicted• ILP-Joint[27] is a joint learning model based on integer linear programming.
• BLCC(BiLSTM-CRF-CNN)[8] is an analytic modelof argument structure based on sequence labeling.
• LSTM-ER[8] is an end-to-end model based on tree
LSTM.
• BiPAM-syn[30] is an argument mining model basedon pre-trained language models using syntactic information augmentation.
• Cross-ST[16] is an incremental learning approachbased on argumentation datasets, and for the sake offairness of comparison nature, only the experimental
results of a single dataset are selected.
• Pointer-Net[11] utilizes a pointer network for theprediction of argument components and relationships.
• Span-LSTM[23] is a model that combines textual input sequences and argumentative markers for encoding.
• Bert-Trans[2] is an argument mining model based onneural transitions of parser states.
• TSP[15] is a graph-based non-tree-structured argument parsing and bisimulation of attention struct• Rel.RoBERTa[13] is an argumentation model basedon the RoBERTa parser.
• Rel.SciBERT[13] is an argumentation model basedon the SciBERT parser.

Dataset

AAEC-Essay

AAEC-Para

AASD
MTC
CDCP
AbstRCT

MethodBoundary

BLCC
LSTM-ER
Cross-ST
LFAM
BLCC
LSTM-ERBiPAM-syn
Cross-ST
LFAM
Cross-ST
LFAM
Cross-ST
LFAM
Cross-ST
LFAM
Cross-ST
LFAM

85.21
85.23
85.55
87.10
87.23
87.68
86.70
82.88
83.13
70.29
70.33

Component
Micro Macro

Link

Relation
Micro Macro

63.23
66.21
75.54
75.21
66.69
70.83
73.5
76.48
75.51
69.06
71.51
78.83
78.80
68.90
68.13
64.16
64.40

55.66
55.84
56.38
54.82
59.80
53.43
55.47
31.94
33.16
39.35
39.02

34.82
29.56
55.17
55.36
39.83
45.52
46.4
59.55
55.52
49.83
53.99
45.92
48.66
31.94
33.16
38.38
38.04

66.59
66.07
66.13
58.06
59.44
73.77
74.70
65.78
64.63
45.04
44.90

42.30
44.16
44.57
42.10
46.45
33.07
38.73
16.26
16.87
31.91
33.67

Table 1. Experiment result of subtasks containing boundary delineation on Datasets.

5.2.1 Experimental Results of End-to-end Modeling.
Our experiments are based on the results of ten random
number seeds taking the average, with the best experimental
results bolded. Tab.1 shows a comparison of the performanceof the baseline model and LFAM on the six datasets when
the boundary segmentation subtask is included, as can befound from the experimental results:(a) The LFAM model performs better than the best existingmodel in most of the evaluation measures across five datasets:
AAEC-Essay, AASD, MTC, CDCP, and AbstRCT. Specifically,
we see the biggest improvements in the three evaluation
measures for relationship identification and classification,
which are 4.98%, 4.16%, and 5.66%, respectively. On the AASD
dataset, our LFAM model even outperforms the baselinemodel in all six evaluation measures, demonstrating its wideranging effectiveness.
(b) Despite the challenge of errors transferring between
the four argument mining tasks, where how well you doin one task affects the others, and performance tends todecrease as you go along, we found something interesting.
When the performance of the boundary task and the constituent task are similar, the performance of the LFAM model
does not decrease obviously from recognizing constituents
to recognizing relations compared to other models. This advantage helps it to achieve better performance in predicting
argument relations.
(c) The performance on the AAEC-Para dataset is not ideal,
doing better than models like BiPAM-syn but falling short of
the Cross-ST model. This situation can be attributed to thefact that the AAEC-Para dataset is derived from a segmented
version of the AAEC-Essay dataset, which is typically shorter.
Consequently, the LFAM model’s robust feature retrieval
and extraction capabilities, may not be as effective in thiscondition

DatasetAAEC-Para

AASD

MTC

CDCP

AbstRCT

MethodILP-Joint
Pointer-Net
Span-LSTM
Bert-Trans
Cross-ST w.map
Cross-ST
LFAM
Cross-ST
LFAM
ILP-Joint
Pointer-Net
Span-LSTM
Cross-ST w.map
Cross-ST
LFAM
TSP
Bert-Trans
Cross-ST
LFAM
Rel.RoBERTa
Rel.SciBERTCross-ST
LFAM

