Hierarchical Enhancement Framework for Aspect-based Argument Mining
Authors: Yujie Fu
Yang Li
Suge Wang
Xiaoli Li
Deyu Li
Jian Liao
JianXing Zheng
Venue: EMNLP 2023 Findings (2023)
Keywords: Aspect-based Argument Mining
Nested Named Entity Recognition
Argument Unit Recognition and Classification
Aspect Term Extraction

Abstract: Aspect-Based Argument Mining (ABAM) is a critical task in computational argumentation. Existing methods have primarily treated ABAM as a nested named entity recognition problem, overlooking the need for tailored strategies to effectively address the specific challenges of ABAM tasks. To this end, we propose a layer-based Hierarchical Enhancement Framework (HEF) for ABAM, and introduce three novel components: the Semantic and Syntactic Fusion (SSF) component, the Batch-level Heterogeneous Graph Attention Network (BHGAT) component, and the Span Mask Interactive Attention (SMIA) component. These components serve the purposes of optimizing underlying representations, detecting argument unit stances, and constraining aspect term recognition boundaries, respectively. By incorporating these components, our framework enables better handling of the challenges and improves the performance and accuracy in argument unit and aspect term recognition. Experiments on multiple datasets and various tasks verify the effectiveness of the proposed framework and components.

Hierarchical Enhancement Framework for Aspect-based Argument Mining
Yujie Fu', Yang Li², Suge Wang' Xiaoli Li', Deyu , Liao', JianXing Zheng'
School of Computer and Information Technology, Shanxi University, China
School of Finance, Shanxi University of Finance and Economics, China
Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, China Institute for Infocomm Research, A SSTR,, Singapore
Abstract
Topic: Naclosr Energy
Example I: Although thelnitaal rapital mor bett puuclesr plant
Aspect-Based Argument Mining (ABAM) is a critical task in computational argumenta- tion. Existing methods have primarily treated ABAM nested named entity recognition task, overlooking the need for tailored strate- gies effectively address the specific chal- lenges of ABAM tasks. To this end, we pro- pose a layer-based Hierarchical Enhancement Framework (HEF) for ABAM, and introduce three novel components: the Semantic and Syntactic Fusion (SSF) component, the Batch- level Heterogeneous Graph Attention Network (BHGAT) component, and the Span Mask In- leractive Attention (SMIA) component. These components serve the purposes of optimiz- ing underlying representations, detecting ar- gument unit stances, and constraining aspect term recognition boundaries, respectively. By incorporating these components, our frame- work enables better handling of the challenges and improves the performance and accuracy in argument unit and aspect term recognition. Experiments multiple datasets and various tasks verify the effectiveness of the proposed framework and components.
Sibioll the pnazntenanc o sunning costs ore relatively low.
Pxample Granted,
ponstraction costs aftic hear nt ongoing juaitlenasoe
proven
than that otherfenegge sources
Topic: School Unileems
Pxample While may help (hnilytngl can cause bullying (ludesis) from fther schools)
Example Studeals not weating the lates (astiousimes feel Leadequale threnfarents afford purchsee some Students may become farpets (bulging for
Figure I: Example annotation of the argument units, the corresponding stances (yellow: supporting/pro: blue: opposing/con) and the aspect term (italics framed in red) for the topics nuclear energy and school uni- forms.
Previous works have attempted to combine pects and arguments, but they often lack clear definition of the relevant task. For instance, Fu- jii and Ishikawa (2006) primarily focuses on sum- marizing viewpoints and defining auguring points. Similarly, Misra et al. (2015) further groups the arguments under discussion into aspects of the ar- gument. Furthermore, Gemechu and Reed (2019) considers aspects in argument relations as part of four functional components. Only recently, study by Trautmann (2020) specifically addresses aspect term extraction and emphasizes the definition, in- troducing the concept of Aspect-Based Argument Mining (ABAM). The main objective of ABAM is to identify the argument units that support corre- sponding stances under a controversial topic, along with the aspect terms mentioned within these ar- gument units. In this context, an argument unit is typically defined as a short text span, providing evidence reasoning about the topic, supporting or opposing (Stab et al., 2018). On the other hand, an aspect term defined as the crucial facet/part the argument unit trying to address, representing a core aspect of the argument (Trautmann, 2020).
1 Introduction
Argument mining, critical task within computa- tional argumentation, has gained considerable at- tention, evident from available datasets (Stab et al., 2018; Trautmann et al., 2020), emerging tasks (Wachsmuth et al., 2017; Al-Khatib et al., 2020), and machine learning models associated to this do- main (Kuribayashi et al., 2019; Chakrabarty et al., 2019). As aspect-level sentiment analysis tasks have flourished, Aspect-Based Argument Mining (ABAM) has recognized the need to decompose argument units into smaller attributes and define aspect terms as components with specific meanings in arguments (Trautmann, 2020).
*Conresponding author. Email: wsg @ sxu. edw.ed..ccn 'Codes available at https//gitnb.com/xxxt/j//BBBB

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Figure illustrates four examples within the topic of nuclear energy and school uniforms. For the first set of examples (example and example 2), argument unit (yellow or blue) opinion are ex- pressed around aspect terms (italics framed in red), such as cost, nuclear plant, and maintenance. Sim- ilarly. the second set of examples (example 3 and example 4) revolves around several aspect terms such as students, and bullying. This targeted ap- proach enables a more precise analysis by zooming on specific aspects that are essential to the ar- gument unit. Moreover, these aspect terms enable the comparison of support or opposing opinions at the aspect level, thereby facilitating the acquisition of more nuanced and fine- fine-grained conclusions.
enhancement module, we leverage the argument unit boundary information provided by the basic module. By integrating a span-based method and utilizing the proposed Batch-Level Heterogeneous Graph Attention Network (BHGAT) component, we are able to judge the stance of the argument unit, thereby refining the calegorization of the ini- tially recognized argument units. Moving on to the aspect term enhancement module, introduce the Span Mask Interactive Attention (SMIA) compo- nent. By incorporating span masks and interactive guidance through attention mechanisms, we can better capture and identify aspect terms within the specified boundaries. Finally, in the decision mod- ule, we combine the initial recognition results with the enhancement results to produce the final output. Our contribution can be summarized follows:
ABAM, as defined by Trautmann (2020), is treated as a Nested Named Entity Recognition (NNER) task which presents three key challenges:
We propose a general SSF component to en- hancing underlying representations, which can simultaneously capture both semantic and syntactic information.
1) How to construct robust underlying repre- sentation effectively encode contextual informa- tion? In the realm of Natural Language Process- ing (NLP), the significance representation serves as a cornerstone for achiev- ing excellent model performance. 2) How to mine the correlation between opinion expressions corre- sponding to different stances under the same topic? Since different users may give different expres- sions of viewpoints on same stances within given topic. As shown in Figure 1, authors express dif- ferent opinions around similar aspect terms under the same topic. Exploring the relationship between these opinion expressions can greatly assist de- termining the corresponding stances of different argument units accurately. 3) How to leverage task-specific features improve the extraction of argument units and aspect terms? By investigating the unique task characteristics and data properties of the ABAM task, we aim to enhance the model's s performance significantly.
robust underlying
We propose a novel framework BHGAT, which can seamlessly integrates the strengths of two distinct types of Graph Neural Net- works (GNNs) within batch-level setting.
We propose task-special SMIA component, which used to effectively constrain the recog- nition range of aspect terms.
Experiments on multiple datasets and various tasks verify the effectiveness of the proposed framework and components.
