Improving Dialogue Discourse Parsing via Reply-to Structures of Addressee Recognition
Authors: Yaxin FAN
Feng Jiang
PEIFENG LI
Fang Kong
Qiaoming Zhu
Venue: EMNLP 2023 Main (2023)
Keywords: Dialogue Discourse Parsing
Reinforcement Learning
Task-aware Structure Transformer

Abstract: Dialogue discourse parsing aims to reflect the relation-based structure of dialogue by establishing discourse links according to discourse relations. To alleviate data sparsity, previous studies have adopted multitasking approaches to jointly learn dialogue discourse parsing with related tasks (e.g., reading comprehension) that require additional human annotation, thus limiting their generality. In this paper, we propose a multitasking framework that integrates dialogue discourse parsing with its neighboring task addressee recognition. Addressee recognition reveals the reply-to structure that partially overlaps with the relation-based structure, which can be exploited to facilitate relation-based structure learning. To this end, we first proposed a reinforcement learning agent to identify training examples from addressee recognition that are most helpful for dialog discourse parsing.  Then, a task-aware structure transformer is designed to capture the shared and private dialogue structure of different tasks, thereby further promoting dialogue discourse parsing. Experimental results on both the Molweni and STAC datasets show that our proposed method can outperform the SOTA baselines. The code will be available at https://github.com/yxfanSuda/RLTST.

Improving Dialogue Discourse Parsing via Reply-to Structures of Addressce Recognition
Yaxin Fan', Feng Jiang234, Peifeng Li"; Fang Kong', and Qiaoming Zhu'
Schooo of Computer Science and Technology, Soochow University, Suzhou, China
Sccooo of Data Science, The Chinese University of Hong Kong, Shenzhen, China
Shenzhen Research Institute of Big Data, Shenzhen, China 4 University of Science and Technology of China, Hefei, China yxfansudaêstu. suda.
wdu.a.edu.cem edu. cn, lnfffreyjinn@ecuhkeeeee .du. {pfli, kongfang, qmzhu edu. cn
Abstract
reply-to
relation-based
(I) william hi.
Dialogue discourse parsing aims to reflect the relation-based structure of dialogue by estab- lishing discourse links according to discourse relations. To alleviate data sparsity, previous studies have adopted multitasking approaches to jointly learn dialogue discourse parsing with related tasks (e.g.. reading comprehension) that require additional human annotation, thus lim- iting their generality. this paper, we propose a multitasking framework that integrates dia- logue discourse parsing with its neighboring task addressee recognition. Addressee recog- nition reveals the reply-to structure that par- tially overlaps with the relation-basee structure, which can be exploited to facilitate relation- based structure learning. To this end, we first proposed a reinforcement learning agent identify training examples from addressee recognition that are most helpful for dialog discourse parsing. Then, task-aware struc- ture transformer is designed to capture the shared and private dialogue structure of differ- ent tasks, thereby further promoting dialogue discourse parsing. Experimental results on both the Molweni and STAC datasets show that our proposed method can outperform the SOTA baselines. The code will be available at https://gitte www.:ihh.b...oo...eeeeeeeeett fonn//aaa
Cont
(2) william how many people are we waiting for?
QAP
(3) Thomas: hi, i think it's more
Ack
(4) william ok
Ack
(5) Markus : yes, one more
Com
(6) Markus seems there's a hickup logging into the game
Ack
(7) Thomas: that's ok, I not schedule
Figure I: An example of relation-based dialogue dis- course parsing from STAC (right lines) (Asher al., 2016), where Cont, QAP, ACK, and Com refer to Contin- uation, Quextion-answer Pair, Acknowledgement and Comment, respectively. We also present the reply-to structure of addressee recognition (left lines) in this example for better illustration. Dialogue discourse pars- ing reveals the relation-based structure by establishing discourse links on discourse relations, while addressee recognition reveals the reply-to structure by identifying the addressee for each utterance. The black-solid lines indicate the shared structure between the two tasks and the orange-dot lines and the blue-dashed lines indicate the private structure of addressee recognition and dia- logue discourse parsing, respectively.
1 Introduction
Dialogue discourse parsing aims to construct a de- pendency tree on discourse relations to reflect the implicit discourse structure of a dialogue, which is helpful for various downstream tasks, such as reading comprehension (He et al., 2021), meeting summarization (Feng et al., 2021), response gener- ation (Hu al., 2019) and sentiment analysis (Sun et al., 2021).
(RST) (Mann and Thompson, 1988). Otherwise, dialogue discourse parsing represents the dialogue as a dependency tree on the Segmented Discourse Relation Theory (SDRT) (Asher and Lascarides. 2003) and an example is shown in Figure
Dialogue discourse parsing suffers from data sparsity because annotating the links and rela- tions requires a precise understanding of dialogues, which is tine-consuming and expensive. To miti- gate the problem of data sparsity, previous works
Most studies on discourse parsing usually focus on monologue and represent a document with a hierarchical tree on the Rhetorical Structure Theory
"Corresponding author

