Argument mining as a multi-hop generative machine reading comprehension task
Authors: Boyang Liu
Viktor Schlegel
Riza Batista-Navarro
Sophia Ananiadou
Venue: EMNLP 2023 Findings (2023)
Keywords: Argument mining; Machine reading comprehension; Generative model; Chain of thought

Abstract: Argument mining (AM) is a natural language processing task that aims to generate an argumentative graph given an unstructured argumentative text. An argumentative graph that consists of argumentative components and argumentative relations contains completed information of an argument and exhibits the logic of an argument. As the argument structure of an argumentative text can be regarded as an answer to a "why" question, the whole argument structure is therefore similar to the "chain of thought" concept, i.e., the sequence of ideas that lead to a specific conclusion for a given argument (Wei et al., 2022). For argumentative texts in the same specific genre, the "chain of thought" of such texts is usually similar, i.e., in a student essay, there is usually a major claim supported by several claims, and then a number of premises which are related to the claims are included (Eger et al., 2017). In this paper, we propose a new perspective which transfers the argument mining task into a multi-hop reading comprehension task,  allowing the model to learn the argument structure as a "chain of thought". We perform a comprehensive evaluation of our approach on two AM benchmarks and find that we surpass SOTA results. A detailed analysis shows that specifically the "chain of thought" information is helpful for the argument mining task.

Argument mining as a multi-hop generative machine reading comprehension task
Boyang Liu', Viktor Schlegel Riza Batista-Navarro' Sophia Ananiadou' 1,
Department of Computer Science, The University of Manchester, UK
2 ASUS Intelligent Cloud Services (AICS), Singapore
3 Artificial Intelligence Research Center, AIST
(boyang.liu-2@1 posigrad, riza.batista sophia.ananiadooue @ Jmanchester.ac.uk viktor_ @ asus.com
Abstract
segmentation (ACS) aims to separate argumenta- tive units from non-argumentative units; argumen- tative component classification (ACC) focuses on classifying argumentative components (AC) into specific types; argumentative relation identifica- tion (ARI) determines which ACs are related to each other, and the direction of these argumen- tative relations (AR); and argumentative relation classification (ARC) handles the labelling of the ARs. The ARI and ARC subtasks can be combined together as an argumentativv relation identification and classification (ARIC) subtask where the model needs not only to predict the existence and the di- rection (if exists) of an AR but also its type (Si et al., 2022).
Argument mining (AM) is natural language processing lask that aims to generate an argu- mentative graph given unstructured argu- mentative text. An argumentative graph that consists of argumentative components and ar- gumentative relations contains completed infor- mation of an argument and exhibits the logic of argument. As the argument structure of argumentative text can be regarded as an answer to a "why" question, the whole ar- gument structure is therefore similar to the "chain of thought" concept, i.c., the sequence of ideas that lead specific conclusion for a given argument (Wei et al., 2022). For ar- gumentative texts in the same specific genre, the "chain of thought" of such texts usually similar, i.e., in student essay, there is usually a major claim supported by several claims, and then number premises which are related to the claims are included (Eger et al., 2017). this paper, we propose new perspective which transfers the argument mining task into a multi-hor hulti-hop reading comprehension task, allow- ing the model learn argument structure "chain of thought". We perform compre- hensive evaluation of our approach on two AM benchmarks and find that we surpass state-of- the-art results.! A detailed analysis shows that specifically the "chain of thought" information is helpful for the argument mining task.
The argument structure of an argumentative text can be regarded therefore, the whole argument structure is similar to the "chain of thought" concept-spelling out sequence of reasoning steps (forming a reason- ing path) that leads to a specific conclusion for a given argument (Wei et al., 2022). For argumenta- tive texts of the same genre, the structure such "chains of thought" tends to be similar: In a student essay, there is usually a major claim supported by several other claims, and then a number of premises which related to the claims (Eger et al., 2017). Similarly, for scientific abstracts such shown in Figure premises about experimental results are used to support aspect-based claims (i.c., ACS Figure mentions three aspects: postoperative IOP, bleb morphology and complications), which are further used to support high-level claim (AC6 in Figure 1) the conclusion of the abstract. How- ever, most of the previous works (Mayer al., 2020; Rodrigues and Branco, 2022; Saadat- Yazdi et al., 2023) ignore such structural similarity and mainly pay attention to single ACs for the AC- related subtasks and AC pairs for the AR-related subtasks.
answer to "why" question:
1 Introduction
Argument mining involves automatically identify- ing and extracting arguments and their relationships from natural language texts, such as persuasive es- says or political speeches. This task aims to iden- tify the various claims and supporting evidence used to support a particular argument.
AM can be broken down into four different sub- tasks (Lgger al., 2017): argumentative component
'Our code is availble from https: http//gttthuhhcee cot. Boyang-L/MRC-GER GEMMAM
To enable the model to learn such chains and

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extract argument structure simultaneously, we pro- pose to convert AM into a generative multi-hop ma- chine reading comprehension (MRC) task (Yavuz to-sequence task
to solve the argument mining task. For example. some models only leverage single ACs or a pair of ACS as input without wider contextual informa- tion (Mayer et al., 2020; Accuosto et al., 2021). Despite using similar input, Galassi et al. (2023) recognised that individual propositions may not always provide enough information for AM.
et al., 2022). is a where the input sequence is a combination of a query as well as context, and the output sequence is the answer with the reasoning path showing how the model obtains the answer. Concretely, given AC query and the whole text the context, our upproach predicts both the reasoning path and the type of the AC or any other ACs related to the query AC according to the different subtasks of argument mining as answer. Here, the reasoning path is a path from the ROOT AC to the query AC, where the ROOT AC is defined as the source node of the longest path in an argumentative graph, which usually contains the core opinion of an argument, such as the main conclusion of the abstract. An example is shown in Figure It worth mention- ing that the direction of the path reversed from the AR so that the model can learn the high-level semantics first. The reason that we chose the gener- ative paradigm is so that only the output is changed and no extra parameters are included, thus letting the model leverage the path information. There- fore, we can directly test the impact of the path information.
In contrast, alternative studies take advantage of broader document context by providing the entire text to their models. They adopt different approaches for the AM task, such as treating it as a sequence tagging task (Eger et al., 2017), de- pendency parsing task (Ye and Teufel, 2021) or em- ploying prompt-based model (Dutta et al., 2022).
Further, some researchers found that combining plain text with its structural information is bene- ficial for AM. Examples include the distance be- tween two ACs (Galassi et al., 2023); the position of the AC in the document or paragraph (Potash et al., 2017; Bao et al., 2021); discourse parser fea- tures (Hewett et al., 2019); and argumentative zon- ing information (Liu et al., 2022). Although these types of information are helpful for the model understand the structure of an argumentative text, a model cannot understand the "chain of thought" from such structural information and the plain text only.
Furthermore, to alleviate the bias arising from the order of answers learned by the generative model, we propose a two-direction-based method that allows the model to learn the output sequence from both directions.
Other tasks as MRC task. A recent successful trend involves treating various other natural lan- guage processing tasks as an MRC task, such as relation extraction (Levy et al., 2017), named entity recognition (Li et al., 2019), event extraction (Liu et al., 2020) and coreference resolution (Wu et al., 2020). Unlike traditional MRC, multi-hop MRC requires the machine to not only predict the answer of given query, but also provide the reasoning path showing how the answer is obtained. In this paper, we propose a model to transfer the argument mining task into a multi-hop MRC task so that the model can learn the "chain of thought" from the prediction of the reasoning path.
To summarise, our contributions are as follows: 1) to our best knowledge, we are the first to cast the argument mining task as a multi-hop MRC task to let the model learn argumentative reasoning chains; 2) to alleviate the bias in the order of an- swers learned by the generative model, propose a (wo-direction-based method; 3) extensive exper- imentation on two different benchmark datasets shows that our framework outperforms related mod- els on most subtasks.
3 Method
2 Related Work
As previously mentioned, we assume that the ACS subtask has been solved and we only focus on the other subtasks. There two ways to combine the remaining subtasks, i.e., ACC ARIC and ACC + ARI ARC. Considering that there is no estab- lished preference in existing literature- former is used on the AbstRCT dataset (Mayer et al., 2020; Si et al., 2022; Galassi et al., 2023) and the latter
Argument Mining. Recently, AM has received increasing attention from the research community, and a range of models has been proposed (Eger al., 2017; Gemechu and Reed, 2019; Hewett et al., 2019; 9; Lytos et al., 2019; Dutta et al., 2020; Liu et al., 2022; Cheng et al., 2022; Liu et al., 2023). Most of them mainly pay attention to local features