Related Work
The objective of Misra et al. (2015) is to identify specific arguments and counter-argument in social media texts, categorize them into different aspects, and utilize this aspect information to generate argu- ment summaries. Similarly, Misra et al. (2016) focus on inducing and identifying argument as- pects across multiple conversations, ranking the extracted arguments based on their similarity, and generating corresponding summaries. However, these earlier works have been limited to a few spe- cific topics. In recent research, the approach has been extended to cover broader range of 28 top- ics, introducing a novel corpus for aspect-based argument clustering (Reimers al., 2019). Fur- thermore, Gemechu and Reed (2019) decompose propositions into four functional components: as- pects, target concepts, and opinions on aspects and target concepts. By leveraging the relationships
Overall, we propose a novel Hierarchical Enhancement Framework (HEF), consisting of four modules: basic module, argument unit en- hancement module, aspect term enhancement mod- ule, and decision module. With regard to the three challenges above, the paper presents three key com- ponents accordingly. In the basic module, we pro- pose the Semantic and Syntactic Fusion (SSF) com- ponent to fine-tune the representation of the pre- trained language model. This fine-tuning helps us complete the initial recognition stage of argument units and aspect terms. Next, in the argument unit

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among these components, they infer argument re- lations and gain a deeper understanding of the ar- gument structure. In a different study, Bar-Haim et al. (2020) focus on summarizing the arguments, supporting each side of a debate, mapping them to a concise list of key points, which are similar to the aspect terms highlighted earlier. Lastly, Trautmann (2020) redefines the aspect-based argument mining task based clause-level argument unit recogni- tion and classification in heterogeneous document collections (Trautmann et al., 2020).
where [;] denotes concatenation, and æ the part- of-speech tagging of w
Encoder. The LSTM widely used for captur- ing sequential information in either the forward or backward direction. However, it faces challenges when dealing with excessively long sentences, as it may struggle to retain long-distance dependen- cies between words. To address this limitation and exploit syntactic information, propose the Se- mantic and Syntactic Fusion (SSF) component by sentence-level GNNs, aiming bridge the gap be- tween distant words by effectively encoding both sequential semantic information and spatial syntac- tic information.
3 Framework
This paper proposes HEF for aspect-based argu- ment mining task, which consists of four modules: basic module, argument unit enhancement mod- ule, aspect term enhancement module and decision module. The architecture of the HEF is visually depicted in figure 2.
The input of SSF: previous cell state Ct-1, previ- ous hidden state ht-1. current cell input 2t, and an additional graph-encoded representation Ir, where Co and ho are initialized to zero vectors, Di is graph-encoded representation generated using Graph Attention Network (GAT), which are capa- ble of bringing in structured information through graph structure (Hamilton et al., 2017).
3.1 Task Definition
Following Trautmann (2020), we formulate the ABAM task nested named entity recog- nition task with two-level nested structure. Given an input sentence of n tokens W lufeat, w22 waret. the topic is Wroppic jun forac ...., Wm w.. topic, the target argument unit (AU) label sequence is YAU
The hidden state h of SSF are computed as fol- lows:
f: = (Wo(W(m UWh 1 1 b())
(4)
[u .22 ,..,, yn and the target aspect term (AT) label sequence is AAT = [MITT AT ..... y,"",, where YoU E (Boon lcon Econ, Bpro Apro Epro O) and AT E (Baep. Tasp: Easp. O).
1 = o(W + /1 + b(0)
(5)
0: a(Wlo)m + U10) ((0))
(6)
à tanh(W + U1 (l0))
(7)
4 = fi x + &
(8)
3.2 Basic Module
m. (W 2i + ((())) ho @m)) ( (m))
(9)
The sentence and topic are concatenated as the input to BERT: [CLS), wefart weart teat [SEP], m
tanh(W (6 h1 2(0) ( b00)
(10)
lopic Jopie [SEP], where [CLS] and
[SEP] are special tokens. The contextualized repre- sentations each token X = [x. Nof,,0r 20 be given as:
h4 = = otttn((((( + my * tanh(s:)
(11)
w can
where, ft. it, Or. are equivalent to the forget gate, input gate, output gate, and cell unit in the traditional LSTM respectively, m, and st are used (1) Finally he to control the information flow of gr is the output of SSF.
(1) Note we also incorporate orthographic and mor- phological features of words by combining charac- ter representations (Xu et al., 2021). The characters representation with in wheat as witar, Then we use LSTM to learn the final hidden state of as the char- acter representation fext,
BERT (
Star- Transforrfrr (Guo et al., 2019) can mea- sure the position information more explicitly and is more sensitive to the order of the input sequence. Building upon this insight, SSF component as the input of Star-Transformer re-encode the context to complete the encoder part.
use the output of
5 LSTM wi char)
(2)
The final token representation obtained as fol- lows:
21 = [-:"; m
(3)
h star Star- Transffroooom(hh)
(12)

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Argussd Cak
Asped Tark
PRO
Derbvn
Span besed AJ
KT
1044
AUTRP
AT DRP
ALI
BHGAT
SMIA
dertng
Crergy
Greph Ataetin Newekk
Charaste Regosettaiiss
Lond
Lane'
Done
Lane
[382]
Figure 2: The overall architecture of HEF for ABAM.
Decoder. CRF has been widely used in NER task (Xu al., 2021; Li et al., 2021). For input sentence, the probability scores 2/ AU and AT' for all tokens 2; X over the argument unit tags and aspect term tags are calculated by CRF decoder:
the strengths of sentence-level GNNs (Zhang et al., 2019; Wang et al., 2020) and corpus-level GNNs (Wang al., 2019; Yao et al., 2019; Linmei et al., 2019). While utilizing the deep contextual word representations generated by pre-trained language models, BHGAT facilitates communication of opin- ion expressions among different samples and es- tablishes correlations between different represen- tations of the same word. This enables the opti- mization of the representations of various hetero- geneous nodes within the graph. Constructing a Graph Neural Network involves defining an initial representation for each node, an adjacency matrix. and a node update method.
A" plve AUl [n,"" CRF(WAU hatar + b
(13)
n" p(y/ "" [n("")) > CRF(W he (TT (14)
3.3 Argument Unit Enhancement Module
Motivated by ppse- methods, we utilize ar- gument unit labels zi that predicted by the basic module to obtain boundary information of argu- ment unit spans, and re-evaluating the stance of each span, thereby correcting the 2/1U/ labels, which is argument unit enhancement module (AUE). We observe that different users often express similar opinions, when discussing similar aspect terms. Learning these similar opinion expressions can as- sist distinguishing the corresponding stances of argument units. Furthermore, pre-trained language models are widely adopted due to their ability to generate robust contextual representations. How- ever, different contexts can yield different represen- tations for the same word. Exploring the correla- tion between different context representations of the same word can aid in optimizing the underly- ing representations. To this end, we propose the Batch-level Heterogeneous Graph Attention Net- work (BHGAT) component. BHGAT combines
Node initialization. In our proposed BHGAT, we distinguish between two types of nodes: argu- ment unit nodes At" and word nodes hster, The specific operation is as follows:
A.Th = [Iiktrrs star atopicic))
(15)
where h hitari; and hater, are the starting and ending word representation of i-th argument unit au,, and au:(topic) is the topic of au,
(G [ .... hu h hetar
h h
(16)
where HG(0) is initial representation of nodes in BHGAT, naa is the number of argument units, and Thy the number of words.
Adjacency matrix. The adjacency matrix A of BHGAT includes 4 parts: [au, aul part, [au, word]

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part, [word, and part, [word, word] part.