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explore the complementary knowledge of related tasks facilitate dialogue discourse parsing, in- cluding joint training with reading comprehension (He et al., 2021) or dropped pronoun recovery (Yang et al., 2021). However, the requirements of additional human annotation, such as the ques- tions and answers for reading comprehension, limit the generality.
are updated by each specific task individually. Experimental results on both Molweni and STAC show that our proposed method outperforms the SOTA baselines. Besides, the reinforcement- learning agent can reduce about 80% of the train- ing data of addressee recognition and help dialogue discourse parsing achieve higher performance. In addition, the task-aware structure transformer effectively capture shared and private structures of different tasks, thus further promoting dialogue discourse parsing.
In this paper, we jointly learn dialogue discourse parsing with its neighbor task addressee recogni- tion (Ouchi and Tsuboi, 2016; Zhang et al., 2018; Le et al., 2019) to alleviate the issue of data spar- sity and additional human annotation requirements. Addressee recognition reflects the reply-to struc- ture between different speakers, while dialogue dis- course parsing reveals the oelation-base structure between the same or different speakers. There- fore, the two tasks have both shared and private structures as shown in Figure 1. Our intuition is to leverage shared structures of addressee recog- nition to promote dialogue discourse parsing in a multitasking manner.
2 Background
2.1 Dialogue Discourse Parsing
Dialogue discourse parsing always focuses multi-party dialogue and can be divided into two types, i.c., single-task and multitask. Those single- task approaches mainly employ various encoding and decoding methods for discourse parsing. Shi and Huang (2019) jointly and alternatively pre- dieted the link and relation by incorporating the historical structure. Wang et al. (2021) adopted a structure transformer to incorporate both the node and edge information of utterance pairs. Liu and Chen (2021) trained a joint model by merging the parsing datasets. Fan et (2022) combined dif- ferent decoding methods for discourse parsing. Yu et al. (2022) proposed a second-stage pre-trained task to enhance speaker interaction.
However, two challenges remain when jointly training our task with addressee recognition. The first challenge arises from the fact that examples from addressee recognition are not equally ben- eficial for discourse parsing due to the partially overlapping structure between the two tasks. It becomes crucial to identify the most useful exam- ples from the data-rich addressee recognition to improve multitask learning. The second challenge involves how capture shared and private struc- tures of different tasks. As shown in Figure two tasks have both their shared structure (black-solid lines) and their private structure (orange-dot and blue-dashed lines). Thus, capturing the shared and private structure poses an additional challenge in effectively promoting dialogue discourse parsing.
Multitask approaches focus exploring the ad- vantages of relevant tasks to facilitate discourse parsing. Yang et al. (2021) jointly trained dialogue discourse parsing with dropped pronoun recovery, and He et al. (2021) jointly trained discourse pars- ing with reading comprehension. However, both of them need additional annotated information of the relevant tasks on the basis of discourse parsing, which is time-consuming and costly. In this paper, we propose a multitask framework of dialogue dis- course parsing and addressee recognition, which does not need any additional annotated labels.
To tackle the first challenge. we introduce reinforeement-leammnn agent identify the train- ing examples from addressee recognition that con- tribute the most to dialogue discourse parsing. To address the second challenge, we propose a task- aware structure transformer that effectively exploits the shared and private dialogue structures across different tasks. Our approach involves evaluating the importance of each neuron in the structure trans- former and categorizing them as either task-shared or task-private based on their significance for each task. Subsequently, the task-shared neurons are updated by all tasks, while the fask-privan neurons
2.2 Addressee Recognition
Addressee recognition aims to determine a possible addressee for some utterance. For example, Ouchi and Tsuboi (2016) first proposed the task and cre- ated a large corpus for studying. Le et al. (2019) introduced a who-to-whon model explore the in- teraction of speaker and utterance. Gu al. (2021) proposed several supervupdd tasks to learn who says what to whom.

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Step I: Data Selection on Reinforcement Learning
Actions
Actor
Bi-GRU
BERT
Addresses
Classifier
Selected Examples
Recognition Exemples
Actual Reward
Critic
Expocted
States
Policy Loss
Reward
EL
ST
Step 2: Multitask Learning on TST
Selected
Classifier
Bi-GRU
Examples
BERT
Dizlogge
Discourse
Classifier
Persing
Examples
EL
TST
Figure The framework of our method.
3 Approach
edge information the same time. We feed the dialogue-level context representation H H into Structure Transformer to obtain the structure representation I as follows.
As shown in Figure 2, our method mainly includes two steps: data selection reinforcement learn- ing and multitask learning Task-aware Structure Transformer (TST). In the following sections, first introduce the backbone of our method, fol- lowed by the reinforcement-leaming agent and task- aware structure transformer.
E (w: h. 111)) (w (1-1)) w! ess
vd
exp(z prppz111
(1)
(2)
3.1 Backbone
-== eap (/21
The backbone includes three components: Encod- ing Layer (EL), Structure Transformer (ST), and classifier.
N
(
+ w Eij
(3)
3.1.1 Encoding Layer
where h; and hj (h;, h, H) are the semantic representations the utterance u, and uj within a dialogue, esj is the edge representation between u; and Uj. We use three concatenated feature em- beddings: speaker, turn, and relative distance edge information following previous work (Wang et al., 2021), Wo. where 0 E {q, k, r, }. is the learnable parameter. I is the 1-th layer of Structure Transformer. The dialogue structure representation I - (h h
Given multi-party dialogue with utterances de- noted as {ui, .., (n), we add dummy root uo represent the beginning of dialogue. The ut- terances simply fed into the pre-trained model, such as BERT (Devlin al., 2019), in the form of [CLS][SE]]uu[ [SEP) M1 ... (SEP),,, and the hidden states of [SEP] previous to each utterance is regarded as the corresponding utter- ance representation. Then, a BiGRU was em- ployed on the utterance representation to obtain the dialogue-level context representation H (ho. h,," h,), where H E (n+1))dd
h }. I I (n+1))xd
3.1.3 Classifier
After obtaining the structure representation I, we adopt Multi-Layer Perceptrons (MLPs) to calculate probabilities of links between utterance pairs for both tasks and probabilities of relation types for dialogue discourse parsing as follows.
3.1.2 Structure Transformer
Structure Transformer (ST) (Zhu al., 2019; Liu et al., 2020; Wang et al., 2021) have been widely applied NLP, which is based on Transformer (Vaswani et al., 2017) and considers both node and
St, S, = Softmar(MLP((P : (4)

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We adopt the actor-critic reinforcement paradigms (Konda and Tsitsiklis, 1999; Ye al., 2020; Pujari et al., 2022) and add the reinforeement-lcariing agent to the tail of the backbone. For each example in the addressee recognition training set, we choose the structure where [:] denotes the concatenation operation, St E Raxaxi S, E Rаxxxm, n and m are the number of utterances within a dialogue and the number of the relation type, respectively.
9 L,
1
(F1
R4) x
m Σ
log (la)))
9 1=1
9
L R Fi)
3.2 Data Selection on Reinforcement Learning
The reason for performing data selection is that the partially overlapping structure between both tasks leads to the fact that not all examples in ad- dressee recognition are equally useful for dialogue discourse parsing. Reinforcement learning can au- tomatically select examples from addressee recog- nition that help discourse parsing the most.
(8)
where μl and a are the mean and standard deviations of the F1 score, e is a smoothing constant, aj is an action for the j-th dialogue of the i-th mini-batch decided by the actor, and 1 is the smooth L1 loss. The algorithm for reinforcement learning is shown in Appendix A.
3.3 Task-aware Structure Transformer
Under multitask learning, the structure transformer can only capture the shared structure of different tasks, because either the examples from addressee recognition or dialogue discourse parsing equally update all neurons of W in the structure trans- former at the back-propagation step.
representation of dumpy node (I) as the cor- responding dialogue representation and feed into the agent. For a mini-batch b consisting of m dialogues, the semantic representation of is denoted as ! where H Rmxdd We first feed ( into the actor to decide whether to select
To capture shared and private structures of differ- ent tasks, proposed Task-aware Structure Trans- former (TST) based on structure transformer. Inspired by the task of model pruning (Zhu and Gupta, 2018; Evei et al., 2020), we assume that different neurons in W have different importance to different tasks. Based on this hypothesis, we can divide the neurons into two types: task-shared and task-private. We force those task-shared neurons to participate in the shared structure learning of all tasks while those task-private neurons only focus on some specific tasks. Thus, the key is deter- mine the task-shared and task-private neurons in Wa for each task. To this end, we use a binary mask matrix M E {0, 1)(Wa) to indicate task-shared and task-private neurons for each task. Each element in M corresponds to a neuron in Wo. An element of O indicates that the neuron is task-shared while indicates that the neuron is task musk-private.
the dialogue in mini-batch and the equation is as follows.
P (eof/mma(W.... H. b1)
(5)
where Pi is probabilities of actions for dialogues in mini-batch b, W. and bi are learnable parameters and bias, respectively.
The critic used to compute the expected reward based on the actions of the actor for the mini-bated b as follows.
R (a (Wr "W,,HH W.H.! + b2) + by)) (6)
where R is the expected reward of the mini-batch b, and and á are the Sigmoid and Tanh activation functions, respectively. W, and W. are learnable parameters, respectively, and b2 and by are learn- able biases.
To obtain mask matrix M, we first train the structure transformer on all tasks in a multitasking manner to obtain the initial learnable parameters W Then, we fine-tune W on specific task to evaluate the importance of neurons, and the most important a% of neurons are reserved as the task- private neurons of the task. Intuitively, fine-tuning W on a specific dataset will amplify the magni- tude of the gradient of the important neurons. Thus, we rank the gradient neurons in descending or- der and reserve the most important a% of neurons as the task private neurons. M is obtained by set-
Then, we assign rewards to the agent by eval- uating the performance of discourse links on the validation set of dialogue discourse parsing. the Fj score on the development set for g mini-batches with size m are denoted as {F], F?. FF} and the expected rewards predicted by the critic is de- noted as {R1, R2.... Rg the policy loss is com- puted follows.
F -P