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Query
EAC5> Ologen resulted in a lower long-term postopcrative IOP, a better bleb morphology, and fewer complications.
Claim
Context
Premise
Snon AC= We assessod bleb morphology and the intraocular pressure (IOP)-lowering effeet of trabeculoctomy with ologen compared to mitomycin C (MMC) in juvenile open-angle glaucoma (JOAG).. EACIST The mean postoperative IOP was statistically significantly lower than the mean preoperative IOP at each follow-up in each group. KAC2> At year, the mcan postoperative IOP was significantly lower in group A. 5AC3a According to the MBGS, blebs with an ologen implant showed significantly better scoring than those with MMC. RAC4= PAC4=AAS-CCT showed that ologen-induecs blebs had significantly more fluid- filled spaces, cleavage planes, and less fibrosis. KACS= Ologen resulted in lower long- term postoperative IOP, a better bleb morphology, and fewer complications. RAC6> Our results suggest that ologen may be useful alternative to MMC JOAG.
Path
Graph
Output sequence
ACC: spath> <AC6> <ACS> sanswer> <Claim>
ARIC: spath> <none> <AC6> ssupport> <ACS> <answer> Ssupport> <AC1> <support>
<AC2> ssupport> <AC3> <support> <AC4=
An example of how our model works on the AbstRCT dataset. Given AC5 as a query and the whole abstract the context, the output sequence is the combination of the predicted path (the tokens between the special tokens <path> and <answer>) and the answer (the tokens following <answer>). In this example, the path contains ACS, the ROOT node AC6 and the query node AC5. The answer varies depending on the subtask, i.e., for the ACC subtask. the answer is <Claim> which means the type of AC5 is claim; for the ARIC subtask, the answer means that AC1, AC2, AC3 and AC4 are ACs that support ACS, The whole argumentative graph the context in the right part can be obtained after all ACs are used queries. All relation types in this graph are "support".
Figure I:
is used on the PE dataset (Eger et al., 2017; Kurib- ayashi et al., 2019; Bao et al., 2021) we focus on all four subtasks to enable a fair comparison.
C
< non nnn_AC AC >
...b... (1)
In this section, we introduce how to cast argu- ment mining as a multi-hop generative MRC task, which consists of four parts, namely, <query, con- text, answer, path>, where the input sequence is the query and the context, and the output sequence is the answer and the path. We introduce the details of these two sequences in the following.
Query Representation. We use the AC tokens as queries. There are two types of query representa- tion Q according to the subtasks: unary query Qu that consists of one AC for the ACC, ART and ARIC subtasks, and a binary query Qs which is composed of two ACs separated by the special to- kens for the ARC subtask:
3.1 Input sequence
Qu LAC,, LAC,,
The input of the model is a word sequence that contains the context and the query.
Q4 =< AC; t.O,, tAC,,
(2)
< AAC, > tAC,, ..... tAC,,,
Let DOC {t1, .t.,, } denote an argumentative document, where represents the i-th token in DOC. As mentioned in Section 1, we assume that the posi- tion of all ACS in the document is known. The context representation consists of all tokens in the document, with additional tokens inserted to de- note the boundaries of each AC. Specifically, insert an < AC_i token before the start token of the i-th AC token sequence in document D and a < non_AC > token before the first token of all non non-argumentative sequences. The context se- quence C is shown below:
Context Representation.
Finally, the input sequence the concatenation of the query Q and the context C differentiated by two specific tokens <query> and <context>.
Input =< query > Q < context > C'
(3)
3.2 Output sequence
The output sequence of a given query consists of two parts, the answer sequence and the path se- quence.
Answer Representation. The answer representa- tion differs for different subtasks. The details are

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Path Representation
Answer Representation
ACC
ACn < ACpa 7
ACT, >
ARI
ACpi
ACpa
ACai ... ACoon
ARC
<ACpi 2.. < ACpn 7
ART, >
ARIC
ARTI >< ACpi > < ART ><
ART >< AC > ... ART ><
ACpm >
ACa, >
Table I: The path and answer representations for different subtasks, Here, < ACp, > is the ROOT AC and ACp. denotes the query AC; < ACa, > ... ACa,, > are ACs that point to the query AC: < ACT, and
ART; > represent the AC type and the AR type.
shown in Table 1. For the ACC subtask, given an AC as query, the model needs to predict the
3.3 Output Order Debiasing
For the ARI and ARIC subtasks, the output is of correct answers rather than a single one, which raises the question of its order in the out- put sequence. Arranging them ascending order (< AC1 >; < AC2 .....) might teach the model an order bias and disincentivise the generation of AC; > tokens when an ACCss >> token is generated. resulting in unrecoverable errors.
type of the AC. Similar to the ACC subtask, given a pair of ACs with a relation between them, the answer for the ARC subtask is specific token ART; > representing the AR type. For the ART subtask, the answer to a given query contains all ACs that are related to it. As for the ARIC subtask, given AC query, the model needs to predict all the ACs related to and the relation types at the same time. Therefore, the ARIC subtask on the AbstRCT dataset is defined as a directed rela- tion classification task with three possible types: None, Support and Attack. Here, a true positive is an outcome where the model correctly predicts both the relation type and direction, given two ACs, Therefore, the answer contains both the type tokens ART; and the AC tokens ACa,
To alleviate this issue, we propose simple but effective two-direction augmentation method. Con- cretely, for each ARI and ARIC query, we create two training samples with different answers, one sorted by the ordinal number of the AC tokens ap- pearing in the sequence and the other one sorted in reverse.
3,4 Training and Inference
Path Representation. A reasoning path starts from the ROOT node and ends at the query AC. We define AC;- > as ROOT when satisfies the follow- ing: first, < AC; > does not point to any other AC3 >; second, there is a path that starts with < AC, > and the length of the path is the longest among all the paths. We use the path representa- tion without relation types for the ACC, ARI and ARC subtasks. For the ARIC subtask, we also in- clude type information the path representation to align with the answer representations. The path representations are shown Table 1.
Training, The output sequence contains two parts, path and answer; this is form of multi-task paradigm that includes the learning of both se- quences jointly. Thus, during training, we calculate the loss of each part separately and then sum the two our final loss function.
(1
+ ALanswer
(5)
where L is the cross entropy loss function and [0, 1] denotes the weights of Lanswer- To let the model leverage the information from pre-trained language models, we employ a warm start strategy inspired by (Guo et al., 2022). To be specific, we use the embedding of number i as the initial representation for the specific token < AC, > instcad of training the embedding from scratch. The model will cost more time and the training will be unstable if the model learns the embeddings from scratch during fine-tuning. The representation of the context is kept the same for all four subtasks.
The final output sequence is the combination of the path sequence and the answer sequence denoted by two specific tokens <path> and <answer>.?
Output =< path > P answer A
'Incorporating the whole graph rather than the path infor- mation might incorporate additional global information, hut it performed worse in initial experiments (see Appendix B).