Component
Micro Macro

Link

Relation
Micro Macro

88.40
87.44
87.08
77.78
80.71
95.65
89.85
91.22
81.03
80.04
89.37
89.90

58.5
60.8
67.8
70.6
69.33
67.16
68.65
63.96
69.47
48.6
57.7
57.5
65.06
65.06
69.45
34.04
37.3
40.15
41.81
59.65
60.36

68.14
66.29
67.49
58.30
64.12
57.89
55.23
60.69
40.11
41.74
48.72
58.21
57.10
57.73

82.6
84.9
85.7
88.4
86.82
79.55
79.47
65.90
69.04
85.7
81.3
83.5
93.08
83.70
86.07
78.91
82.5
82.34
78.54
67.57
68.32

57.11
55.84
55.63
49.22
53.93
57.34
39.27
46.19
20.39
21.20
17.53
36.76
47.12
49.00

Table 2. Performance comparison of the boundary delineation subtask without boundary delineation on Datasets.

5.2.2 Experimental Results of Non-End-to-end Modeling. In addition, to compare the task performance withother non-end-to-end models, this section uses the golden
result on the first subtask task boundary segmentation bydirectly using the golden result as the boundary segmentation and conducting experiments on the next three subtasks.
Tab. 2 demonstrates the performance comparison between
the baseline model and LFAM on the five datasets withoutboundary segmentation subtasks, as can be found from theexperimental results:(a) The LFAM model performs better than other models
in most metrics on the AASD, MTC, CDCP, and AbstRCT

Dataset
AAEC-Essay
AAEC-Para
AASD
MTC
CDCP
AbstRCT

MethodBoundary

LFAM
LFAM w/o. tree
LFAM
LFAM w/o. tree
LFAM
LFAM w/o. tree
LFAM
LFAM w/o. tree
LFAM
LFAM w/o. tree
LFAM
LFAM w/o. tree

85.23
84.28
85.55
84.76
87.23
87.21
86.70
86.15
83.13
82.71
70.33
68.76

Component
Micro Macro

Link

Relation
Micro Macro

75.21
74.93
75.51
74.78
71.51
72.63
78.80
77.97
68.13
68.31
64.40
62.82

55.84
54.54
56.38
55.49
59.80
54.87
55.47
54.39
33.16
32.18
39.02
35.01

55.36
54.07
55.52
54.80
53.99
50.61
48.66
47.46
33.16
32.04
38.04
33.99

66.07
65.09
66.13
65.01
59.44
59.93
74.70
73.46
64.63
64.90
44.90
43.89

44.16
42.30
44.57
42.15
46.45
44.21
38.73
37.06
16.87
16.33
33.67
30.03

Table 3. Impact of maximum tree layers on LFAM Models.

datasets. Although it falls short of the baseline model in two
metrics within the CDCP dataset’s component recognition,
we see improvements in all six metrics for the AASD, MTC,
and AbstRCT datasets. This shows that the LFAM model isquite effective across a range of situations.
(b)In the AAEC-Para dataset, many researchers simplified the process by combining "Claim: Against" and "Claim:
For" into a single "Claim" label during training and prediction, which we call "w. maps" in this section. Specifically, theCross-ST w. map model performs worse than the Bert-Transmodel in both component macro and link evaluation metrics.
However, when we analyze a more detailed analysis of argument components, the LFAM model performs better than the
Cross-ST model in the macro metrics for relationship identification and relationship recognition but is slightly behindin the other three metrics. Since the LFAM and Bert-Transmodels use different architectures, the former is based ongraph parsing and the latter on parser transformations. It
suggests that combining these approaches in the future forargument mining tasks might lead to improved results inparsing argumentative texts.
(c)In the MTC dataset, we first changed the labels of argument parts to "statement" and "premise" categories, and we
mapped argument connections to "support" and "attack" categories. In this setup, the Cross-ST w. map model performed
the best for these two contrasting categories. But when we
looked at the original labels, the LFAM model performs muchbetter than the Cross-ST model in all aspects, showing that
the LFAM model is effective on the MTC dataset.
To sum it up, the LFAM model performs really well compared to both the end-to-end and non-end-to-end models,
especially in tasks like relationship identification and relationship classification in argument recognition. We further
demonstrate the superiority of the LFAM model in the taskof argument relationship classification in the App. ??.
5.3

Ablation Study

Multi-task learning can be really helpful in handling different argument-related tasks together as seen in previous

studies. In this part, we’re looking at how having maximal
tree layers affects the argument mining task. Tab. 3 shows
the impact of removing these layers on the argument mining
task across five datasets. The results are quite clear: whenit comes to identifying and classifying argument relationships, all three performance metrics drop noticeably, with thelargest decreases being 4.93%, 4.05%, and 3.63%, respectively.
This confirms that having maximal tree layers is crucial forrecognizing argument relationships. For the tasks of boundary segmentation and component recognition, things are a
bit different. Only some metrics in the CDCP dataset show
small improvements, while the other four datasets see a decline in performance. This can to some extent support theperformance assistance of the maximum tree layer for the
first two subtasks, and also demonstrate the advantages ofmulti-task learning.