Finally, we perform stance classification on the representations of argument unit nodes. The prob- ability Pou, of different stance belonging to au; is calculated as follows:
[ax, au] [wordd, au)
(au, word] [word, word]
A
(17)
Pau: s0 =oftmaz(W twaa(whaeeeeeer hg) +b"*), (I.... nan) (24)
The (aus, auy) captures the relationships be- tween argument units within a batch, facilitating communication and understanding among argu- ment units that share the same topic, and allowing to learn similar opinion expressions:
where Pau; Iaan Pasc: Pau, is the stance prob- ability distribution of the argument unit au,
Through BHGAT, we first obtain the stance classes of the corresponding argument units. Then, we map the obtained stance classes to the enhanced argument unit recognition space, resulting in the vector 2 zAA' =AU can be divided into two parts: the boundary part and the stance part. In the map- ping process, first, according the boundary of the argument unit label 2, 2/U it is judged whether the boundary of 2t ZAUE corresponding to the token, B-* I-*, E or O. Then according to p pro =AU (*-con, Por we determine the stance part in 27 pro, O) as follows:
1, if auu(topic) = atyppopic) otherwise
(aus, auy) -00,
(18)
The (aus, wordj) represents the association be- tween argument units and words, using the atten- tion mechanism between nodes to complete the update of argument unit nodes and word nodes, which is:
(aus, words) = {
1, if wordj in aus
(19)
0, otherwise
Iaan O, O. pan, O, Pan,]
t= Baus
Similarly, the (word;, auz) denoted as:
21
AUE
[0, Pany O, O paug O Pau.)
8an, < t Cans
[0, 0,ay 0, 0, p.pae Pour).
== Cun, otherwise
(wordg, auj) = [1 1, if word in auj
00.0,0.0.00011,,
(20)
(25)
0, otherwise
where Sous is the start position of argument unit au, and Can, is the end position.
The (word,, word,) focuses on node represen- tations of the same word in different contexts. This part facilitates the information interaction word nodes between different argument units, which is conducive to optimize the dynamic representations of the underlying words.
3,4 Aspect Term Enhancement Module
To enhance the label sequence results 2/" of ini- tially identified aspect terms, we introduce the As- pect Term Enhancement (ATE) module. Since an aspect term is specific to a corresponding argument unit, it is essential to establish a component that constrains the recognition range of aspect terms within the text. Building upon this concept, we pro- pose the Span Mask Interactive Attention (SMIA) component, which ensures that the attention mech- anism focuses on the relevant argument unit spans while effectively disregarding irrelevant text. The overall process can be formulated as:
(word,, word,) = {1.
iʃ word, = word, in different AU
(0 otherwise
(21)
Furthermore, the diagonal values of A are all
Node update. We adopt the method of informa- tion aggregation complete node updates, similar to GAT (Veličković al., 2018). The specific oper- ation as follows:
mask
x H w
(26)
cap(LeakyReLU((r [Whg (hgg)))
asy ΣLeN exp(LeukyReLU((= hg. [hog]))
(22)
Att(Q, K.V) Poottmax(MAS)) + QK
Vdx ))
).V
(27)
hg:
(1+1) = 0 K
Σ Σ a w
(23)
-00, Di
mask:
(28)
k=
0,
AU V
40
where hg, the representation of nodes for /-th layer.
Once we obtain the new context representation, we proceed to feed it into the decoder model, which

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is responsible for generating the aspect term en- hanced label sequence 2 ATE
4 Experiments
To evaluate the effectiveness of HEF framework and the corresponding components, we conducted experiments on four datascts.
( CRF(W ATT maak k DATB) ATE ply?" AT
21
(29)
4.1 Datasets
3.5 Decision Module
ABAM We employ the latest ABAM dataset, which was released in 2020 and comprises topics (Trautmann, 2020). The statistics are presented in the table 1. We followed the inner dataset split (2268 / 307 636 for train dev test) defined the ABAM corpus (Trautmann, 2020).
Through the AUE and ATE module, can ob- tain the enhanced argument unit label probability =t AUE and the enhanced aspect term label proba- bility 2 ZATE, Finally, we fuse the probabilities in the two label spaces (initial, enhanced) as the final
output.
21
ATE AT
2t.
(30)
Table 1: The proportion of ABAM.
Topic
Psentlence
Aorgnantt
Areperttonipa
AAU AUE
2t
+ AU
(31)
cloning
343
415
4:5
910
813
192
4B44
murijunna legulization
626
676
1889
8877
wags
624
745
3.6 Objective Function
nuclear energy
615
671
1992
980
death penalty
588
637
1325
515
The first part aims to minimize two negative log- probability of the correct sequence of labels ba- sic module.
gun cuninel
480
479
school unitorms
705
300
2019
923
120.00
4525
AURC-8 The argument unit recognition and classification (AURC) dataset published in 2020, consists of 8 topics (Trautmann et al., 2020). The statistics shown in the table 2. We used the inner dataset split (4000 / 800 / 2000 for train / dev / test) given by Trautmann al. (2020).
LNFR Σ (u(( A2 12AT)
loy(p(s( 40 (A)"))
(32)
where =/A and 2AA represent the predicted se- quence, AAU and ya represent the correct se- quence.
The second part loss is the cross-entropy loss for stance classification argument unit span in AUE module, denoted as:
Table 2: The proportion of AURC-8.
topic
#number
#arg-Sent
#uon-arg
Parg-unit
abortion
1000
424
576
458
cloning
1000
LAUE Σ>
stancej yaus log(Pau;
stancej
(33)
marijunna legalization
minimum wage
1000
1000
630
630
370
370
703
689
nuclear euergy
1000
623
377
684
i=1
death penalty
1000
598
402
651
where n the number of argument units, and m the number of stance classes.
gun control
1000
school uniforms
1000
287
821
total
8000
4500
3500
4973
Similar to the first part, the third part uses the
negative log-likelihood loss in ATE module.
2emEval-2016 Task 6A The dataset has been divided into training and test set for each of the five claims. Each sample can be classified three categories: against, none and favor.
LATE- Σ
log(p(y< (TE)
(34)
ABAM argument segment The dataset is a collection of argument units in ABAM. Each ar- gument unit can be classified into two categories: PRO and CON.
Finally, the fourth part also aim to minimize neg- ative log- likelihood for enhanced label probability distribution.
Table 3 shows the distribution of SemEval-2016 Task 6A and ABAM argument segment.
Lg Σ Jog(rlsh (T (Td"))
Σ
log(plys (
(35)
https://gituu..com//tt cow/trtm/ABAM
The final loss function is defined as follows:
hhtps://gittub.com/tttt womm/rtt//AUKt
"htpss://ithub..omm/// http://twww..oo//oooyy//ttttaaa
L = LNER + LAUE + LATE + LE (36)

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Table 3: The proportion of SemEval-2016 Task 6A and
Span, BPE, Word (Yan al., 2021) present new formulation of the NER task as an entity- span sequence generation problem.
ABAM argument segment.
Train
Dev
Tost
total
SemFval-2016 Task 6A
2914
1249
4163
ABAM argument segment
2447
333
693
3473
4.3
Experimental results and analysis
4.3.1
Comparison results of different methods for ABAM
4.2 The experimental setup
We show the performance comparison of differ- ent methods in table 4. All comparison methods use the code provided by their respective original papers, and undergo testing and evaluation the ABAM dataset.
Evaluation Metries: For different tasks, we pro- vide specific evaluation metrics to assess the perfor- mance. For aspect-based argument mining task, we conduct model evaluation using the ABAM dataset at the segment-level and token-level. In segment-level evaluation, we consider prediction as correct only the model correctly identifies the boundary and category of an entity. We use ex- act matching F1 to measure the accuracy. At the token-level, we proposed two evaluation methods: Tesend evaluation and Token-Flat evalua- tion. In the Token-Nested evaluation, extend the stance labels by incorporating aspect informa- tion, resulting in six possible combinations: INON, OJ, INON, ASPJ, [PRO, OJ, [PRO, ASP), (CON, O}, and [CON, ASPJ. We report both Macro-Fl and Micro-F1 scores for this evaluation. In Token- Flat evaluation, we concatenate the sequence la- bels of aspect term recognition and argument unit recognition to generate label sequence twice the sentence length, containing four label categories: [ASP, PRO, CON, Oj. We report both Macro-F1 and Micro-Fi scores for this evaluation. For the ar- gument unit recognition and classification task, we employ the segment-level evaluation metric on the AURC-8 dataset. The Macro-F1, Micro-Fl and separate Fl scores for each category are provided. Finally, for the stance detection task, we report the Macro-F1 score, Micro-Fl score, and Fl scores the SemExal-2016 Task 6A and ABAM argu- ment segment datasets.