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During the training phase, we optimise the nega- tive loglikelihood using teacher forcing.'
ARs: support and attack. In order to maintain con- sistency with previous studies (Bao et al., 2021; Kuribayashi et al., 2019), each paragraph with its ACs and ARs is considered as an instance. Here, each AC is associated with at most one outgoing AR, which means that the argumentative graph of each paragraph has tree or forest structure.
Inference. During inference, we use beam search decoding to obtain the output sequence Outpul in an autoregressive manner. We then post-process the decoded sequence using the answer indicator (<an- swer>) to obtain the answer and convert the output sequence into labels according to their meanings described in Section 3.2.
4.2 Evaluation and Implementation
Evaluation. For the AbstRCT datasct, we follow previous studies (Mayer et al., 2020; Si et al., 2022; Galassi et al., 2023) by merging major claim and claim into a single category. All results on both datasets are averaged scores of three different ran- dom seeds and are reported macro- -veeragd FI scores. For the AbstRCT dataset, we use the same train-developmen-ttss split as et al. (2022). For the PE dataset, we keep the same train-test split and randomly select 10% of the training set for validation, like Bao et al. (2021) do.
4 Empirical study
4.1
Dataset
We used two publicly available datasets to evaluate our model and compare it with results obtained by previously proposed models. Descriptive informa- tion on these two datasets is provided in Table 2. Next, we will give a brief description of them.
Documents
All ACs
All ARs
Neo train
350
2267
1427
Implementation. We fine-tune BART-Basc model (Lewis al., 2020) for the PE dataset and BioBART-Bass model (Yuan et al., 2022) for the AbsRRT dataset. Regarding the learning rate, we set it to 3e-5 for the ACC and ARC subtasks, 2e-5 for the ARI subtask, and 8e-5 for the ARIC subtask. The max sequence length is 512 for the PE dataset and 768 for the AbstRCT dataset. The batch size is 16, and we assign a value of 0.7 to the hyperparameter A. During inference, ploy beam search with a beam size of 4 for decod- ing purposes. To optimise our model, we employ A (Loshchilov and Hutter, 2017). We train our model for 15 epochs except for the ARC sub- task where we train it for 20 epochs and select the best checkpoint on the development set.
Neo_ dev
50
326
210
Neo_lest test
100
686
424
Gla_test _es_tett
100
594
367
Mix_test
100
570
329
PE
2235
6095
3832
Table 2: Statistics of datasets used in our paper. In order to show the difference between different test sets of the AbstRCT dataset, we report the data statistics of three test sets separately. Here, Neo, Gla and Mix represent neoplasm, glaucoma and mixed. The statisties of the PE dataset are paragraph level.
em-
AbstRCT (Mayer al., 2020) is comprised of 659 abstracts from biomedical publications. These abstracts are annotated with three types of ACs (major claim, claim and evidence) and two types of ARs (support and attack). The dataset is divided into three parts. The first part, the ncoplasm corpus, is further divided into training, development and test sets. Additionally, there are two separate test sets: one for glaucoma and another for mix of topics. The argumentative graphs in this dataset exhibit a non-tree (graph) structure.
4.3 Baselines
We compare our method it with the following base- lines on AbstRCT:
ResArg (Galassi et al., 2018) is residual network model combined with a long short-term memory (LSTM) network that jointly addresses the ACC, ARI and ARIC subtasks.
Persuasive Essays (PE) (Eger et al., 2017) con- tains of 402 essays (2235 paragraphs). Three types of ACs (MajorClaim, Claim, and Premise) are used for annotation. In addition, there are two types of
ResAttArg (Galassi et al., 2023) is an extension of the ResArg model that includes an attention module and ensemble learning. Both ResArg and ResAt- tArg have an average and ensemble version.
'Note that we experimented with multi-task learning by training the model on all subtasks at once, but this set-up performed worse than the formulation introduced ahove (see Appendix A for details).
ST (Si et al., 2022) implements a multi-task learning framework that leverages the sequential dependency between the ACC and ARIC subtasks

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ACC
ARIC
Neo
Gla
Mix
Neo
Gla
Mix
ResArg(avg)
86.18
85.53
86.74
59.15
57.23
60.31
ResArg(Ensemmle)
86.38
87.13
87.59
63.16
61.86
68.35
ResAAttArg(avg)
86.19
86.26
87.51
66.49
62.68
63.47
ResAAttAr(EEnemmle))
87.87
87.71
89.70
70.92
68.40
67.66
SeqMT
91.89
92.35
92.21
71.24
73.27
72.71
MRC_GEN
92.76
92.62*
93.97M
74.97
74.28
73.87*
Table 3: Overall results the AbaRCT dataset. Here, Neo, Gla and Mix correspond to the results achieved for the neoplasm, glaucoma and mixed test sets, respectively. The highest scores are emboldened. iidiccatss statistically significant improvements over the baselines with our model, according pair-wise t-fest with p 0.05.
ACC
ART
ARC Support 94.7
Macro
MC
Claim
Premise
Macro
Rel
No-Rel
Macro
Attack
Joint-ILP
82.6
89.1
68.2
90.3
75.1
58.5
91.8
68.0
41.3
St-SVM-full
77.6
78.2
64.5
90.2
60.1
Joint-PN 84.9
89.4
73.2
92.1
76.7
60.8
92.5
Span-LSTM 87.3
81.1
79.0
96.8
61.1
BERT-Trans 88.4
93.2
78.8
93.1
82.5
70.6
94.3
81.0
MRC_GEN
89.2*
94.8*
79.6
93.2
82.7*
70.9*
94.4
78.2*
97.7*
58.9*
Table 4: Overall results the PE dataset. * indicates statistically significant difference between the baselines with our model, according to pair-wise t-test with < 0.05.
by transferring the representation of the input and output of the ACC subtask to the ARIC subtask. For PE, we compare with the following baseline approaches:
for both datasets, even though our model is fine- tuned BART-Base model, while other baselines are considerably more complex than ours.
Specifically, on the AbstRCT dataset, our model achieves the best performance on both the ACC and ARIC subtasks on all test sets. We also observe that for the PE dataset, our improvement is smaller the ARI subtask and the performance does not reach that of the BERT-Trans model on the ARC subtask. However, BERT-Trans uses additional fea- tures, such as the relative distance between each two ACs and the bag-of-words vectors to improve the performance, while such features are not in- cluded in our model.
Joint-ILP (Stab and Gurevych, 2017) is an end- to-end argumentation structure parser that globally optimizes ACs and ARs through Integer Linear Programming (ILP).
St-SVM-full (Niculae et al., 2017) is a linear struc- tured SVM which formulates AM as inference in a full factor graph (Kschischang et al., 2001), which solves the ACC and ARI subtasks jointly. Joint-PN (Potash et al., 2017) is joint model based on Pointer Network architecture classify types of ACs and identify relations between ACs. Span-LSTM (Kuribayashi et al., 2019) is an LSTM-minus-base based span representation model with argumentative markers as extra information. HERT-Trane (Bao et al., 2021) is a neural transition-based model designed for ACC and ARI subtasks. It is now the state-of-the-art (SOTA) model on this dataset.
4.5 Ablation Study
We perform ablation experiments to investigate the effect of our method design on the overall per- formance on both benchmarks. There are mainly three models for the ablation study, MRC_GENC path) is designed to test whether the path informa- tion improve the performance; MRC (EN((L)) aims to test the effect of the two-direction method; and MRC GEN(-Ws) is for exploring the impact of warm start. Since the two-direction method is only suitable for the ARI and ARIC subtasks where the answer is a sequence of special tokens,
4.4 Main Results
Performance comparisons between our model and the baselines are shown Table 3 and Table 4. Our model achieves SOTA results on most of the tasks