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ting the indices of task-private neurons and the others to O. Once the mask matrix M for all tasks is obtained, we continue to fine-tune the structure transformer on all tasks and only update task-private neurons for each task during the back- propagation step. The whole process is shown in Algorithm 1.
Task Dataset
Train
Valid
Test
UttNum
STAC
1062
111
1-37
DDP
Molweni
9000
500
500
7-14
Hu
311K
5K
5K
6,7
Ou5
461K
28K
32K
5
AR
Oul0
495K
30K
35K
10
Oul5
489K
30K
35K
15
Algorithm TST Learning
Table I: Statistics of Dialogue Discourse Parsing (DDP) and Addressee Recognition (AR). "UuNum' indicates utterance number.
Require: addressee recognition dataset A, dia- logue discourse parsing dataset D, initial learn- able parameters W of structure transformer, and learning rate 7)-
where 0g denotes the parameters of dialogue dis- course parsing to be optimized, y" and and represent golden links and relation types, respectively. is and Tij represent predicted links and relation types, spectively. the number of golden links and the number of relation types.
1: for dataset in {_A, D) do
2: initialize mask M = W
3: fine-tuning W on dataset and obtain gra- dients GO for all neurons in 1
4: sort gradients G in descending order and
For multitask learning, we add the loss of all tasks as follows.
obtain the indices Ie of top a% gradients
5: set M if Ie
6: end for
L Lo
(11)
7: initialize time step ==1
8: for epoch e 2. do
Experimentation
9: for dataset e in (A, D) do
10: for mini-batch j in dataset e do
4.1 Datasets
11:
Wi W 17 G 3 M
We evaluate our proposed model on two dialogue discourse datasets STAC (Asher et al., 2016) and Molweni (Li et al., 2020) and the statistics are shown in Table 1. For STAC, we follow previous work and select 10% of the training dialogues for validation. For addressee recognition, we utilize all training data from four commonly used corpora Hu (Hu et al., 2019) and Ou5/Ou Mu5/Ouu0//ull 10/Ou15 (Ouchi and Tsuboi, 2016), including about 1.75 M dia- logues, to facilitate dialogue discourse parsing in multitasking manner.
12: t ++1
13: end for
end for
15:
end for
16: return W
3.4 Multitask Learning
For optimization of addressee recognition, we min- imize the cross-entropy golden links as follows.
La (0.) = Σ y log (y.)
(9)
4.2 Experimental Settings
We use bert-based-uncasel to initialize the parame- ters BERT. The learning rate of BERT, reinforce- ment learning, and structure transformer are set to le-5, 3e-5, and 3e-4, respectively. The layer and head of the structure-aware transformer are set to and 4, respectively. Performance of adopting differ- ent values of alpha in TST is shown in Appendix B and we set to 0.8 and 0.7 on Molweni and STAC, respectively. The maximum utterance number for Molweni and addressee recognition dataset set to 15. We follow previous work (Wang et al., 2021) and adopt a sliding window to split the dialogue in STAC into chunks and the maximum utterance
where 6, denotes the parameters of addressee recognition be optimized, y² represents the golden links, Yi is the predicted links, and n is the number of golden links.
For optimization of dialogue discourse parsing, we minimize the cross-entropy of golden links and relation types as follows.
(06) = Σ nd log (y:)
(10)
a
m
Σ
llloo (ris)
1