Based on the results presented in Table 4, our proposed method demonstrates superior perfor- mance compared to all the existing methods, both in segment-level and token-level evaluation met- rics. This improvement can be attributed to the inclusion of two key modules: Argument Unit En- hancement (AUE) and Aspect Term Enhancement (ATE). Specificaly, our method shows substantial improvements in MicF1, MacF1, Token- Flat, and Token-Nested evaluation metrics, with gains of at least 0.0767, 0. 1274, 0.0647, and 0.0745, respec- tively, compared to the other comparison methods. The ATE module effectively constrains the recogni- tion range of aspect terms, leading significant improvement in the F1 score for aspect term recog- nition (ASP column).
4.3.2 Ablation experiments for ABAM
To evaluate the individual impact of each functional module or component within the HEF framework on model performance, conduct a series of abla- tion experiments. The results of these experiments are presented in Table 5. -w/o topic, -w/o SSF, -woo Star-Transfin Transformer, -w/o AUE and -w/o ATE represents our model after removing the topic in BERT, SSF, Star-Transforme AUE module, and ATE module, respectively.
Compared Models: We compare the HEF framework with the following state-of-the-art meth- ods:
The experimental results in the table above clearly demonstrate that the removal of different modules or components has a significant impact on the performance of the HEF framework. In particular, the absence of the AUE module has a substantial negative effect on overall performance. The utilization of BHGAT to re-judge the category of argument unit spans has proven be an effec- tive strategy for correcting samples in the basic module where the boundary is correctly identified but the category judgment incorrect. Moreover, the inclusion of the topic information in BERT
CNN-NER (Yan al., 2022) utilizes con- volutional neural network capture the inter- actions among neighboring entity spans.
N (Li et al., 2022) introduces a novel approach to tackle named NER by framing it as word-word relation classification prob- lem.

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Table 4: Performance comparison of different methods in ABAM.
Segment Level
Token Level
ASP
CON
PRO
MicFI
MacFl
Token-Flat
Token-Nested
CNN-NER et 2022)
0.7480
0.4535
0.4491
0.6707
0,5502
W'NER(Li 2022)
0.7495
0.4618
0.4495
0.6727
0.7587
0.5347
Yon 2021)
0.7191
0.4423
0,4650
0.6474
0.5421
0.7656
0.5438
Spant Yan et al., 2021)
0,7175
0.4657
0.4702
0.6516
0.5511
0,7680
0,5459
BPE(Yan et al,, 2021)
0,7090
0.3695
0.3560
0.6265
0,4782
0.6676
0,4344
0.7969
0.6283
0.7494
0.8327
Table 5: Performance comparison results without different module or components.
Segment-Level
Token-Level
ASP
CON
PRO
MicFl
MacF1
Token-Flat
Tokcn-Nested
HEF
0.7969
0,6283
0.6176
0.7494
0.6810
0.8327
0.6204
-wo topic
0.7921
0.6073
0.6083
0.7416
0.6693
0.8238
0.6096
-Wo SSF
0.7911
0.4460
0.4646
0.6963
0.5672
0.8008
0.5735
wwo Star- Transformer
0.7851
0.4715
0.4823
0.6980
0.5797
0.7845
0.5574
-wf AUE
0.7367
0.4276
0.4249
0.6525
0.5298
0,7716
0.5568
-wfo ATE
0.7399
0.4620
0.4843
0.6667
0.5621
0,7700
0,5462
contributes significantly to the framework's per- formance. The SSF and Ttan-frranfrrmmrr compo- nents also play crucial roles in generating high- quality underlying representations. The absence of these components has detrimental impact on the model's performance. Lastly, due aspect terms only exist within the argument unit spans. The ad- dition of the ATE module improves the accuracy of aspect term range extraction.
gument segment datasets. In our experiments, we incorporate the BHGAT component into the BERT- based framework and compare the experimental results, as shown in Table 7.
Table clearly demonstrate that the inclusion of the BHGAT component has resulted in significant performance improvements. This improvement can be attributed to several key factors. Firstly, the BH- GAT component has the ability capture and lever- age information from multiple samples that share the same topic. By considering the expressions of different stances and the embedding representa- tions of words different contexts, the BHGAT component enhances the model's discriminative power and facilitates more accurate stance detec- tion. Furthermore, the versatility of the BHGAT component is noteworthy. It can be seamlessly in- tegrated into various frameworks, enabling perfor- mance enhancements across different models. This flexibility makes the BHGAT component highly adaptable, particularly in classification tasks that involve topic information, such as stance detection.
4.3.3 Effectiveness of SSF component
To evaluate the effectiveness of SSF component, we conducted experiments on two sequence labeling datasets, AURC-8 and ABAM. For the sequence labeling task, we use LSTM and SSF the encoder and CRF as the decoder. The experimental results are presented in Table 6.
Based on the experimental results in Table 6, we observe the scores on the ABAM dataset are signif- icantly higher compared to those in the AURC-8 dataset. This discrepancy can be attributed to the in- herent dissimilarities between the two datasets. In the AURC-8 dataset, argument units may not exist in every sample, while the ABAM dataset ensures the presence of at least one argument unit in each sample. By integrating spatial syntactic informa- tion with the LSTM-encoded sequential semantic information, the SSF component demonstrates clear performance advantage. leading to significant improvements both datasets.
4.3.5 Effectiveness of SMIA component
The SMIA component introduced in the ATE mod- ule aims to restrict the range of aspect term recog- nition. To assess its effectiveness, we present the confusion matrix based on the Token-Nested evalu- ation index in figure 3.
In figure 3, the confusion matrix is presented with dimension of 6x6. is important note that in real data, there no combination of the aspect la- bel ASP and the stance label NON, as aspect terms only exist within argument units. As a result, the
4.3.4 Effectiveness of BHGAT component
To comprehensively assess the superiority of this component, we conduct experiments on two datasets: SemEval 2016 Task 6A and ABAM ar-

---

Table 6: Performance comparison results in ABAM and AURC-8.
ABAM
AURC-8
Segment-Level
Token-Level
Segment- Level
ASP
CON
PRO
MieFI
MacFl
Token-Flat
Token-Nested
CON
PRO
MieFl
MacFl
LSTM-CRF
0.7456
0,3890
0.1879
0.6434
0.5075
0.7633
0.5373
0.3264
0,3350
0,3308
0.3307
SSF-CRF
0.7473
0.3995
0.3995
0.6485
0,5154
0.7762
0.5526
0,3329
0.3585
0.3488
0.3457
Table 7: Performance comparison of results in SemEval 2016 task 6A and ABAM argument segment.
SemEval 2016 task
ABAM argument segment
CON
NONE
PRO
MicF1
MacFI
CON
MicFI
MacF1
BERT
0.7551
0,5885
0.6436
0.6942
0.6624
0.7989
0.7881
0.7937
0.7935
BERT- BHGAT
0.7541
0.6071
0.6591
0.6990
0.6734
0.8126
0.7908
0.8023
0.8017
Prddct label
Predict Ioled
0400
0-900
0.004
JMC
X1
80
17
D/NOX
PBL
Predkct labe
1M
Lerdes
6.20
WiNEX
Spen
Piodictlidad
Prodict bibed
JAP
encEs
Jeh
4577500
Figure 3: The confusion matrices for different models
fifth row of the confusion matrix is always zero. In the confusion matrix, we focus on the fifth column of each confusion matrix. The model identifies as- pect terms and argument units separately, during the prediction process, if the prediction range of term is not constrained, it may generate a wrong match between the aspect term label ASP and the stance label NON. However, by observing the fifth column of each confusion matrix, we can observe significant reduction in misjudgment after adding the span mask constraints imposed by the SMIA component. This outcome reinforces the effective- ness of the SMIA component in constraining the recognition of aspect terms.
ing hierarchical enhancement framework consist- ing of four modules: basic module, argument unit enhancement module, aspect term enhancement modulc, and decision module. The SSF component plays a crucial role in optimizing underlying rep- resentations, which can be utilized across various tasks. It enhances the framework's capability by incorporating syntactic information into the encod- ing process, improving performance sequence labeling tasks. The BHGAT component, effective for classification tasks involving topic information, enhances the framework's generalization capabili- tics. The SMIA component is specifically designed for aspect-based argument mining tasks, aiming to constrain the recognition range of aspect terms. It effectively improves the accuracy of aspect term recognition and contributes to the overall perfor- mance of the framework.