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ACC
ARIC
Neo
Gla
Mix
Neo
Gla
Mix
MRC_ GEN
92.76
92.62
93.97
74.97
74.28
73.87
MRC_GENT LLC_GENN-paat (path)
92.29
92.27
93.72
72.47
70.10
71.20
MRC_ (tN(td)
74.29
71.90
73.50
92.83
92.72
94.11
74.23
70.12
73.09
Table 5: Results of ablation experiments on the AbstRCT dataset. MRC_ BRCC_GE((-pathl denotes that the model only needs to predict the answer without the path information: MRC_ (ENC_E)) means that the two-direction method is excluded; MRC_GEN(<<N)) GEN(-ws) uses a cold start method and the specific tokens are trained from scratch.
ACC
ARI
ARC
Macro
MC
Claim
Premise
Macro
Rel
No-Rel
Macro
Support
Attack
MRC_ DeC_
89.2
94.8
79.6
93.2
82.7
70.9
94.4
78.2
97.7
58.9
MRC_ (RC_GEN(-path)
88.0
93.3
78.2
92.7
79.8
65.6
93.8
75.7
97.4
54.1
(RC__GEN-td)
81.7
69.0
94.4
MRC_ GEN(Ws)
87.9
93.5
78.0
92.5
81.1
68.6
93.5
74.0
97.5
50.5
Table 6: Results of ablation experiments the PT: dataset.
MRC__EN((C can not be applied to the ACC sub- task. The results are shown in Table 5 and Table 6. Two-Direction Method. Comparing the results of MRC_GEN(-td) and MRC_GEN reveals that our two-direction method works for the ARI and ARIC subtasks. To explain this observation, we calculate the percentage of examples where the order of answer AC tokens is strictly ascending. For the ARIC subtask on the AbstRCT dataset, the percentage is 27.09%. The proportion on the PE dataset for the ARI subtask is 34.83%. This shows that forcing the model to learn only from examples in ascending order may inhibit its ability to generate the correct answer.
is about the content of the AC. However, overlearn- ing the representation from the warm start method may also decrease the performance. Therefore, good hyper-parametee are also important to let the model learn balanced representation of these two types of information. Thus, we believe that the syper-parameters for the ACC subtask on the PE dataset are good enough to learn a balanced representation.
Path Information. In general, without path in- formation, the scores drop for all tasks on both datasets. The decrease is more obvious on the relation-basee based tasks such as ARI, ARC and ARIC as opposed to ACC. Specifically, for the ACC task, the average improvement from the path informa- tion on all three test sets on the AbstRCT and PE datasets is 0.36 points and 1.2 points, respectively. This may be due the model without path informa- tion already reaching high performance on this sub- task. As for the ARI and ARC subtasks, generating path information results in improvements of 2.9 and 2.5 points on the PE dataset, which shows the pos- itive impact of the path information and indicates that the "chain of thought" method is useful for the argument mining task. The path information is most important for the ARIC subtasks. Here, with- out the path information, the performance drops 3.1 points on average, possibly because the ARIC sub- task a combination of the ARI and ARC subtasks which both benefit from path information.
Warm Start. The warm start method improves the performance in most cases, in line with liter- ature (Guo et al., 2022). Leveraging pre-trained embeddings starting points for newly-added to- kens is better than training their embeddings from scratch, since the size of the dataset for fine-tuning is much smaller than that of the pre-training dataset. Therefore. is difficult for the model to fully learn the semantics of the new tokens only from the fine- tuning data. However, from Table 5, we also find that warm start is clearly hurting the ACC task on AbstRCT. From an intuitive point of view, each AC token needs to include two types of informa- tion: the location of the AC and the content of the AC. The warm start method only includes lo- cation information since we use the embedding of the numbers as a starting point for each AC token, while the model also needs to learn that this token

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4.6 Impact of the Path
where MRC_ GEN(--ath) predicts the wrong an- swer while MRC G_N predicts the correct answer even though the path is predicted wrongly. We find two main behaviours. We call the first one path extension, where the predicted path is an ex- tension of the ground truth path. It usually occurs on sub-graphs with smaller number of nodes, when argumentative graph consists of two un- connected sub-graphs. Because in most cases, the smaller sub-graphs do not contain the full "chain of thought", the model may learn it from the biggest sub-graph to have a more general understanding of an argument. One example can be seen in Ex- ample I of Table 8. There are two unconnected sub-graphs, the smaller one containing AC3 and AC7. To understand the full "chain of thought", AC6 is wrongly predicted as the predecessor of AC7, while the model correctly predicts the answer. The second behaviour, which we refer to as claim replacement, occurs when one claim is exchanged with another one with similar high-level semantics to conclude the whole paper. As shown in Table 8, the model wrongly predicted the path as '<none> <AC9> + <support> <AC6>' given Example 2 due to the similarity of AC8 and AC9. Seemingly. in these two situations, slightly wrong path is also beneficial the model. We sampled 30 ex- amples where the path was predicted wrongly and manually analysed them. We found that 16 were in- stances of path extension whereas 6 were instances of claim replacement.
In Section 4.5, we only show the general impact of the path for the argument mining task by com- paring model performance with and without path information. Here, examine the effect of path information.
all
ACC
83.19
70.49
38.09
6,25
70.61
ART
87.72
71.63
31.59
10.41
72.16
ARC
89.51
57.14
0
81.33
Table 7: Accuracy of the predicted path on the PE dataset. Here, I. 2, 3 and 4 refer to the length of the path; all denotes the accuracy on all lengths.
First, we calculate the accuracy of the predicted path. Here, only exactly matching true and pre- dicted paths are treated as a correct instance. We not only report the overall accuracy, but also the accuracy according to the length of the path. It is clear in Table 7 and Table 9 that an increasing path length leads to significant drops in prediction accu- racy. This is in line with our hypothesis that longer paths are harder to learn for the model. Another interesting phenomenon is that the overall accuracy on the ART and ARIC subtasks higher than that on the ACC subtask. One possible reason is that the path prediction subtask requires the understanding of ARs, and thus might benefit from the ARI and ARIC subtasks.
Furthermore, we explore whether correctly pre- dicting the path indeed improves the overall task performance. Therefore, we compare the results of MRC GEN with DARC_GEN(-pattee when the path is predicted correctly or wrongly, respectively. Specifically, we first run the MRC_GEN model and then split the test set into parts where the predicted path is correct and incorrect. Then, report the performance difference between the two models on these two subsets. The results are shown in Ta- ble 10 and Table 11. On the PE dataset, it is clear that with the path information, the performance of our model drops when the predicted path is wrong, while it increases when the predicted path is correct. This means that the performance truly improves be- cause of correctly predicted paths.
4.7 Tree vs. Non-tree Argument Structure
As we mentioned in Section 4.1, the argumentative graphs in the two datasets exhibit different struc- tures (non-tree structure for the AbstRCT dataset vs. tree structure for the PE dataset). In this sec- tion, we will discuss the impact of the difference in structure.
Intuitively, the graph structure is more random compared with the tree structure. That might be the reason that the accuracy of the path prediction the PE dataset (from 70.61 to 81.33 on all subtasks) is higher than that on the AbstRCT dataset (from 60.34 to 66.44) as shown in Table 7 and Table 9.
Another conclusion is that the path information might be more useful on the graph-based dataset. From Table 10 and Table 11 can see that even when the path information predicted wrongly, the model could still improve the performance. See Example in Table 7. The argumentative graph
More interestingly, however, for the AbstRCT dataset, the performance increases on most of the subtasks on all three test sets regardless of the correctness of the predicted path. To inves- tigate this issue, we manually analyse the cases