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Molweni
STAC
Model
Link
Rink&Rel
Link
Link& Rel
ChatGPT
59.91
25.25
63.75
23.85
DSM
76.94
53.49
71.99
53.62
SSAM
81.63
58.54
73.48
57.31
Single-task
COMB
80.15
56.60
73.25
57.18
DAMT
82.50
58.91
73.64
57.42
SSP
83.70
59.40
73.00
57.40
Single-ST
81.06
56.81
71.24
55.51
DiscProReco
74.10
57.00
Multitask
DPRC
80.00
57.00
Multi-ST
82.59
58.21
72.31
56.49
Multi-ST Subsed
83.16
58.80
73.06
57.24
Ours
FST
83.75
59.21
73.26
57.47
TST Subsct
85.26
60.91
73.69
57.63
Table 2: Main results on both Molweni and STAC.
number for STAC set to 40. The maximum se- quence length for Molweni, STAC, and addressee recognition dataset are set to 380, 512, and 380. The batch sizes for Molweni, STAC, and addressee recognition dataset are respectively. The epoch for reinforeement learn- ing, structure transformer, and task aware structure transformer are set to 5, 3, and 3, respectively. All experiments are performed using a GeForce RTX 3090 GPU. In this paper, we follow Shi and Huang (2019) and adopt Link Fi and Rel F1 for evaluation. The Link F1 evaluates the performance of link prediction, and Rel Fi evaluates the performance of link and relation prediction.
ture the structure of the single dialogue discourse task. Multitask: 1) DiscProReco (Yang et al., 2021), it proposed to jointly train dropped pronoun recovery and dialogue discourse parsing: 2) DPRC (He et al., 2021), it proposed jointly train read- ing comprehension and dialogue discourse parsing; 3) Multi-ST /wl (our simplified version), it used all addressee recognition datasets and employed a structure transformer to capture the shared struc- ture between addressee recognition and dialogue discourse parsing. 4) Multi-ST Subsct (our simpli- fied version), Similar to Multi-ST Full but using the selected data from all addressee recognition datasets on reinforcement learning.
200, 100, and 200
As shown in Table 2, we report the main re- sults on the Molweni and STAC test sets following previous work (Fan al., 2022; Yu al., 2022). our subset achieves Link F1 of 85.26 and Link&Rel F1 of 60.91 in the Molweni test set, outperforming all previous SOTA systems. On STAC, our T achieves 73.69 on Link Fi and 57.93 on Link&Rel F,, outperforming most of the previous SOTA systems. These results show the effectiveness of our Subset on dialogue dis- course parsing and ensure that dialogue discourse parsing benefit from addressee recognition.
4.3 Baselines and Experimental Results
We compare our method with the following base- lines. Single-task: 1) ChatGPT(Fan and Jiang, 2023): it adopt ChatGPT! for dialogue discourse parsing. 2) DSM (Shi and Huang, 2019), it al- ternately predicted discourse links and discourse relations by incorporating historical structure; 3) SSAM (Wang et al., 2021), it adopted a struc- ture transformer and two auxiliary training signals for parsing; 4) COMB (Liu and Chen, 2021), it trained the model by merging the parsing datasets; 5) DAMT (Fan et al., 2022), combined transition- based and graph-based decoding methods for pars- ing; 6) SSP (Yu et al., 2022), it proposed a second- stage pre-trained task to enhance the speaker in- teraction; 7) Single-ST (our simplified version), it utilized BERT and BiGRU to encode the dia- logues and applicd a structure transformer to cap-
Compared with Single-ST, our simplified ver- sion of Multi-ST is effective for joint training dia- logue discourse parsing with addressee recognition. In addition. Multi-ST Subset can achieve better per- formance than Multi-STrwl by using about 20% and 19% training data of addressee recognition on Molweni and STAC, respectively, indicating the effectiveness of data selection on reinforcement
"haps://opeaai.co//loggg..a

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Dialogue Discourse Parsing
Percentage Private (16)
Addressee Recognition
Molweni
STAC
25
Link
Link&Rel Link
Link&Rel
20-
None
81.06
56.81
71.24
55.51
Hu
82.45
57.87
72.02
56.35
15
Ou5
81.48
56.95
71.34
55.57
Oul0
81.96
57.20
71.69
55.62
Oul5
82.16
57.32
71.76
55,80
Subset
83.16
58.80
73.06
57.24
11.
0u5
Oni0
Oul5
SobiaM Subia
Dataset
Table 3: Performance on dialogue discourse parsing in ablation study for each addressee recognition dataset. 'None' indicates without data of addressee recognition and 'Subset' indicates the selected data from all ad- dressee recognition datasets on reinforcement learning-
Figure 3: Percentage of private structures in different ad- dressee recognition datasets. The private structures are as shown orange-dot lines in Figure I. 'SubsetM and 'SubsetS' indicates the subsets selected from addressee recognition reinforcement learning for dialogue dis- course parsing dataset Molweni and STAC, respectively.
learning. However, Multi-ST can only capture the shared structure and neglect the influence of pri- vate structures of addressee recognition on dialogue discourse parsing. Compared with Multi-ST, our TST Subset captures shared and private structures of different tasks, which can significantly improve the performance of dialogue discourse parsing. No- tably, our approach does not need any additional an- notated information, compared with DiscProReco or DPRC. These results verify the effectiveness of our method.
and 19% data from all four datasets for Molweni and STAC, respectively, help dialogue discourse parsing achieve higher performance. On Molweni, the performance reaches 83.16 and 58.80 on Link Fi and Link&Rel F1 metrics, respectively. On STAC, the performance reaches 73.06 and 57.24 on Link Fi and Link&Rel F1, respectively. These results indicate the effectiveness of data selection by using reinforcement learning.
Our TST performs worse on STAC than on Molweni, with the same trend as other SOTA systems. We attribute this to the fact that more ut- terances of dialogue in STAC increase the difficulty of dialogue discourse parsing as shown in Table 1, addition, the performance of our SStsee has a slight decrease on Link F1 on STAC, com- parison with DiscProReco. We attribute this to the different pre-trained models and joint learning tasks. Moreover, TST only achieves compara- ble performance with the previous systems on the task of addressee recognition because of the small size of the dialogue discourse parsing dataset.
Less private structures of addressee recognition help more Since reply-to structures of addressee recognition and relation-based structures of dis- course parsing partially overlap, we hypothesize that examples with less private structures ad- dressee recognition can better benefit dialogue dis- course parsing. To validate this, we analyze the per- centage of private structures in different datasets, as shown in Figure 3. Among all addressee recog- nition datasets, Hu has the smallest proportion of private structures, accounting for 21%. This corre- sponds to the results in Table 3, where Hu benefits discourse parsing the most. In addition, the sub- sets selected for Molweni and STAC have an even lower proportion of private structures, accounting for 17% and 19%, respectively. This consistent with our intuition that the addressee recognition dataset with less private structure can facilitate dia- logue discourse parsing better.
5 Analysis
5.1 Effectiveness of Data Selection
Ablation Study of addressee recognition datasets To reveal the effectiveness of adopting reinforcement learning for data selection, we conduct ablation studies the four addressce recognition datasets, shown in Table 3. We can see that all addressee recognition datasets can improve the performance of discourse parsing and the Hu dataset contributes the most. Subsets on reinforcement learning that only select around 20%
5.2 Effectiveness of TST
A Ablaionn study of TST To illustrate the effective- ness of TST, we compare it with several variants as shown in Table 4. Prune-ST indicates that we reserve the lask-private neurons for each task and