5 Conclusion
This paper presents a novel layer-based approach for the aspect-basee argument mining task, utiliz-

---

Limitations
Atsushi Fujii and Tetsuya Ishikawa. 2006. system for summarizing and visualizing arguments in sub- jective documents: Toward supporting decision mak- ing. In Proceedings of the Workshop on Sentiment and Subjectivity Text, pages 15-22.
However, it should be noted that the proposed BH- GAT is currently only suitable for classification tasks with topic information. Its generalization to more general tasks needs further investigation in our future work. In addition, our current frame- work has primarily focused on adopting layer- based method for Nested Named Entity Recogni- tion (NNER), without extensively exploring how to mine the correlation between argument units and aspect terms. In future work, it is essential to delve deeper into the correlation between these two entities and fully utilize the guiding information between them.
Debela Gemechu and Chris Reed. 2019. Decom- positional argument mining: A general purpose approach for argument graph construction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 516-526.
Qipeng Guo, Xipeng Qiu, Pengfei Liu, Yunfan Shao, Xiangyang Xue, and Zheng Zhang. 2019. Star- transformer. In Proceedings of the 2019 Conference of the North American Chapter the Association for Computational Linguistics: Human Language Technologies, pages 1315-1325.
Acknowledgements
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. CoRR, abs/170990555 709.055584
The authors would like to thank all anonymous reviewers for their valuable comments and sugges- tions which have significantly improved the qual- ity and presentation of this paper. The works de- scribed in this paper are supported by the National Key Research and Development Program of China (2022QY0300001 National Natural Science Foun- dation of China 02210130, 620761558 58, 62072294, 62272286), Natural Science Foundation of Shanxi Province, China (2021 00220002110 124044) Scientific and Technological Innovation Programs of Higher Ed- ucation Institutions in Shanxi, China (202)L284), and CCI-Zhipu Al Large Model Foundation of China (CCF-2hipu202310).
Tatsuki Kuribayashi, Hiroki Ouchi, Naoya Inoue, Paul Reisert, Toshinori Miyoshi, Jun Suzuki, and Ken- taro Inui. 2019. An empirical study of span rep- resentations in argumentation structure parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4691-4698.
Fei Li, Zheng Wang. Siu Cheung Hui, Lejian Liao, Dandan Song, Jing Xu, Guoxiu He, and Meihuizi Jia. 2021. Modularized interaction network for named entity recognition.
In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 200-209.
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Reviews:
[{"review_id": "lVwAE2ht4U", "reviewer": "Reviewer_1g4G", "summary": "", "questions": "The same to the above the section \"Reasons To Reject\". ", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "QA1: It is better to choose more baselines from different articles for comparison.\n\n    ABAM, initially introduced by Trautmann at the 2020 Argument Mining workshop of ACL, has yet to yield substantial subsequent research contributions. To address this gap, our present study aims to rectify the situation by leveraging five recent baselines from the years 2021 and 2022. While we recognize the importance of broadening our comparative scope, we share your perspective on the need for a more comprehensive evaluation strategy. In direct response to your query, we have expanded our baseline repertoire with an additional model rooted in contemporary advancements – the ChatGLM-6B. This open-source and lightweight alternative to ChatGPT is an advanced language processing model imbued with extensive capabilities. Noteworthy attributes of ChatGLM-6B include:\n       \n    Has about 6.2 billion parameters.\n    The model is pre-trained on 1 trillion tokens—equally from English and Chinese.\n    Subsequently, techniques such as supervised fine-tuning and reinforcement learning with human feedback are used.\n\n    Next, we outline how ChatGLM-6B can be effectively applied to the ABAM task. We construct a prompt structured as follows: “{“content”: topic is “xxx”, text is “xxxxx”, “summary”: “CON Argument Unit”--->“xxxxx”, “PRO Argument Unit”--->“xxxxx”, aspect term--->“xxxxx”}”. The model undergoes fine-tuning using the ABAM dataset and is subsequently evaluated on the test dataset to yield final experimental results.\n\n    Our findings once again verify the superiority of our proposed HEF framework when compared with the ChatGLM-6B, both for the segment-level and token-level analyses. These outcomes not only reinforce the efficacy of our approach but also underline its significance in the domain. We trust that these results will effectively allay any concerns you might have had.\n\n    Building upon your insightful suggestion, we are dedicated to extending our experimental endeavors. Our forthcoming plans encompass the incorporation of additional comparative methodologies. We are committed to meticulously documenting and incorporating the outcomes of these novel experiments into the final paper, thereby enriching the overall robustness of our research.\n\n                       Table 1 Comparison with large language model\n    --------------------------------------------------------------------------------------\n    |\t   |                  Segment-Level              |          Token-Level      |\n    |          |   ASP   |  CON   |  PRO   |  MicF1 | MacF1  | Token-Flat | Token-Nested |\n    |ChatGLM-6B|  0.7010 | 0.4402 | 0.4311 | 0.6321 | 0.5241 |  0.7522    |    0.5981    |    \n    |HEF(our)  |  0.7969 | 0.6283 | 0.6176 | 0.7494 | 0.6810 |  0.8327    |   0.6204     |\n    --------------------------------------------------------------------------------------\n\nQA2: The paper lacks related work.\n\n    In our initial submission, owing to constraints on available space, we have amalgamated the discussion on Related work with the Introduction section. We will include an independent Related work Session in the final version of our paper to include more relevant research papers. Please refer below for a more comprehensive overview of our Related Work section. We are open to incorporating further references should you have any suggestions on additional pertinent research. Your input is highly appreciated.\n\n    Related Work\n\n    Below, we will present an overview of two relevant areas of research, namely Aspect-based Argument Mining and Named Entity Recognition.\n\n    Aspect-based Argument Mining:\n\n    The objective of Misra et al. [1] is to identify specific arguments and counter-arguments in social media texts,  categorize them into different aspects, and utilize this aspect information to generate argument summaries. Similarly, Misra et al. [2] focus on inducing and identifying argument aspects across multiple conversations, ranking the extracted arguments based on their similarity, and generating corresponding summaries. However, these earlier works have been limited to a few specific topics. In recent research, the approach has been extended to cover a broader range of 28 topics, introducing a novel corpus for aspect-based argument clustering [3]. Furthermore, Gemechu and Reed [4] decompose propositions into four functional components: aspects, target concepts, and opinions on aspects and target concepts. By leveraging the relationships among these components, they infer argument relations and gain a deeper understanding of the argument structure. In a different study, Bar-Haim et al. [5] focus on summarizing the arguments, supporting each side of a debate, mapping them to a concise list of key points, which are similar to the aspect terms highlighted earlier. Lastly, Trautmann [6] redefines the aspect-based argument mining task based on clause-level argument unit recognition and classification in heterogeneous document collections [7].\n\n    Named Entity Recognition:\n\n    The related work on Named Entity Recognition (NER) methods can be broadly classified into two categories: token-based NER and span-based NER.\n\n    Token-based NER is typically framed as a sequence labeling task, where each word is assigned a label indicating its entity type. The widely used approach for token-based NER involves utilizing a bidirectional LSTM as an encoder to learn contextual representations of words, coupled with Conditional Random Fields (CRFs) [8] as a decoder to label the words [9]. Some researchers have also incorporated character-level representations to capture spelling features. These character-level and word-level embeddings are concatenated and fed as input to a BiLSTM with CRF for labeling [10, 11]. More recently, the field of NER has experienced notable progress, with the introduction of powerful pre-trained language models, such as ELMo and BERT [12, 13]. These models have been widely adopted to enhance NER performance.\n\n    Span-based NER approaches, on the other hand, involve enumerating all possible spans in sentences and classifying each span into a specific entity category (e.g., PER, ORG, LOC). For example, Li et al. [14] view NER as a Machine Reading Comprehension (MRC) task, where entities are extracted as answer spans. Yu et al. [15] propose an approach that extracts spans based on start and end positions, and employ biaffine attention to measure the likelihood of a text span being a mention. Furthermore, Shen et al. [16] present a two-stage NER model that generates span proposals first and then classifies them into corresponding entity categories. While span-based models offer a fine-grained approach to NER, they may encounter certain limitations related to the maximum span length and computational complexity inherent to their enumerative nature. Particularly, enumerating all possible spans can lead to a significant increase in the number of candidate spans, making the task computationally expensive.