---

Example
Example
Text
<AC3> Kaplan-Meie estimates showed trend in over- all survival favoring epoetin alfa 11 log-rank test), <non AC AC6> Epoctin alfa safely and effee- tively ameliorates anemia and significantly improves QOI. cancer parients receiving nonplatinum chemother- apy. AC7 Encouraging results regarding increased sur- vival warrant another trial designed to confirm these find- ings.
<AC6>> Hepatic glucose production decreased after rapamyein pre-treatment (- 1.I 1.1 mg/kg/min, p 0.04) after IIx (- 1.6 0.6 mg/kg/min, p 0.015), <non _C>..... <AC8> Rapamycin pre-treatment before IT' succeeds in reducing insulin requirement, enhancing hepatic insulin sensitivity. <AC9> This treatment may improve short-term ITx outcomes, possibly in selected patients with TIDM complicated by insulin resistance. (<AC2> sup <AC8>), (<AC3> sup <AC8>), (<AC6> sup <AC8>)
Rel
(<ACI> sup <AC6>), (<AC2> sup <AC6>), (<AC4> sup <AC6>), (<ACS> sup <AC6>). (<AC3> sup <AC7>)
TP
"<none> <AC7>'
<none> <ACS>' '<support> <AC6>*
<nooe> <AC6>*
<supports <ACT>'
<none> <AC9>' 'ssupport> <support><AAC>>>> <AC6>
Table 8:
Two examples where MRC_ BreC_GENN-pahh predicts the wrong answer while MRC_ gets the correct answer even though the path is predicted wrongly. Rel denotes the relations in the given argumentative text. TP denotes "true path" and PP represents "predicted path".
consists of two unconnected subgraphs. The first one includes five ACs with AC1, AC2, AC4 and all
2
4
ACC NEO
67.60
69.81
21.49
9.52
60.73
AC5 supporting AC6. The second one contains only two ACs (AC3 supports AC7). When AC7 is used as a query, the true path is '<none> <AC7>'. However, the model predicts wrong path '<none> <AC6>' '<support> <AC7>' that includes the information of AC6 and correctly predicts the rela- tion between AC3 and AC7. is clear that AC7 is a more general claim which can be used in many papers while AC6 and AC3 share some important information that is specific in this paper, such as epoetin alfa. From this point of view, AC6 be regarded piece of implicit information for the relation between AC3 and AC7. Therefore, the path extension helpful for the non-tree structures because some sub-graphs may not contain enough information. Meanwhile, for the tree structures, all the relations are connected explicitly. Thus, the model will surf from the wrongly predicted paths significantly.
GLA
62.24
66.22
5.3
61.89
MIX 60.46 67.29
24.11
22.22
60.44
ARIC NEO 69.92 64.09
27.41
19.04
60.34
GLA 66.53 66.11
13.33
64.08
MIX 67.13 69.30 48.93 22.22 66.44
Table 9: Accuracy of the predicted path on the AbsRCT dataset. Here, 1, 2, 3 and 4 refer the length of the path; all denotes the accuracy on all lengths.
ACC
ARI
ARC
Wrong_path -9.22
-4.42
-4.08
_ort
+4.13
+6.09
+3.97
Table 10: The difference between MRC_GEN and MRC GEN(-path) the PE dataset when the path is predicted correctly or wrongly. Here, (Wrong/Correct) path means that the path is predicted wrongly/correetly. Positive values mean that the path information improves the performance.
Conclusion
In this paper, we cast the argument mining task a multi-hop generative MRC task, which provides us with a means to leverage the "chain of thought" of an argument in a generative manner for the argu- ment mining task. In addition, we also introduce a two-direction method to alleviate the order bias of the output sequences of our model. The extensive experimental results and detailed analysis demon- strate the positive impact of the "chain of thought". One direction for future work is quantifying the error of the path prediction to develop a reward model and train the generative model on that using proximal policy optimization.
ACC
ARIC
NEO
Wrong_path +1.07
+2.86
Correct_palt +0.30
+2.45
GLA
Wrong_path -1.49
+11.52
Correct_patl +0.66
+4.75
MIX
Wrong_path -1.49
+3.67
Correct_path
+0.81
+3.31
Table 11: The difference between MRC_GEN and MRC GEN( on the AbstRCT dataset when the path is predicted correctly wrongly.

---

Limitations
Subhabrata Dutta. Dipankar Das. and Tanmoy Chakraborty, 2020. Changing views: Persuasion modeling and argument extraction from online dis- cussions. Information Processing & Management, 57(2):002085.
In this paper, we show that the "chain of thought" information can improve the performance of ar- gument mining models. However, such informa- tion may be not useful in user-generated arguments drawn from other domains such as social media, i.e., online forums, where the "chain of thought" can be more random compared with scientific ab- stracts and student essays. Another shorteoming is that we use only path information as the chain of thought. However, this captures only part of the semantics since argument structure is usually a graph rather than single path.
Subhabrata Dutta, Jeevesh Juneja, Dipankar Das, and Tanmoy Chakraborty. 2022. Can unsupervised knowledge transfer from social discussions help gument mining? In Proceedings of the 60th Annual Meeting of the Association for Computational Lin- guistics (Volume I: Long Papers), pages 7774-7786, Dublin, Ireland. Association for Computational Lin- guistics.
Steffen Eger, Johannes Daxenberger, and Iryna Gurevych. 2017. Neural end-to-end learning for computational argumentation mining. In Proceed- ings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume I: Long Pa- pers), pages 1 11-22, Vancouver, Canada, Association for Computational Linguistics.
Acknowledgement
This work is supported by the computational shared facility at the University of Manchester and the project JPNP20006 from New Energy and Industrial Technology Development Organi- zation (NEDO).
Andrea Galassi, Marco Lippi, and Paolo Torroni. 2018. Argumentative link prediction using residual net- works and multi-objective learning. In Proceedings of the 5th Workshop on Argument Mining, pages 1-10, Brussels, Belgium. Association for Compu- tational Linguistics.
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Single
Multi
ACC
NEO
92.76
92.83
GLA
92.62
93.03
MIX
93.97
94.26
ARIC
NEO
74.97
74.35
GLA
74.28
72.06
Hongyi Yuan, Zheng Yuan, Ruyi Gan, Jiaxing Zhang, Yutao Xie, and Sheng Yu. 2022. BioBART: Pretrain- ing and evaluation of biomedical generative lan- guage model. Proceedings of the s1 Workshop on Biomedical Language Processing, pages 97-109, Dublin, Ireland. Association for Computational Lin- guistics.
MIX
73.87
73.14
Table 12: Results of multi-task training and single task training on AbsRRC dataset.
ACC
ARI
ARC
A Multi-task Training
Single 89.2
82.7
78.2
Multi
87.5
80.2
72.4
We also try use a multi-task training paradigm. As mentioned Section we use the same query token <query> for all the subtasks. However, is not suitable for the multi-task training setting because given the same query and the context, the model needs to predict different answers accord- ing to the different subtasks. In this setting, need to distinguish queries for different subtasks. Concretely, use <q_acc>, <q_ari>, <q_are> and <q_arie> represent the queries for the ACC, ARI,
Table 13: Results of multi-task training and single task training the PE dataset.
the path information since the path information only include part of the argument structure infor- mation. Since our model is BART-Basy model and the output is only sequence but not a graph. we need to transfer the graph representation into a sequence. We propose two ways to do this. Due to the nature of argumentation, argumentative graphs are all directed acyelic graphs. Therefore, we use a graph traversal algorithm, namely topological sort- ing, to represent a graph sequence. Given argumentative graph {V,E). the algorithm returns sequence of nodes:
ARC and ARIC subtasks.
The loss function for each subtask is the same as the single subtask setting, and the final loss for both subtasks is shown as below:
AbsERCT = LACO LARIC
(6)
= LACC + LARI LARC
(7)
< AC >< AC AC,, n
(8)
We report the results in Table 12 and Table 13. It can be seen that the results of the multi-task training for most subtasks on both datasets are lower than the single task training setting. For the AbstRCT dataset, the reason may be that the difference be- tween the best learning rate for the ACC (3c-5) and ARIC (8e-5) subtasks is large. Meanwhile, on the PE dataset, our results are line with pre- vious works (Bao et al., 2021; Kuribayashi et al., 2019) which found that multi-task training for all the three subtasks decreases the performance. One possible factor is the weight of each subtask. In paper, use 1:1:1 when there three sub- tasks (ACC, ARI and ARIC) and 1:1 when there are two subtasks (ACC and ARIC). Fine-tuning this hyper-parameter might improve the performance.
In order to show the hicrarchical structure of the graph, we use 'I' as a separator between different layers.
G >< AC2 <<
(9)
In addition, we also leverage the adjacency ma- trix represent the argumentative graph inspired by (Guo et al., 2022; Bao al., 2022). Each edge represented by the start and end pairs of the edge. and is used to distinguish different edges:
G=< ACT >< AC2 > I...
< AC, >< AC; > >
(10)
< AC >< AC,
From Table 14 we find that the models using the graph information instead of the path information work poorly even when compared with the case where there is no further information included. It
B Graph as Reasoning Path
We also conducted some initial experiments to leverage the whole graph information instead of