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STAC
Ours SSP Ground Truth
350
300
250
200
150
100
0
Contimation
QAP
Contrat
Q-Eaab
Expl Explanaion
Comment
Background
Reall
Correction
Parallel
Altemaionn
Condtional
Clar Q
ACK
Elaboration
Naration
Figure 4: The comparison of correct relation prediction between our TST Subset and SOTA system SSP STAC.
Molweni Link
STAC
capture both shared and private structures signifi- cantly, we analyze the accuracy of both shared and private structures in dialogue discourse parsing, as shown Table 5. We can see that Multi-ST Full can promote the shared structure performance but lead to slightly decline in private structure. We attribute this to the negative effect of private struc- tures of addressee recognition on the private struc- ture learning of dialogue discourse parsing. Be- sides, Multi-STswbset can promote the performance of private structures in discourse parsing by re- ducing private structures addressee recognition. Furthermore, our TST demonstrates the capability to promote the performance of shared and private structures in dialogue discourse parsing through joint learning with either the Full data of addressee Subset selected from addressec recognition using reinforcement learning. These results prove the effectiveness of our TST.
Model
Len Link Link& Rel
Prune-ST 65.15
33.32
61.04
32.87
Random-ST 82.29
57.85
72.23
56.50
Reversed-ST 79.15
53.87
70.82
52.09
TST 85.26
60.91
73.69
57.63
Table 4: Ablation study Link and Link&Rel Fi perfor- mance of TST. Subsets selected from addressee recog- nition was adopted for all experiments.
Accuracy
Method
Molweni
STAC
Shared
Private Shaerd Private
None
80.13
54.49
63.01
71.61
Multi-ST Full
81.60
53.90
64.38
70.33
Multi-STsubaed
82.00
57.48
65.34
72.12
recognition
FST 83.33
59.87
64.66
73.17
TST subeet
84.46
63.47
65.48
75.45
Table 5: Accuracy of shared and private structures in dialogue discourse parsing. The shared and private struc- tures are shown in black-solid and blue-dashed lines in Figure 1, respectively.
5.3 Analysis on Improvement of Relation Performance
Given that relation correct and only if both link and relation type is predicted correctly, the improvement in relation performance can be at- tributed to the success of link prediction. To ex- plore what kinds of relations benefit the most from rank the gradients of neurons and reserve the low- est a % neurons as task-private neurons. We can see that the performance decreases sharply when adopting Prune-ST ST in comparison with our TST, which indicates the essential of shared neurons. When adopting Random-ST or Reversed-SZ, the performance decreases significantly in comparison with our TST, which indicates the effectiveness of our approach that reserving the top a neurons according to the gradients as task-private neurons.
abandon the shared neurons. Random-ST indicates that we randomly select a % of specific neurons for each datasct. Reversed-ST indicates that we
our method, we analyze the number of correct re- lation predictions of STAC shown in Figure 4. We compare our method Subset with the SOTA system SSP (Yu et al., 2022) that uses the same pre- trained model as us. We can see that our method outperforms the baseline on some high- higu-resource relations like Question-Answer_PPe _air (QAP), Com- ment. Acknowledgement (ACK), Continuation and Elaboration. The improvement of these relations mainly derives from the success of link prediction. Besides, both our TST TST Subsct and SSP hardly pre- diet the low-resoue resource relations, indicating that the low-resource relations cannot benefit from the im-
TST can significantly capture both shared and private structures To explore whether TST can

---

(1h) At anyone have any idea why ubuntu would boot correctly after hanging during one boot, and versa
baselines and our method. The dialogue and dis- course structures are shown Figure 5. In the an- notation, we can observe that ue, 47, and ug that all uttered from speaker A form the Queweion_Answer Ansser Pair relation with M5 uttered from speaker B, which is shared with the reply-to structure of addressce recognition. Although SSP enhanced the speaker interaction, U6-2y is still recognized incorrectly. Besides, our simplified version of Single-ST only considers the relation-basse based structure of dialogue discourse parsing, leading to the incorrect recog- nition u6-17 and U7-Ug. However, our TST recognizes U5-U6. U5-U/7, and u5-tig correctly by leveraging the reply-to structure of addressee recog- nition in multitasking manner, which illustrates the effectiveness of our method.
(0r) Bowhen hangs, what does say before
hanging itself??
(0g) A: lact, think will boot after hibernating
(0:) langs blank dead ) screen
(0u) A: appears to be just gnome is loading
(1g) B: can you get a login prompt by pressing
ctrlaltft
hangs"
(11) A: no. and ctri-alt-dee del will not reboot, either
(u:) A: nothing happens it still says network:0
disabled when push button
(9a) A: when reboot the computer manually
recover from the hang, it hoots fine
(1,) B: can you copy FII FPATH pustie? ?
(a) Dialogue
(h) Annotation
6 Conclusion
In this paper, we proposed to improve dialogue dis- course parsing by leveraging reply-to structures of addressee recognition in multitasking manner. To this end, we first adopt a ceiforcementtlearning agent to identify training examples from addressee recognition that help dialogue discourse parsing the most. Then, we proposed task-aware struc- ture transformer capture the shared and private structures of different tasks. Experimental results on Molweni and STAC verify the effectiveness of our method. In the future, we will focus on how to incorporate multiple relevant tasks to improve dialogue discourse parsing.
(c) SSP
(d) Single-ST
Limitations
(e) Subset
Our work has several limitations, which we aim to address in our future work. First, since method to promote the target task with the data of the neighbor task, we can certainly use the data of the dialogue discourse parsing to promote addressee recognition. However, due to the much smaller size of the corpus of dialogue discourse parsing than addressee recognition, the performance of the addressee recognition is not improved significantly. Second, method focuses on the improvement of link prediction, and the improvement of relation prediction mainly derived from the success of link prediction due to the cascade nature. In the future, we will continue to refine our approach to overcome the above shortcomings.
Figure 5: (a) a dialogue from Molweni. (b) the discourse structure annotated manually. (c)(() struc- tures predicted by SSP, Single-ST. and our TST Subeets respectively. Solid, dashed, dashed-dotted, red dot- ted lines denote the relations Question-Answer Pair, Question_Flaborreee Flaboration, Clarification Quesstinn_eeennn and Continuation, respectively.
provement of link prediction, and more attention required in the future. The same phenomenon can be observed on Molweni, as shown in Appendix C.
5.4
Case Study
To better illustrate the advantage of our method, we draw the discourse structures predicted by several