\n\n    [1] Amita Misra, Pranav Anand, Jean E. Fox Tree, and Marilyn Walker. Using summarization to discover argument facets in online idealogical dialog. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 430–440, 2015.\n    [2] Amita Misra, Brian Ecker, and Marilyn Walker. Measuring the similarity of sentential arguments in dialogue. In Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 276–287, 2016.\n    [3] Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, and Iryna Gurevych. Classification and clustering of arguments with contextualized word embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 567–578, 2019.\n    [4] Debela Gemechu and Chris Reed. Decompositional argument mining: A general purpose approach for argument graph construction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 516–526, 2019.\n    [5] Roy Bar-Haim, Lilach Eden, Roni Friedman, Yoav Kantor, Dan Lahav, and Noam Slonim. From arguments to key points: Towards automatic argument summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4029–4039, 2020.\n    [6] Dietrich Trautmann. Aspect-based argument mining. In Proceedings of the 7th Workshop on Argument Mining, pages 41–52, 2020.\n    [7] Dietrich Trautmann, Johannes Daxenberger, Christian Stab, Hinrich Schütze, and Iryna Gurevych. Fine-grained argument unit recognition and classification. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 9048–9056, 2020.\n    [8] John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the Eighteenth International Conference on Machine Learning, page 282–289, 2001.\n    [9] Rrubaa Panchendrarajan and Aravindh Amaresan. Bidirectional LSTM-CRF for named entity recognition. In Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation, pages 531–540, 2018.\n    [10] Fei Li, Zheng Wang, Siu Cheung Hui, Lejian Liao, Dandan Song, Jing Xu, Guoxiu He, and Meihuizi Jia. Modularized interaction network for named entity recognition. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 200–209, 2021.\n    [11] Ying Luo, Fengshun Xiao, and Hai Zhao. Hierarchical contextualized representation for named entity recognition. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, pages 8441–8448, 2020.\n    [12] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, 2019.\n    [13] Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2227–2237, 2018.\n    [14] Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, and Jiwei Li. A unified MRC framework for named entity recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5849–5859, 2020.\n    [15] Juntao Yu, Bernd Bohnet, and Massimo Poesio. Named entity recognition as dependency parsing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6470–6476, 2020.\n    [16] Yongliang Shen, Xinyin Ma, Zeqi Tan, Shuai Zhang, Wen Wang, and Weiming Lu. Locate and label: A two-stage identifier for nested named entity recognition. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 2782–2794, 2021.\n\n\nQA3: Experiments on the effectiveness of SMIA component could be added to make the experiments more comprehensive.\n\n    We fully agree with you. Following your suggestion, we have demonstrated the Superiority of SMIA in Label Combination through Confusion Matrix Analysis. In particular, to validate the efficacy of the SMIA component in terms of label combination, we have conducted a comprehensive analysis of the confusion matrix. We intend to include the following experimental aspect in our paper.\n\n    Evaluating the Efficacy of SMIA in the ATE Module:\n\n    Our analysis involves a 6x6 dimensional confusion matrix that encompasses the following label combinations: [NON, O], [NON, ASP], [PRO, O], [PRO, ASP], [CON, O], and [CON, ASP]. It's worth noting that, in real-world data, the combination of the aspect label ASP and the stance label NON is non-existent, as aspect terms exclusively manifest within argument units. Consequently, the fifth row of the confusion matrix consistently remains at zero.\n\n    Our particular focus lies in the fifth column of each confusion matrix. The model distinguishes aspect terms and argument units as separate entities during the prediction process. Without constraints on the prediction range of terms, there is a potential for incorrect matches to emerge between the aspect term label ASP and the stance label NON. However, upon scrutinizing the fifth column of each confusion matrix, a noticeable reduction in misjudgments becomes evident subsequent to the implementation of span mask constraints imposed by the SMIA component.\n\n    This empirical evidence strongly bolsters the notion of the SMIA component's effectiveness in precisely constraining the recognition of aspect terms, reaffirming its significant role in refining the ATE module.\n\n\n\nQA4: Compared with LSTM-CRF, although the performance of SF-CRF is slightly better, will the computational complexity increase dramatically?\n\n\n    The SSF component serves the purpose of refining BERT representations to align with downstream tasks, thereby establishing a robust foundational representation that enhances overall model performance. Our aim is to elucidate the temporal aspects associated with training LSTM-CRF and SF-CRF over the span of one epoch using two distinct datasets. \n\n    You are absolutely right that the incorporation of SSF introduces heightened computational complexity, attributed to the inclusion of graph attention neural networks and supplementary gating mechanisms. Consequently, this addition leads to an increase in time-related costs, as shown in Table 2. \n\n    However, within the context of ABAM tasks, the trade-off of investing additional time is justified by the resultant enhancement in model performance. The heightened computational demands brought about by SSF align with the objective of refining the model's capabilities in addressing the intricacies of ABAM tasks, substantiating the rationale for embracing this time-cost exchange. In addition, as computational power increases, the impact of extended processing times becomes less significant.\n\n\n       Table 2 Time cost for LSTM-CRF and SSF-CRF to train an epoch\n                -------------------------------------------\n                |Model         |  Dataset |   Time/epoch  |\n                |LSTM-CRF      |   AURC   |    53.631s    |\n                |              |   ABAM   |    54.692s    |\n                |SSF-CRF       |   AURC   |    119.061s   |\n                |              |   ABAM   |    127.293s   |\n                -------------------------------------------", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The idea of hierarchical enhancement framework for aspect-based argument mining is innovative and intuitive.\n2. This paper clearly and logically describes its contribution to hierarchical enhancement framework for aspect-based argument mining. From the extensive and comprehensive experimental results of experiments in this paper, the proposed framework demonstrates significant performance improvements over existing approaches..\n3. The framework studied in this paper is interesting and well motivated and the paper is well written.\n4. Experiments are tested on real datasets.", "reasons_to_reject": "1. It is better to choose more baselines from different articles for comparison.\n2. The paper lacks related work.\n3. Experiments on the effectiveness of SMIA component could be added to make the experiments more comprehensive.\n4. Compared with LSTM-CRF, although the performance of SF-CRF is slightly better, will the computational complexity increase dramatically?", "questions_to_authors": "The same to the above the section \"Reasons To Reject\". ", "paper_topic_and_main_contributions": "1. This paper proposes a general SSF component to enhancing underlying representations, which can simultaneously capture both semantic and syntactic information.\n2. This paper proposes a novel framework BHGAT, which can seamlessly integrates the strengths of two distinct types of Graph Neural Networks (GNNs) within a batch-level setting.\n3. This paper proposes a task-special SMIA component, which used to effectively constrain the recognition range of aspect terms.\n4. Extensive experiments on multiple datasets and various tasks verify the effectiveness of the proposed framework and components.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "1dijR01Az6", "reviewer": "Reviewer_3LjC", "summary": "", "questions": "Question A: In Basic Module, obtaining the two representations uses BERT and LSTM, respectively. Is there anything special about this?\nQuestion B: Compared to the other methods, what are HEF's strengths in addressing the three key challenges in ABAM?\nQuestion C: In the experiment, how does HEF perform when all the topics are mixed together?\n", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "QA1: The Semantic and Syntactic Fusion (SSF) component aiming to bridge the gap between distant words is not novel. The topic of long-distance dependencies between words has been studied in previous works [1][2]. It’s not clear how the proposed method advances from them.