5.4

Case Study

To further validate the effectiveness of the LFAM model, atypical case from the MTC dataset is selected for analysis in
this section, the detail is in App. C.

6

Conclusion

In this paper, we focus on the argument mining task, especially on argument relation recognition. Aiming at the lackof a complete end-to-end model and the lack of validation ofmodel generalization in multiple domain datasets in existing methods, we propose an end-to-end multi-task learningbased argument relation recognition model LFAM. LFAM
incorporates the semantic information of the pre-trainedmodel well, provides a suitable deconstruction of the four
subtasks of argument mining, and the maximal tree graphprovides good performance of the argument relation identification and argument relation identification classification.
The experimental results show that the LFAM model exhibits
excellent performance and wide application potential on six
argument mining datasets in multiple domains.

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A

Datasets

The statistics of the three datasets are shown in Tab. 4, where
the AAEC-Essay dataset and the AAEC-Para dataset are
denoted together as the AAEC dataset.
Considering the small amount of data in the AASD and
MTC datasets, which are not divided into fixed training andtesting sets, five-fold cross-validation[4] is applied to thetraining data to iterate the model parameters to ensure thereliability of the experiments, and the AAEC, CDCP, and
AbsRCT datasets are processed in the normal training way.

B

Experimental Setup

In the experiments, the model is optimized using the Adam
optimizer, and a linear hot-start mechanism is adopted. The
batch size is set to 4, the dropout is set to 0.1, the hidden
layer dimension is set to 768, the number of training epochs

Dataset
AAEC
AASD
MTC
CDCP
AbstRCT

Argumentative Component/Quantity
Claim:
Claim:
Major
Pre.
For
Against
claim
3932
278
1228
751
Pro.
Ass. Res. Obs. Mea. Des.
110
88
73
11
63
7
Proponent
Opponent
451
125
Policy Value Fact Testimony
Ref.
815
2160 746
1026
32
Major Claim
Claim
Evidence
93
993
2193

Argumentative Relationships / Quantity
Support
3613
Sup.
124
Sup.
263

Att. Det.
0130
Exa. Reb.
9108
Reason
1307
Sup.
Att.
1762
60

Attack
219
Add.
27

Seq.
11
Undercut63
Evidence
46
Partial-Attack
238

Table 4. The label distribution statistics for the datasets.

is set to 30, the validation is performed every 2 epochs,
and the learning rate is set to 1𝑒 − 5. The hyperparameters 𝜆𝑖 (𝑖 ∈ 𝑠, 𝑐, 𝑏, 𝑟 ) of the four sub-task loss functions ofargument mining were selected, and the values of the hyperparameters were varied for each dataset.
we use two evaluation metrics, Macro-F1 and Micro-F1.
Macro-F1 calculates the F1 scores under each category oflabels separately and takes the average value, while Micro-F1
considers all labels uniformly and counts the F1 scores of alllabels directly. The Macro-F1 score is used for all four subtasks, and the Micro-F1 score is additionally used for the two
key subtasks of component identification and relationshipclassification.

C

Case Study

Fig. 5 shows the visualization of the argument structure of
the data under the two models and the real labels. Each ellipse
represents a component of the argument, and the connecting
arrows indicate the relationship between the two components of the argument. It is clear that the Cross-ST model
incorrectly predicts the type of argument component "butgiven the cost of the star player, one would have expected
more" and the type of argument relationship with "the teamended up in third place" as shown by the orange color in
the figure above. The LFAM model corrects for these typesof arguments, as shown in orange in the figure above. The
LFAM model corrects these confusing argument structuresby determining that there should be an undercut relationshipbetween the two argument components that refutes but does
not completely negate the argument and that the argumentcomponent in the lower right corner should belong to the
supporter proponent, as shown in green in the figure below.
Figure 5. Case study.