---

Acknowledgements
Asia Conference on Language, Information and Com- putation, pages 598-608.
The authors would like to thank the three anony- mous reviewers for their comments on this paper. This research was supported by the National Natu- ral Science Foundation of China (Nos. 62376181, 62276177, 17, and 62276178), and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
Wenpeng Hu, Zhangming Chan, Bing Liu, Dongyan Zhao, Jinwen Ma, and Rui Yan. 2019. GSN: A -rrph-SStucttued Network for Multi-Party Dialogues. In Proceedings of the 30th International Joint Con- ference on Artificial Intelligence, pages 5010-5016.
Vijay Konda and John Tsitsiklis. 1999. Actor-Critic Al- gorithms. In Proceedings of the Advances in Neural Information Processing Systems, pages 1008 1014..
References
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Nicholas Asher, Julie Hunter, Mathieu Morey, Farah Benamara, and Stergos Afantenos. 2016. Discourse Structure and Dialogue Acts in Multiparty Dialogue: the STAC Corpus, In Proceedings of the 10th In- ternational Conference on Language Resources and Evaluation, pages 2721-2727.
Nicholas Asher and Alex Lascarides. 2003. Logics of conversation. Cambridge University Press.
Jiaqi Li, Ming Liu, Min- Yen Kan, Zihao Zheng, Zekun Wang, Wenqiang Lei, Ting Liu, and Bing Qin. 2020. Molweni: A Challenge Multiparty Dialogues- based Machine Reading Comprehension Dataset with Dis- course Structure. In Proceedings of the 28th Inter- national Conference on Computational Linguistics, pages 3642-2652.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Un- derstanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pages 4171-4186,
Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, and Yang Liu. 2020. Setrieval-Augmentt Gen- eration for Code Summarization via Hybrid GNN. In Proceedings of the International Conference on Learning Representations, pages 1-16.
Utku Evei, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen. 2020. Rigging the Lottery: Making All Tickets Winners. In Proceedings of the 37th International Conference on Machine Learning, pages 2952.
Yaxin Fan and Feng Jiang. 2023. Uncovering the poten- tial of chatgpt for discourse analysis in dialogue: An empirical study. arXiv preprint arXiv:2305.0833
Zhengyuan Liu and Nancy Chen. 2021. Improving Multi-Party Dialogue Discourse Parsing via Domain Integration. In Proceedings of the 2nd Workshop Computational Approaches to Discourse, pages 122-127.
Yaxin Fan, Peifeng Li, Fang Kong, and Qiaoming Zhu.
2022. A Distance- Aware Multi-Task Framework for Conversational Discourse Parsing. In Proceedings of the 29th International Conference on Computational Linguistics, pages 912-921.
William C Mann and Sandra A Thompson. 1988. Rhetorical Structure Theory: Toward functional theory of text organization. Text- pees--nterdiccilinary Journal for the Study of Discourse, 8(3):243-22) 2-11.
Xiachong Feng. Xiaocheng Feng. Bing Qin, and Xin- wei Geng. 2021. Dialogue Aware Graph Model and Data Augmentation for Meeting Summa- rization. In Pruceedings of the 30th International Joint Conference Artificial Intelligence, pages -8144
Hiroki Ouchi and Yuta Tsuboi. 2016. Addressce and Response Selection for Multi-Party Conversation. In Proceedings of the 2016 Conference on Empir- ical Methods in Natural Language Processing, pages 2133 2143.
Jia- Cia-ChenCe Gu, Chongyang Tao, Zhenhua Ling, Can Xu,
Xiubo Geng, and Daxin Jiang. 2021. MPC-BERT Pre-Trained Language Model for Multi-Party Con- versation Understanding In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the I 11th International Joint Confer- ence on Natural Language Processing, pages 3682- 3692.
Rajkumar Pujari, Erik Oveson, Priyanka Kulkarni, and Elnaz Nouri. 2022. Reinforcement guided multi-task learning framework for low-resource stereotype de- tection. Proceedings of the 60th Annual Meeting the Association for Computational Linguistics ( (Vol- ume 1: Long Papers), pages 6703-6712.
Zhouxing Shi and Minlie Huang. 2019. A Deep Se- quential Model for Discourse Parsing on Multi-Party Dialogues. Proceedings of the AAAI Conference on Artificial Intelligence, pages 7007-7014.
Yuchen He, Zhuosheng Zhang, and Hai Zhao. 2021. Multi-tasking Dialogue Comprehension with Dis- course Parsing. In Proceedings of the 35th Pacific

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Yang Sun, Nan Yu. and Guohong Fu. 2021. Discourse- Aware Graph Neural Network for Emo- tion Recognition in Parti Conversation. In Findings of the Association for Computational Lin- guistics: EMNLP 2021, pages 2949-2958.
90.0
Molweni
STAC
86.0
80.0
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Proceedings of the Advances in Neural Information Processing Systems, pages
75.0
70.0
Ante Wang. Linfeng Song, Hui Jiang, Shaopeng Lai, Junfeng Yao, Min Zhang, and Jinsong Su. 2021. Structure Self- Selr-Aware Model for Discourse Parsing on Multi-Pant Party Dialogues. In Proceedings of the 30th International Joint Conference on Artificial Intelli- gence, pages 3943-3949.
Alpha
Figure 6: Link F1 on the test set of Molweni and STAC with different values of alpha.
recognition, the actor-network AN determines whether or not to select and the critic-network com- putes the expected reward Rdev based on actions of the actor. Then, the selected examples are used to train the backbone BN and obtain actual reward Frev Finally, the parameters of netor- AN and critic-network CN are updated. The selected examples (byedent brelect ..r are returned after training.
Jingxuan Yang. Kerui Xu, Jun Xu, Li. Sheng Gao, Jun Guo, Nianwen Xue, and Ji-Rong Wen. 2021. A Joint Model for Dropped Pronoun Recovery and Con- versational Discourse Parsii Chinese Conversational Speech. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguisties and the 11th International Joint Conference on Natural Language Processing, pages 1752-1763.
Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng. Feng Wang, Jun Zhang, and Huajun Chen. 2020. Zero-shot text classification via reinforced self-training. In Proceedings of the 58th Annual Meeting of the Association for Compu- National Linguistics, pages 3014-3024.
Algorithm 2 Data Selection on Reinforcement
Learning
Require: addressee recognition dataset A, valida- tion set of dialogue discourse parsing Ddler backbone BN, actor network AN, and critic network CNN
Nan Yu, Guohong Fu, and Min Zhang. 2022. Speaker- Aware Discourse Parsing on Parti-Party Dialogues. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5372-5382.
for epoch e = 1, 2, ..... do
2:
for mini- batch in do
Rui Zhang, Honglak Lee, Lazaros Polymenakos, and Dragomir Radev. 2018. Addressee and Response Selection in Multi-Party Party Conversations With Speaker Interaction RNNs. In Proceedings of the AAAI Con- ference on Artificial Intelligence, pages 5690-5697.
3: AN make select or not decision for each example in b
CN computes expected reward Rdev based on examples selected by AN train BN on the selected mini-batch sub- set b grelect
Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, and Guodong Zhou. 2019, Modeling Graph Structure in Transformer for Better AMR-to- Generation. In Proceedings of the 57th Annual Meet- ing of the Association for Computational Linguistics and the 1 1th International Joint Conference on Natu- ral Language Processing, pages 5459-5468.
evaluate BN on Deer and obtain Fyle compute reinforcement loss based on prle and Rd
7:
8:
update the parameters of actor network AN and critic network CN
Michael Zhu and Suyog Gupta. 2018. To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression. In Proceedings of the Inter- national Conference on Learning Representations, pages 1-10.
9: end for
10: end for
11: return {bgelect .g..
A Algorithm of Data Selection on Reinforcement Learning
B Performance of TST with Different Values of Alpha
Algorithm/ illustrates the process of selecting ex- amples from addressee recognition by reinforce- ment learning. For each example of addressee
The performance on the test set of Molweni and STAC as alpha changes as shown in Figure 6. We