\n\n    Yes, you are right that the main contribution of this article is the BHGAT component, followed by SMIA. BHGAT is a new component that can communicate the expression of opinions between different argument units around aspect terms under the same topic in the same batch. SMIA is an ABAM task-specific component that effectively constrains the identification scope of aspect terms. Drawing on other work, SSF generates components for just one presentation that can be used to fine-tune downstream tasks with BERT. The corresponding reference will be added later. And it will be removed in the contribution part of introduction and not put forward as an innovative point.\n    \n    Indeed, you are correct in identifying the primary contribution of this article, which lies prominently within the BHGAT component, followed by the SMIA module. The BHGAT introduces a novel capability to facilitate the exchange of opinion expressions across different argument units centered around aspect terms within the same topic and batch. Meanwhile, the SMIA component caters specifically to ABAM tasks, skillfully limiting the scope for aspect term identification.\n    \n    Expanding on this, the SSF component, derived from prior work, generates elements optimized for fine-tuning BERT with respect to individual presentations. Although a corresponding reference will be incorporated later, it's important to highlight that this aspect will not be presented as an innovative point in the contribution section of the introduction, and will subsequently be removed from there. In our revised paper, we will highlight the other two main contributions, namely BHGAT component and SMIA component, ensuring the clarity and accuracy of the article's contributions.\n\nQA2: The overall framework figure is not clear. This figure does not illustrate The key component modules (Figure 2).\n\n    Figure 2 illustrates our endeavor to visually depict the three pivotal component modules, each occupying a prominent section of the figure. Following your suggestion, our intention for the final version is to enhance their distinction by employing distinct colors for each module, to further highlight them clearly.\n\nQA3: This paper does not clearly explain how the three key challenges were addressed, and each module does not correspond to a challenge.\n\n    In the introductory section, we've delineated three distinct challenges:\n    1. Constructing a robust foundational representation capable of effectively encapsulating contextual information.\n    2. Unearthing the correlations between opinion expressions associated with various stances within the context of the same topic.\n    3. Harnessing task-specific attributes to enhance the extraction of argument units and aspect terms.\n\n    Our three modules are meticulously tailored to directly address these challenges:\n    1. SSF Module: This module is devoted to constructing a robust foundational representation, ensuring the effective encoding of contextual nuances.\n    2. BHGAT Module: Our BHGAT components are strategically designed to uncover and exploit correlations among opinion expressions that align with different stances under a shared topic.\n    3. SMIA Module: Focused on task-specific enhancements, the SMIA components serve to bolster the extraction of argument units and aspect terms.\n\n    In essence, each of our three modules takes a dedicated approach to tackling one of these challenges, effectively aligning with them in a one-to-one manner. We will make it clear in our final version. \n\nQuestions For The Authors:\n\nQuestion A: In Basic Module, obtaining the two representations uses BERT and LSTM, respectively. Is there anything special about this? \n\n    BERT, as a general language model, often requires fine-tuning in downstream tasks. BERT and LSTM are common combined operations. The same operation is used in the following papers. We will give corresponding quotes later in the article.\n\n    BERT, functioning as a broad-spectrum language model, frequently necessitates fine-tuning when employed in downstream tasks. The combination of BERT and LSTM is a prevalent practice, consistently applied across various works within the field. This approach is mirrored in the subsequent papers, and we will provide relevant citations to substantiate this assertion later in our article.\n    \n    [1] Jingye Li, Hao Fei, Jiang Liu, Shengqiong Wu, Meishan Zhang, Chong Teng, Donghong Ji, and Fei Li. 2022. Unified named entity recognition as word-word relation classification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 10965–10973.\n    [2] He Zhao, Longtao Huang, Rong Zhang, Quan Lu, and Hui Xue. Spanmlt: A span-based multi-task learning framework for pair-wise aspect and opinion terms extraction. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 3239-3248.\n    \n    The combination of BERT and LSTM is a prevalent practice due to the complementary strengths these two models offer. BERT excels at capturing intricate contextual information and semantic nuances, while LSTM is adept at modeling sequential dependencies and patterns within data. Integrating the two models allows for a synergistic approach where BERT's contextual understanding is enriched by LSTM's sequential processing capabilities. This combination often leads to improved performance, particularly in tasks that involve both contextual understanding and sequential reasoning. BERT's pre-trained representations provide a solid foundation, which LSTM can then build upon by capturing temporal dependencies and context coherence. This joint utilization taps into the best of both worlds, resulting in enhanced results across a range of natural language processing tasks.\n\nQuestion B: Compared to the other methods, what are HEF's strengths in addressing the three key challenges in ABAM? \n\n    In the ablation experiment (as depicted in Table 5), we delve into the impact of incrementally incorporating components on the experimental outcomes. Notably, the SSF components play a pivotal role in crafting resilient model representations that offer tangible benefits for both argument unit identification and attribute item extraction. On the other hand, the BHGAT module stands out in its capacity to proficiently convey diverse argument unit expressions under a common topic, thereby lending crucial support to the argument unit identification process.\n\n    Furthermore, the SMIA component assumes a distinct role by refining the scope within which attribute items are identified. This strategic refinement, in turn, yields a marked enhancement in the efficacy of attribute item extraction. This comprehensive combination of modules illustrates their interdependent contributions towards refining and amplifying the overall performance of the model.\n\nQuestion C: In the experiment, how does HEF perform when all the topics are mixed together?\n\n    During the experiment, we have amalgamated all topics to conduct a comprehensive analysis. Through the integration of each individual component, we optimized their respective performances. Additionally, the utilization of topic information further enhanced the overall model performance. The precise outcomes of these manipulations are elaborated upon in the ablation experiment results, presented in Table 5.\n\nTypos Grammar Style And Presentation Improvements:\n\n1.In line 57, 61, two errors arise in using the English a, an (i.e., an argument unit). 2.In line 174, the sentence “The characters representation within $w^{text}_i$ as $w^{char}_i$.” is unclear.\n\n    We greatly appreciate your valuable suggestions and insightful comments. As a result, we have carefully reviewed the manuscript and rectified any identified errors. Your input has contributed significantly to the refinement of our work.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The studied problem is interesting and important. In general, this paper is well-written and organized.\n2. The experimental results show that the proposed method significantly outperforms state-of-the-art baselines\n", "reasons_to_reject": "1.The Semantic and Syntactic Fusion (SSF) component aiming to bridge the gap between distant words is not novel. The topic of long-distance dependencies between words has been studied in previous works [1][2]. It’s not clear how the proposed method advances from them.\n2.The overall framework figure is not clear. This figure does not illustrate The key component modules (Figure 2).\n3. This paper does not clearly explain how the three key challenges were addressed, and each module does not correspond to a challenge.\n\n[1]\tYuanhe Tian, Guimin Chen, Yan Song, and Xiang Wan. 2021. Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4458–4471.\n[2]\tLi, Y., Li, Z., Zhang, M., Wang, R., Li, S., & Si, L. (2019). Self-attentive Biaffine Dependency Parsing. In IJCAI (pp. 5067-5073).\n", "questions_to_authors": "Question A: In Basic Module, obtaining the two representations uses BERT and LSTM, respectively. Is there anything special about this?\nQuestion B: Compared to the other methods, what are HEF's strengths in addressing the three key challenges in ABAM?\nQuestion C: In the experiment, how does HEF perform when all the topics are mixed together?