---

no_path
graph_ad
graph_topy topo
-n-neo
-8-ga mix
ACC
NEO
92.29
90.14
92.70
0.78
GLA
92.27
91.92
93.02
MIX
93.72
91.20
94.14
0.74
ELSCORE
ARIC
NEO 72.47
67.79
64.03
0.7
GLA 70.10
64.99
62.74
MIX
71.20
67.45
64.75
0.6
Table 14: Results of leveraging the whole graph infor- mation on the AbstRCT dataset.
LAMBDA
Figure The performance on the ARIC subtask based on the AbstRCT dataset using different values of A.
seems that the topological sorting representation is helpful on the ACC subtask; however, it decreases the performance on the ARIC subtask significantly. In general, we believe that the reason why includ- ing the whole argument graph instead of the reason- ing path harms performance is that the sequence- to-sequence model is not strong enough to learn the whole graph information. The accuracy (based exact match) of the predicted graph is too low (5.86% the AbstRCT dataset). We think that there are mainly two reasons. First, the number tokens for the representation of a whole graph (from tokens to 26 tokens) much longer than that of a path (from I token to tokens). As shown in Table 7 and Table 9, the accuracy of the path prediction drops significantly with the increase of the path length. When the path length is 4, the accu- racy be only 9.52%. Another reason is that the semantics of a path is in sequential order which is consistent with the learning method of pre-trained language models, while the graph structure is more complex and is not easy to learn in a sequential order.
and 0.9. As mentioned Section 3.4, the higher X is, the less attention is paid to the path information. Figure 2 and Figure show the impact of À on the AbstRCT dataset. It is clear that when À is too small, the model cannot learn the answer part of the output sequence well, which causes lower performance. However, if the À is too large, the model mainly concentrates the answer part and the path information is fully understood by the model.
Hyperparameter Analysis
- neo
-8 gla - mix
0.555
0.94
0.535
F1-SCORE
0.90
6.525
0.90
0.91
0.2
0.2
0.7
0.8
0.5
LAMBDA
Figure 2: The performance the ACC subtask based the AbstRCT dataset using different values of X.
In this section, we investigate the effect of the loss weight A. The value of À is set between 0.1