---

Molweni
Trudh
1000
000
200
QAP
Contred
Qeibet
Explasatin
Conmint
Bas Backprurd
Rovatr
Commioy
Phoatel
Alsimate
Goalaned
Ctet a
ACK
Naramon
Figure 7: The comparison of correct relation prediction between our TST Subset and SOTA system SSP Molweni.
can see that the performance of link prediction reaches the peak when alpha is set to 0.8 and 0.7 Molweni and STAC, respectively.
Relation Performance on Molweni
Figure 7 shows the performance of relation on Molweni. We can observe that our method outper- forms the baseline some high nigh-resource relations like Question-Answer_ Pair (QAP), Comment, and Clarification_quee question (Cla_gr q). However, both our TST Subset and SSP hardly predict the low-resource relations.


Reviews:
[{"review_id": "lVi6BEukjV", "reviewer": "Reviewer_nH36", "summary": "", "questions": "- Could you provide some insights/numbers on how dialogue discourse parsing performance will impact downstream tasks?\n- Could you provide more details on multi-task training for Equation 11, as these two tasks have datasets at different scales?", "limitations": "", "rating": 3, "confidence": 3, "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 valuable comments and suggestions.\n\n**Q1: Could you provide some insights/numbers on how dialogue discourse parsing performance will impact downstream tasks?**\n\nA1: The specificity of dialogue lies in the segmentation of sentences (utterances) between parties in communication. These utterances are not independent but rather are part of a larger dialogue discourse unit. Thus, the structure of discourse between utterances, particularly in multi-party dialogues, can reveal the significance of historical discourse structure in a dialogue and is a crucial foundation for downstream tasks, thereby improving their performance. \n\nPrevious research has demonstrated that modeling dialogue discourse structures can enhance downstream tasks. Regarding response generation, Hu et al. [1] argue that it is inadequate to encode utterances sequentially, as many real-world dialogues involve multiple interlocutors. They propose a Graph-Structured neural Network to capture directly the discourse structures in multi-party dialogues. Experimental results reveal that incorporating discourse structures can result in a 2.27 BLEU enhancement on Ubuntu Dialogue Corpus. Regarding meeting summarization, Feng et al. [2] propose that it is preferable to model the varying relations among distinct utterances rather than considering the meeting as a linear progression. They present a Dialogue Discourse-Aware Meeting Summarizer explicitly modeling the interaction between utterances in a meeting by modeling different discourse relations parsed by the Deep Sequential Model (DSM) [3]. By integrating discourse structures, there was a 1.67 and 0.64 improvement in ROUGE-L on AMI and ICSI, respectively. In sentiment analysis, Sun et al. [4] argue that being aware of dialogue discourse structures is better than only capturing their sequential information. They propose a discourse-aware graph neural network to model these structures. Ablation studies show that discourse structures can improve Average-F1 by 1.88, 1.78, and 1.00 on MELD, EmoryNLP, IEMOCAP, respectively.\n\n**Q2: More details on multi-task training for Equation 11.**\n\nA2: In this paper, we set the loss weight ratio for both tasks as 1:1, consistent with the approach used by He et al. [5]. Although we also experimented with different weight ratios, our preliminary results suggested no significant improvement in the parsing task. Therefore, we adopt the multi-task training of He et al. [5] and refrain from further exploring weighted loss optimization.\n\n[1] Hu, W., Chan, Z., Liu, B., Zhao, D., Ma, J. and Yan, R., 2019. GSN: A graph-structured network for multi-party dialogues. arXiv preprint arXiv:1905.13637.\n\n[2] Feng, X., Feng, X., Qin, B. and Geng, X., 2020. Dialogue discourse-aware graph model and data augmentation for meeting summarization. arXiv preprint arXiv:2012.03502.\n\n[3] Shi, Z. and Huang, M., 2019, July. A deep sequential model for discourse parsing on multi-party dialogues. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 7007-7014).\n\n[4] Sun, Y., Yu, N. and Fu, G., 2021, November. A discourse-aware graph neural network for emotion recognition in multi-party conversation. In Findings of the Association for Computational Linguistics: EMNLP 2021 (pp. 2949-2958).\n\n[5] He, Y., Zhang, Z. and Zhao, H., 2021. Multi-tasking dialogue comprehension with discourse parsing. arXiv preprint arXiv:2110.03269.", "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 proposed framework utilizes large addressee recognition datasets (>300K conversations) to mitigate the data sparsity issue for the dialogue discourse parsing (<10K conversations) task, and improves the state-of-the-art performance on this task.\n- The approach might inspire people working on other tasks regarding utterance relations.", "reasons_to_reject": "- The contribution is incremental. The paper mentioned the purpose of dialogue discourse parsing is to benefit downstream tasks like reading comprehension, response generation, and sentiment analysis. However, it's unclear how significant the marginal improvement is on these downstream tasks, which do not suffer from data sparsity issues.", "questions_to_authors": "- Could you provide some insights/numbers on how dialogue discourse parsing performance will impact downstream tasks?\n- Could you provide more details on multi-task training for Equation 11, as these two tasks have datasets at different scales?", "paper_topic_and_main_contributions": "To tackle the data sparsity issue for dialogue discourse parsing, this paper utilizes datasets from addressee recognition in a multi-task setting to boost the performance of dialogue discourse parsing.", "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": {"Missing_References": "n/a", "Typos_Grammar_Style_and_Presentation_Improvements": "n/a"}, "extra_text": {}}, {"review_id": "LWBZRq1XSq", "reviewer": "Reviewer_AseC", "summary": "", "questions": "Question A. You use the gradient as the metric to divide the task-private and shared neurons. Why do you choose *gradient* and what other factors can be considered?\n\nQuestion B. The joint training of discourse parsing and addressee recognition can improve the performance of discourse parsing. So, does the joint training also benefit to the performance of addressee recognition?\n\nQuestion C. How do you think the reasons that ChatGPT performs extremely worse?\n\n", "limitations": "", "rating": 4, "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 valuable comments and suggestions.\n\n**Q1: Why do you choose gradient as metrics and what other factors can be considered?**\n\nA1: There are various factors used to assess neuron significance, such as weight, gradient, activation, and activation sparsity. Neuron weight and gradient are commonly used and effective methods in previous studies. We conducted preliminary experiments using both weight and gradient methods and found that the gradient performs better. The potential explanation is that gradient information can reveal the neurons’ adaptability of the model to the dialogue data, thus enabling a more accurate representation of the data's characteristics.\n\n**Q2: Does the joint training benefit the performance of addressee recognition?