\n", "paper_topic_and_main_contributions": "This paper focuses on the Aspect-Based Argument Mining (ABAM) task and proposes a novel Hierarchical Enhancement Framework (HEF) consisting of four modules to solve three key challenges of ABAM. The experimental results demonstrate the effectiveness of the proposed method.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "1.In line 57, 61, two errors arise in using the English a, an (i.e., an argument unit).\n2.In line 174, the sentence “The characters representation within w{text}_i as w^{char}_i.” is unclear.\n"}, "extra_text": {"Missing_References": "[1]\tYuanhe Tian, Guimin Chen, Yan Song, and Xiang Wan. 2021. Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4458–4471.\n[2]\tLi, Y., Li, Z., Zhang, M., Wang, R., Li, S., & Si, L. (2019). Self-attentive Biaffine Dependency Parsing. In IJCAI (pp. 5067-5073)."}}, {"review_id": "qt6d63GnLW", "reviewer": "Reviewer_qcN8", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "QA1: Semantic and Syntactic Fusion component proposed in the paper are not innovative. The author's explanation of why this component is used lacks clarity.\n\n    Indeed, your observation is accurate. The primary contribution of this article rests with the BHGAT component, closely followed by SMIA. BHGAT introduces a novel functionality wherein it facilitates the exchange of opinion expressions across distinct argument units centered around aspect terms within the same topic and batch. On the other hand, SMIA, tailored specifically for ABAM tasks, adeptly confines the scope of aspect term identification.\n\n    Additionally, the SSF component, drawing inspiration from existing research, generates components suited for singular presentations. These components serve the purpose of fine-tuning downstream tasks with BERT. While a corresponding reference for this aspect will be included subsequently, it's noteworthy that this won't be presented as an innovative point within the contribution section of the introduction. In our revised paper, we will highlight the other two main contributions, namely BHGAT component and SMIA component, ensuring the clarity and accuracy of the article's contributions.\n\n\nQA2: The paper does not consider many state-of-the-art ABAM methods for comparison, it has been noted that the compared baseline models do not adequately address the ABAM task but rather other tasks. Therefore, the comparison between the proposed method and the baseline model fails to convincingly demonstrate its advancement.\n\n    ABAM, initially introduced by Trautmann at the 2020 Argument Mining workshop of ACL, has not garnered sufficient subsequent research outputs. Our current study aims to bridge this gap by harnessing five recent baselines from the years 2021 and 2022. While we acknowledge the significance of expanding our comparative spectrum, we concur with your viewpoint on the necessity of a more comprehensive evaluation strategy. In direct response to your inquiry, we have augmented our baseline repertoire with an additional model grounded in contemporary advancements – the ChatGLM-6B, an open-source and lightweight alternative to ChatGPT. It functions as an advanced language processing model imbued with extensive capabilities. The ChatGLM-6B has the following distinctive characteristics:\n       \n    Has about 6.2 billion parameters.\n    The model is pre-trained on 1 trillion tokens—equally from English and Chinese.\n    Subsequently, techniques such as supervised fine-tuning and reinforcement learning with human feedback are used.\n\n    Next, we outline how ChatGLM-6B can be effectively applied to the ABAM task. We construct a prompt structured as follows: “{“content”: topic is “xxx”, text is “xxxxx”, “summary”: “CON Argument Unit”--->“xxxxx”, “PRO Argument Unit”--->“xxxxx”, aspect term--->“xxxxx”}”. The model undergoes fine-tuning using the ABAM dataset and is subsequently evaluated on the test dataset to yield final experimental results.\n\n    Our findings once again verify the superiority of our proposed HEF framework when compared with the ChatGLM-6B, both for the segment-level and token-level analyses. These outcomes not only reinforce the efficacy of our approach but also underline its significance in the domain. We trust that these results will effectively allay any concerns you might have had.\n\n    Building upon your insightful suggestion, we are dedicated to extending our experimental endeavors. Our forthcoming plans encompass the incorporation of additional comparative methodologies. We are committed to meticulously documenting and incorporating the outcomes of these novel experiments into the final paper, thereby enriching the overall robustness of our research.\n\n                       Table 1 Comparison with large language model\n    --------------------------------------------------------------------------------------\n    |\t   |                  Segment-Level              |          Token-Level      |\n    |          |   ASP   |  CON   |  PRO   |  MicF1 | MacF1  | Token-Flat | Token-Nested |\n    |ChatGLM-6B|  0.7010 | 0.4402 | 0.4311 | 0.6321 | 0.5241 |  0.7522    |    0.5981    |    \n    |HEF(our)  |  0.7969 | 0.6283 | 0.6176 | 0.7494 | 0.6810 |  0.8327    |   0.6204     |\n    --------------------------------------------------------------------------------------\n\nQA3: Equations and model structure diagrams, being essential components of your research, should align and corroborate with each other. However, in this paper, there are evident discrepancies, such as eq9, eq10, eq11.\n\n    Thank you for your valuable feedback. We have taken note of the confusion arising from the representation of the gated unit's connection with \"$g_t$\" in Figure 2, and we have since rectified this issue. Your input has been instrumental in improving the clarity and accuracy of our depiction.\n\nQA4: In the second part of the narrative, some of the elaborations are not clear enough, for example， in the Basic Module, the meaning of the symbols W and U and Q is not given.\n\n    We deeply appreciate your guidance, and we are committed to incorporating the recommended enhancements. We intend to provide further elucidation for the formula in question. Specifically, we will offer clarifications for the parameter matrices W, U, and Q. These matrices correspond respectively to the semantic representation $x_t$, the preceding state $h_{t-1}$, and the syntactic representation of $g_t$. Your input has proved invaluable in our pursuit of rendering the material more comprehensible and transparent.\n\nQA5: Lack of clarity in expression: The writing of the article could be improved, it lacks clarity and precision, and certain paragraphs are too colloquial.\n\n    We recognize the need to enhance the article's readability and clarity by incorporating additional explanatory details. Concurrently, we're dedicated to refining the article's focus by streamlining and eliminating extraneous descriptions. We'll proceed to adjust the writing accordingly in alignment with these objectives.\n\n    We extend our sincere gratitude for your valuable feedback and perceptive comments, which play a pivotal role in elevating the caliber of this paper. Your insights have proven indispensable in our continuous efforts to enhance its quality.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1、The paper propused novel module the BHGAT component.", "reasons_to_reject": "1、Semantic and Syntactic Fusion component proposed in the paper are not innovative. The author's explanation of why this component is used lacks clarity.\n\n2、The paper does not consider many state-of-the-art ABAM methods for comparison, it has been noted that the compared baseline models do not adequately address the ABAM task but rather other tasks. Therefore, the comparison between the proposed method and the baseline model fails to convincingly demonstrate its advancement.\n\n3、Equations and model structure diagrams, being essential components of your research, should align and corroborate with each other. However, in this paper, there are evident discrepancies, such as eq9, eq10, eq11.\n\n4、In the second part of the narrative, some of the elaborations are not clear enough, for example， in the Basic Module, the meaning of the symbols W and U and Q is not given.\n\n5、Lack of clarity in expression: The writing of the article could be improved, it lacks clarity and precision, and certain paragraphs are too colloquial.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper proposes a layer-based Hierarchical Enhancement Framework for the ABAM task to efficiently overlook the need for tailored strategies to effectively address the specific challenges of ABAM tasks, and introduces three novel components: the Semantic and Syntactic Fusion component, the Batch-level Heterogeneous Graph Attention Network component, and the Span Mask Interactive Attention component. The approach is evaluated effective on multiple datasets and multiple tasks, with further ablation study.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper addresses the Aspect-Based Argument Mining (ABAM) task and proposes a Hierarchical Enhancement Framework (HEF) consisting of four modules for ABAM. This paper studies an important problem and it is well written. Experimental results show that the proposed model outperforms baseline models. The paper needs to add a relation work section. Experiments on the effectiveness of SMIA component could be added as well. This paper should also clearly explain how the three key challenges were addressed by the proposed components.
Decision: Accept-Findings