Reviews:
[{"review_id": "7steJBz98a", "reviewer": "Reviewer_g9cj", "summary": "", "questions": "(Q.a) What are the implications of the difference between PE and AbstRCT for the performance of the method on each task, especially in light of the ablation study (e.g. table 9 VS table 10)? \n\n(Q.b) The authors claim on line 428 that “The warm start method improves the performance in most cases”. This is while the performance on ACC on all three test sets in AbstRCT is worse with a warm start. This does not seem to be a random observation, and the reason does not seem clear, especially since path prediction for ARIC does not seem much better than ACC (table 8), i.e., worse path prediction might not be a cause of this difference. What is causing this then? (The cause could be unknown to the authors, but a discussion pointing this out seems appropriate.)\n\n(Q.c) Line 525 reports some statistics on occurrence of path extension and claim replacement: 16 and 6 occurrences (respectively) out of a sample of 30 observations. Are these 30 examples instances of wrong predicted path but correct answer? If so, is there any other feature for the remaining 8 instances that stands out? \n\n(Q.d) (optional) Why and in what ways does including the whole argument graph instead of the reasoning path hurt the performance? This is to some extent discussed in Appendix D, but a brief mention of this in the main text, beyond a footnote, could be helpful.\n\n(Q.e) (optional) Are the baseline models trained via teacher forcing? If not, how do their performances compare with the proposed method without teacher forcing? Or how do they perform if they are trained via teacher forcing?\n", "limitations": "", "rating": 4, "confidence": 2, "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": "Thank you very much for your review. We read it carefully and try our best to solve your concerns. The details are shown below. Please note that we use the same serial number as you used to reply to your comments (i.e., Q.a means the answer to your question Q.a). \n\n**Response to\"Reasons To Reject\"**:\n\n**W.a.1**. Due to the limitation of the space, we moved this part into the Appendix. We agree that it is important. Therefore, if our paper is accepted, we will move this part into the main text.\n\n**W.a.2**. That is really one phenomenon that confuses us. However, we are not the only ones met this problem. As we mentioned in Appendix C, previous papers (Bao et al., 2021; Kuribayashi et al., 2019) already show that their models suffer from joint learning of all three subtasks. Another successful multi-task method, SeqMT (Si et al., 2022), implements a multi-task learning framework that leverages the sequential dependency between the ACC and ARIC subtasks by transferring the representation of the input and output of the ACC subtask to the ARIC subtask. SeqMT shows the positive impact of joint learning by leveraging the sequential dependency between the ACC and ARIC subtasks. Based on these three papers, we think that a simple joint learning method is not enough to learn the complex relation between the subtasks of argument mining and some better methods are needed to model this complex relation. Another possible impact factor is the weight of each subtask. In our paper, we use 1:1:1 when there are three subtasks (ACC, ARI and ARIC) and 1:1 when there are two subtasks (ACC and ARIC). Fine-tuning this hyper-parameter might improve the performance. We will add these analyses to our paper if it is accepted so that some other researchers could also join to find the reasons or develop a better joint learning method.\n\n[1] Jianzhu Bao, Yuhang He, Yang Sun, Bin Liang, Jiachen Du, Bing Qin, Min Yang, and Ruifeng Xu. 2022. A generative model for end-to-end argument mining with reconstructed positional encoding and constrained pointer mechanism. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10437–10449, AbuDhabi, United Arab Emirates. Association for Computational Linguistics.\n\n[2] Tatsuki Kuribayashi, Hiroki Ouchi, Naoya Inoue, Paul Reisert, Toshinori Miyoshi, Jun Suzuki, and Kentaro Inui. 2019. An empirical study of span representations in argumentation structure parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4691–4698, Florence, Italy. Association for Computational Linguistics.\n\n[3] Jiasheng Si, Liu Sun, Deyu Zhou, Jie Ren, and Lin Li. 2022. Biomedical argument mining based on sequential multi-task learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics.\n\n**W.a.3**. It is a good question. We are also interested in why the warm start method does not work on the ACC subtask on the AbstRCT dataset. However, since we directly used the warm start method and it is not our contribution and concentration, we can just give some simple reasons based on our experimental results. We guess one possible reason is that fine-tuning hyper-parameters could have a similar effect with the warm start method as the warm start could only provide better start points for the specific tokens <ACi> but not better final representations. \n\nWe got this conclusion from our experimental process. In the beginning, we used a set of common hyper-parameters to test the performance of our model and we compared the performance of the two models with and without warm-start, we found that warm-start could improve the performance on all the subtasks. Then we started fine-tuning the hyper-parameters and chose the ones that achieved the best performance. Then we did an ablation study based on these hyper-parameters, from which we found that the warm start method hurt the performance on the ACC subtask when the AbstRCT dataset is used. The best learning rate for the ACC subtask on the AbstRCT dataset is 3e-5. When we use 2e-5 as the learning rate, the warm start method works (the F1-scores are 92.50, 91.84 and 93.71 when warm start is used and are 92.21, 91.66 and 93.59 without warm start). \n\nFrom an intuitive point of view, each AC token needs to include two types of information, the location of the AC and the content of the AC. The warm start method only includes location information since we use the embedding of the numbers as a start point for each AC token, while the model also needs to learn that this token is about the content of the AC. However, overlearning the representation from the warm start method may also decrease the performance. Therefore, good hyper-parameters are also important to let the model learn a balanced representation of these two types of information. So we think the possible reason is that the hyper-parameters for the ACC subtask on the PE dataset are good enough to learn a balanced representation.\n\n**W.b**. We agree that discussing the differences between different types of argument structures (graph or tree) is very useful. The reason we did not provide this type of information is that it is impossible to give a direct comparison as the differences between the two datasets are multiple. To be specific, they are in different domains(the PE dataset is the general domain and the AbstRCT dataset is the biomedical domain), different genres (student essay and scientific paper) and different lengths (the average length of the texts in the AbstRCT dataset is much longer than that in the PE dataset). \n\nHowever, we can still obtain something based on the differences in the performances of the two datasets. Intuitively, the graph structure is more random compared with the tree structure. That might be the reason that the accuracy of the path prediction on the PE dataset (from 70.61 to 81.33 on all subtasks) is higher than that on the AbstRCT dataset (from 60.34 to 66.44) as shown in Table 6 and Table 8. \nAnother conclusion is that the path information might be more useful on the graph-based dataset. From Table 9 and Table 10 we can see that even when the path information is predicted wrongly, the model could still improve the performance. See Example 1 in Table 7. The argumentative graph consists of two unconnected subgraphs. The first one includes 5 ACs with AC1, AC2, AC4 and AC5 supporting AC6. The second one only contains two ACs (AC3 supports AC7). When AC7 is used as a query, the true path is ’<none> <AC7>’.  However, the model predicts a wrong path “<none> <AC6>’→ ’<support> <AC7>” that includes the information of AC6 and correctly predicts the relation between AC3 and AC7.  This phenomenon is called path extension in Line 503. The content of these three ACs are shown below:\n\n<AC3>: *Kaplan-Meier estimates showed a trend in overall survival favoring epoetin alfa (P =.13, log-rank test),*\n\n<AC6>: *Epoetin alfa safely and effectively ameliorates anemia and significantly improves QOL in cancer patients receiving nonplatinum chemotherapy.*\n\n<AC7>: *Encouraging results regarding increased survival warrant another trial designed to confirm these findings.*\n\nIt is obvious that AC7 is a more general claim which can be used in many papers while AC6 and AC3 share some important information that is specific in this paper, such as epoetin alfa. From this point of view, AC6 can be regarded as a piece of implicit information for the relation between AC3 and AC7. Therefore, the path extension is helpful for the graph structures because some sub-graphs may not contain enough information. While for the tree structures,  all the relations are connected explicitly. Thus, the model will surf from the wrongly predicted paths significantly. \n\n**W.c**. We accept this suggestion. We agree that quantitative information is more perspective than speculative or equivocal language. However, it is worth mentioning that “a smaller number of nodes” does not mean a range of node numbers here. As we mentioned in line 504 to line 507,  “It usually occurs on sub-graphs with a smaller number of nodes, when an argumentative graph consists of two unconnected sub-graphs.”  Here “sub-graphs with a smaller number of nodes” means the smaller sub-graph of the two unconnected sub-graphs. We will clarify it in the next version of our paper. \n\n**Response to \"Questions For The Authors\"**:\n\n**Q.a**. As this question is similar to W.b, please back to the answer W.b.\n\n**Q.b**. We agree that the accuracy of the path prediction is not the reason for that as the same contexts are used for the prediction of the path and only the queries are different for different tasks. For more details of the possible reason, please back to our answer W.a.3.\n\n**Q.c**. These 30 examples are instances where with a wrongly predicted path, the model corrects the answer compared with the model when path information is not included. For the other remaining instances, there are no obvious common features. It seems that the model predicted the correct answer even though the path was predicted wrongly. \n\n**Q.d**. In general, we think the reason why including the whole argument graph instead of the reasoning path hurt the performance is that the sequence-to-sequence model is not strong enough to learn the whole graph information. \n\nOur original idea is to use graph information as the representation of the chain of thought. However, as you can see in Appendix D, we tried different ways to get the graph representation but the performance was bad. The main reason we think is that the accuracy (exact match) of the predicted graph is too low (5.86% on the AbstRCT dataset). We think there are mainly two reasons. First, the number of tokens for the representation of a whole graph (from 5 tokens to 26 tokens) is much longer than that of a path (from 1 token to 4 tokens). As shown in Table 6 and Table 8, the accuracy of the path prediction drops significantly with the growth of the path length. When the path length is 4, the accuracy can be only 9.52%. Another reason is that the semantics of a path is in a sequence order which is consistent with the learning method of PLM, while the graph structure is more complex and is not easy to learn in sequence order.\n\nWe will add the analysis above to our paper if it is accepted.\n\n**Q.e**. As none of the baselines use a generative model to solve the argument mining task, teacher forcing is unavailable for these models.", "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": "(S.a) The paper proposes a novel, intuitive, and clearly described method to take advantage of the “chain of thought” structure for AM. \n\n(S.b) The experimental results suggest that this approach, formulated as an MRC task and implemented through the proposed method, could help to learn useful information from the reasoning path, improving the AM performance.\n\n(S.c) The ablation study appropriately tests the contribution of each component and, as highlighted by the authors, the experiments offer useful insight pointing to directions for future research. \n\n(S.d) Overall, the experiments alongside the core idea of the \"chain of thought\" structure behind utilizing MRC, open paths with potentially vast opportunities for future research.