**\n\nA2: Our model only achieves comparable performance with the previous systems on addressee recognition. We attribute this to the small size of the dialogue discourse parsing dataset as illustrated in lines 420-423. We suggest that joint training can enhance the performance of addressee recognition  if the scale of the dialogue discourse parsing dataset approaches that of the addressee recognition dataset.\n\n**Q3: The reasons for ChatGPT performs extremely worse.**\n\nA3: As the cited paper [1] pointed out, dialogue discourse parsing is a relatively complex task which can be treated as a spanning tree problem and it is difficult for ChatGPT to understand the rhetorical structures. Moreover, some work [2, 3] also pointed out that ChatGPT performs poorly in some complex tasks. In addition, we have two private views. Firstly, the seq2seq paradigm (the next token is generated based on previous tokens, and the closer tokens have a more significant impact.) of ChatGPT is linear, both for pretraining and instruction tuning. Thus, ChatGPT can understand the linear topic structures better, but performs poorly in hierarchical discourse structures, as shown in the experimental results in the cited paper [1]. Secondly, it could be that no similar task to discourse parsing in the phase of instruction tuning, leading to the poor performance in the out of domain scenario.\n\n**Q4: Missing the description of ACK in the caption of Figure 1.**\n\nA4: Thanks for your careful review. ACK is short for Acknowledgement. We will introduce it in our revised paper.\n  \n[1] Fan, Y. and Jiang, F., 2023. Uncovering the potential of ChatGPT for discourse analysis in dialogue: an empirical study. arXiv preprint arXiv:2305.08391.\n\n[2] Liu, H., Ning, R., Teng, Z., Liu, J., Zhou, Q. and Zhang, Y., 2023. Evaluating the logical reasoning ability of ChatGPT and gpt-4. arXiv preprint arXiv:2304.03439.\n\n[3] Gao, J., Zhao, H., Yu, C. and Xu, R., 2023. Exploring the feasibility of ChatGPT for event extraction. arXiv preprint arXiv:2303.03836.", "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 is clearly written and well organized. It is easy for readers to understand and follow up.\n\n2. Straightforward and effective method. It is a common solution to adopt multi-task learning to jointly improve the dialogue discourse parsing and the related task. Advanced than the common solution, the authors propose the important example selection and the task-aware structure Transformer is well-motivated and effective.\n\n3. Comprehensive evaluations.", "reasons_to_reject": "I do not see any rejection reasons so far.  But I have questions that waiting for the author response.", "questions_to_authors": "Question A. You use the gradient as the metric to divide the task-private and shared neurons. Why do you choose *gradient* and what other factors can be considered?\n\nQuestion B. The joint training of discourse parsing and addressee recognition can improve the performance of discourse parsing. So, does the joint training also benefit to the performance of addressee recognition?\n\nQuestion C. How do you think the reasons that ChatGPT performs extremely worse?\n\n", "paper_topic_and_main_contributions": "This paper attempts to improve the performance of discourse parsing by jointly optimizing the neighboring task, addressee recognition, in a multitasking training manner. The motivation is that the *reply-to* structure in the addressee recognition task is similar to the relation-based structure in the discourse parsing task. To achieve the goal, a two-step method is presented. The first step is to select the most suitable examples of addressee recognition via a reinforcement learning agent. The second step is to jointly optimize the two tasks via a task-aware structure Transformer. Experiments on STAC and Molweni datasets show the effectiveness of the proposed method. Comprehensive ablation studies and analyses are also provided.", "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": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "Missing the description of *ACK* in the caption of Figure 1.", "Justification_for_Ethical_Concerns": "N/A"}, "extra_text": {}}, {"review_id": "QxVjaFvzgW", "reviewer": "Reviewer_hgfu", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "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 positive comments!", "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 strengths of this paper are:\n\n1. The proposed multitasking framework integrates dialogue discourse parsing with addressee recognition, which does not require any additional annotated labels and can alleviate the problem of data sparsity in dialogue discourse parsing.\n2. The paper proposes a reinforcement learning agent and a task-aware structure transformer to improve the performance of dialogue discourse parsing.\n3. The proposed method outperforms the state-of-the-art baselines on both the Molweni and STAC datasets.\n\nIf this paper were to be presented at the conference or accepted into Findings, the main benefits to the NLP community would be:\n\n1. The proposed multitasking framework can be applied to other related tasks to facilitate discourse parsing and improve the performance of NLP models.\n2. The reinforcement learning agent and task-aware structure transformer can be used in other NLP tasks to capture shared and private structures of different tasks and improve the performance of NLP models.\n3. The proposed method can be used to develop more accurate and efficient dialogue systems, which can benefit various applications such as customer service, personal assistants, and chatbots.", "reasons_to_reject": "N/A", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a multitasking framework that integrates dialogue discourse parsing with addressee recognition to improve the performance of dialogue discourse parsing. The paper addresses the problem of data sparsity in dialogue discourse parsing, which requires precise understanding of dialogues and is time-consuming and expensive. \n\nThe main contributions of the paper are:\n1. Proposing a reinforcement learning agent to identify training examples from addressee recognition that are most helpful for dialogue discourse parsing.\n2. Designing a task-aware structure transformer to capture the shared and private dialogue structure of different tasks, thereby further promoting dialogue discourse parsing.\n3. Outperforming the state-of-the-art baselines on both the Molweni and STAC datasets.", "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": {}}]

Author rebuttal:

Meta-review: The paper presents a multitasking framework that combines dialogue discourse parsing and addressee recognition to address the issue of data sparsity in dialogue discourse parsing. It introduces a reinforcement learning agent to identify the most beneficial training examples from addressee recognition, and a task-aware structure transformer to optimize the performance for both tasks. The method has shown superior performance over state-of-the-art baselines on the Molweni and STAC datasets.

Pros:
The multitasking approach doesn't need extra annotated labels so it alleviates data sparsity in dialogue discourse parsing.
The incorporation of a reinforcement learning agent enhances dialogue discourse parsing.
State-of-the-art performance on two datasets.
The techniques proposed can be potentially applied to other NLP tasks, benefiting various applications like chatbots and personal assistants.
The paper is described as being well-written, organized, and straightforward.

Cons:
The significance of the improvements for downstream tasks, especially those without data sparsity challenges, is not well-demonstrated.
Decision: Accept-Main