\n", "reasons_to_reject": "(W.a) While the experiments are overall well-designed, the results deserve further discussion, investigation, or interpretation. The following are some examples: \n\n- The authors find that including the full argument graph instead of only the path hurts the performance, but this, not necessarily intuitive observation, remains as a footnote referring to an appendix. \n- The authors find that training all subtasks on all the dataset at once (instead of teacher forcing) hurts the performance. \n- There is a clear difference between the two datasets with regards to the impact of warm start. There is no discussion on why PE seems to benefit from warm start on all tasks, while warm start is clearly hurting the ACC task on AbstRCT. \n\n(W.b) Related to the last point, the differences between the two datasets are underexplored. For example, the authors mention that the argument graph is a tree in PE, but not in AbstRCT. This is a potentially important difference, given the core idea of this method. In light of the differences in performance, this deserves more discussion. \n\n(W.c) Speculative or equivocal language could be replaced with more quantitative information. For example, the authors remark on line 504 that path extension “usually occurs on subgraphs with a smaller number of nodes”. This sentence does not give a reader who is unfamiliar with the data or the task a clear idea of what is considered “small” and how often is considered “usually”. This could be clarified with some statistics on the distributions of graph size and occurrence of path extension.\n", "questions_to_authors": "(Q.a) What are the implications of the difference between PE and AbstRCT for the performance of the method on each task, especially in light of the ablation study (e.g. table 9 VS table 10)? \n\n(Q.b) The authors claim on line 428 that “The warm start method improves the performance in most cases”. This is while the performance on ACC on all three test sets in AbstRCT is worse with a warm start. This does not seem to be a random observation, and the reason does not seem clear, especially since path prediction for ARIC does not seem much better than ACC (table 8), i.e., worse path prediction might not be a cause of this difference. What is causing this then? (The cause could be unknown to the authors, but a discussion pointing this out seems appropriate.)\n\n(Q.c) Line 525 reports some statistics on occurrence of path extension and claim replacement: 16 and 6 occurrences (respectively) out of a sample of 30 observations. Are these 30 examples instances of wrong predicted path but correct answer? If so, is there any other feature for the remaining 8 instances that stands out? \n\n(Q.d) (optional) Why and in what ways does including the whole argument graph instead of the reasoning path hurt the performance? This is to some extent discussed in Appendix D, but a brief mention of this in the main text, beyond a footnote, could be helpful.\n\n(Q.e) (optional) Are the baseline models trained via teacher forcing? If not, how do their performances compare with the proposed method without teacher forcing? Or how do they perform if they are trained via teacher forcing?\n", "paper_topic_and_main_contributions": "Viewing the argument structure within a text as akin to a \"chain of thought\", the authors propose to formulate the argument mining (AM) task as a multi-hop machine reading comprehension (MRC) task, allowing the model to learn the argument structure as a sequence of reasoning steps leading to a conclusion. By converting AM into a generative multi-hop MRC task that takes a query and context as its input and outputs an answer plus the predicted path, they enable the model to learn the “chain of thought” and extract the argument structure simultaneously. Additionally, the authors use a bi-directional method to mitigate the order-induced bias. The proposed method and its different components are tested through experimentation on two benchmark datasets. The experimental results show that this method achieves the SOTA on all or most tasks for both datasets, which speaks to the utility of the \"chain of thought\" approach and the corresponding MRC method for argument mining.", "excitement": 4, "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": {}}, {"review_id": "3QKERWywAz", "reviewer": "Reviewer_SLiR", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 2, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your review. We read it carefully and try our best to solve your concerns. The details are shown below.\n\n**Response to \"Reasons To Reject\"**:\n\nThank you very much for your comments! It is a good suggestion to consider the inherent variability of the \"chain of thought\" instead of only using a single path. We will consider that for our future 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": "The results support the efficacy of the model introduced by the authors for this particular task.\n\n", "reasons_to_reject": "One potential constraint of this study lies in its focus on a single path, without considering the inherent variability of the \"chain of thought\".\n\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper aims to transfer the argument mining task into a multi-hop reading comprehension task adopting the innovative concept of “chain of thought”.\nThe contribution of this paper involves the novel utilization of the concept of “chain of thought”, coupled with a comprehensive evaluation process.\n\n", "excitement": 4, "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": {}}, {"review_id": "PyrEqreWmE", "reviewer": "Reviewer_As1i", "summary": "", "questions": "- Could you provide more details on the training process? How many epochs? Any fine-tuned hyperparameters? Did you perform cross-fold validation? That can make your results stronger and more reliable.\n\n- Did you think of implementing your idea as a prompt for large language models? It would be nice to see how that works", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your review. We read it carefully and try our best to solve your concerns. The details are shown below.\n\n**Response to \"Reasons To Reject\"**:\n\nAs mentioned in line 336 to line 343 (“All results on both datasets are averaged scores of three different random seeds and are reported as macro-averaged F1 scores. For the AbstRCT dataset, we use the same train-development-test split as Si et al. (2022). For the PE dataset, we keep the same train-test split and randomly select 10% of the training set for validation, like Bao et al. (2021) do.”), we followed the same evaluation method as previous models so that we could get a fair comparison between our model and the baseline models. Therefore, we did not use cross-fold validation in the evaluation. \n\n[1] Jianzhu Bao, Yuhang He, Yang Sun, Bin Liang, Jiachen Du, Bing Qin, Min Yang, and Ruifeng Xu. 2022. A generative model for end-to-end argument mining with reconstructed positional encoding and constrained pointer mechanism. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10437–10449, AbuDhabi, United Arab Emirates. Association for Computational Linguistics.\n\n[2] Jiasheng Si, Liu Sun, Deyu Zhou, Jie Ren, and Lin Li. 2022. Biomedical argument mining based on sequential multi-task learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics.\n\nAs for the significance test, we agree that it is a good way to improve the reliability of our results. Thus, we conducted a significance test and the results are shown below. \n\n\n|        |         |  ACC   |           |          |  ARIC |           |\n|  ----  | ----  |  ----  | ----  | ----  |  ----  | ----  |\n|   | Neo  |  Gla  | Mix  |Neo  |  Gla  | Mix  |\n| ResArg(avg) |  86.18 | 85.53|  86.74 | 59.15 | 57.23|  60.31|\n| ResArg(Ensemble)  |86.38 |87.13 |87.59 |63.16 |61.86 |68.35|\n| ResAttArg(avg)  | 86.19| 86.26| 87.51| 66.49| 62.68| 63.47 |\n| ResAttArg(Ensemble)  | 87.87| 87.71| 89.70| 70.92| 68.40| 67.66|\n| SeqMT  | 91.89| 92.35| 92.21| 71.24| 73.27| 72.71|\n| MRC_GEN  | **92.76***| **92.62***| **93.97***| **74.97***| **74.28***| **73.87***|\n|        |         |    |           |          |  |           |\n\nTable 1: Overall results on the AbstRCT dataset. Here, Neo, Gla and Mix correspond to the results achieved for the neoplasm, glaucoma and mixed test sets, respectively. The highest scores are emboldened. * indicates statistically significant improvements over the baselines with our model (*: by pair-wise t-test with p < 0.05)\n\n|        |         |  ACC   |           |          |  |   ARI |          |  |   ARC |   |\n|  ----  | ----  |  ----  | ----  | ----  |  ----  | ----  | ----  | ----  |  ----  | ----  |\n|   | Macro |MC |Claim |Premise |Macro |Rel| No-Rel| Macro| Support |Attack |\n| Joint-ILP |82.6 |89.1 |68.2 |90.3| 75.1 |58.5 |91.8 |68.0| 94.7 |41.3 |\n| St-SVM-full |77.6 |78.2| 64.5 |90.2 |- |60.1| -| -| - |-|\n| Joint-PN |84.9 |89.4| 73.2 |92.1 |76.7| 60.8 |92.5| - |-| -|\n| Span-LSTM |87.3| - |- |- |81.1| - |- |79.0 |96.8 |**61.1** |\n| BERT-Trans |88.4 |93.2| 78.8| 93.1| 82.5 |70.6| 94.3| **81.0**| - |-|\n| MRC_GEN |**89.2*** |**94.8***| **79.6*** |**93.2** |**82.7***| **70.9*** |**94.4**| 78.2*| **97.7***| 58.9*|\n|        |         |    |           |          |  |           |  | |   ||\n\nTable 2: Overall results on the PE dataset.* indicates statistically significant difference between the baselines with\nour model (*: by pair-wise t-test with p < 0.05)\n\nPlease note that we cannot conduct the significance test to compare our model with SeqMT on the AbstRCT dataset since their code is not available and we cannot get enough information from their paper for a significance test. Therefore, our significance test is based on other models.  If our paper is accepted, we will add the results of the significance test into our paper.\n\n**Response to \"Questions For The Authors\"**:\n\n**Q.a** As mentioned in line 347, the details of the hyper-parameters are shown in Appendix B due to the limitation of the space. We agree that this type of information is important. If our paper is accepted, we will move it to the main context so that the readers can find it easily. \n\n**Q.b** Thanks for your suggestion. We agree that it is worth trying our idea in large language models. We proposed a generative model to solve the argument mining task mainly because the large language model era is coming. We will try it in the future.", "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": "- Converting the task into a multi-hop reading comprehension task is interesting and well-motivated.\n\n- The experiments are sound.\n\n- The paper is well-written in most of its parts.", "reasons_to_reject": "- The results don't seem very reliable to me, especially since it is not clear whether there was any cross-fold validation performed or significance test", "questions_to_authors": "- Could you provide more details on the training process? How many epochs? Any fine-tuned hyperparameters? Did you perform cross-fold validation? That can make your results stronger and more reliable.\n\n- Did you think of implementing your idea as a prompt for large language models? It would be nice to see how that works", "paper_topic_and_main_contributions": "This paper addresses the argument mining task by transferring it to a multi-hop reading comprehension task (MRC). To this end, the authors process the instances into a quadruple of <query, context, answer, path> based on the task specification. For example, for argument unit classification, the query is the component to be classified, context is the whole essay, the answer is the label of the component (claim, main claim, etc.), and the path is a sequence of components starting from the main component to the query component, representing a chain of thoughts. Given this processed dataset, the authors train a BART model on jointly predicting the path and the answer.\n\nIn a set of experiments, the authors compare their approach to several baselines on two datasets. The results demonstrate a degree of boost gained from the implemented approach.\n\nThe paper is well-written and easy to follow in most of its parts. The experiments conducted, including the selected baselines, are adequate. However, details on the training process and evaluation measures are not clear.\n\nUPDATE: After the authors response that clarified my concerns, I decided to increase my scores.", "excitement": 3, "reproducibility": 2, "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: The reviewers agreed that the proposed method to take advantage of the “chain of thought” structure for Argument Mining is novel and sound. The reviewers highlighted some drawbacks in their reviews (i.e., the experiments and the results lack of key details like on the training process, and deserve a deeper investigation like for the issue of considering full argumentation graphs). The reviewers appreciated the author rebuttal which addressed most of the mentioned issues.
Decision: Accept-Findings