Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing
Authors: Wei Li
Luyao Zhu
Wei Shao
Zonglin Yang
Erik Cambria
Venue: EMNLP 2023 Findings (2023)
Keywords: dialogue discourse parsing
emotion recognition in conversations
self-supervision
Soft-window triangular mask

Abstract: Dialogue discourse parsing is a fundamental natural language processing task. It can benefit a series of conversation-related downstream tasks including dialogue summarization and emotion recognition in conversations. However, existing parsing approaches are constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks. To this end, we propose to introduce a task-aware paradigm to improve the versatility of the parser in this paper. Moreover, to alleviate error propagation and learning bias, we design a graph-based discourse parsing model termed DialogDP. Building upon the symmetrical property of matrix-embedded parsing graphs, we have developed an innovative self-supervised mechanism that leverages both bottom-up and top-down parsing strategies. This approach allows the parsing graphs to mutually regularize and enhance each other. Empirical studies on dialogue discourse parsing datasets and a downstream task demonstrate the effectiveness and flexibility of our framework.

Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing
Wei Li", Luyao Zhu", Wei Shao', Zonglin Yang" and Erik Cambria
"Nanyang Technological University, Singapore "City University of Hong Kong, Hong Kong SAR {wei008, luyao001, zonglin@01 Jêe. ntu, edu. sg wei ci cityu. edu. hk, cambriaentu. edu. sg
Abstract
As: What What's the matter sally you look so unhappy?
dowa
By: had bad day yesterday
Dialogue discourse parsing is a fundamental natural language processing task. It can bene- fit series of conversation-relate downstream tasks including dialogue summarization and emotion recognition conversations. However, existing parsing approaches are constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks. To this end, we propose to introduce a task- aware paradigm to improve the versatility of the parser in this paper. Moreover, to alleviate error propagation and learning bias, we design a graph-based based discourse parsing model termed DP, Building upon the symmetrical prop- erty matrix- embedded parsing graphs, we have developed an innovative supervisdd mechanism that leverages both bottom-up and top-down parsing strategies. This approach al- lows the parsing graphs mutually regularize and enhance each other. Empirical studies on dialogue discourse parsing datasets a down- stream task demonstrate the effectiveness and flexibility of our framework!
Ag what happened?
EXP
QAE
Ba: went to the library lost, my bag
B
Comment
A Y our bag. Did you get back?
BAP
up
B5: went back for it but was already gone!
An example of a dialogue dependency graph for dailydialog (Li al.. 2017) conversation snippet. Here A and B are two speakers with subscripts indicating the turn of the dialogue. "QAP", "CQ", "EXP" and "Comment" stand for dependency relations.
Figure I:
The parsed dependencies between utterances should form a Directed Acyclic Graph (DAG). For example, dependent utterance B3 in Fig. depends on head utterances B1 and Ag in "Explana- tion" and "QAP" relations, respectively. The early neural-based paradigm (Shi and Huang, 2019) for the DDP task involved sequentially scanning the utterances in dialogues and subsequently predict- ing dependency links and corresponding relation types, which is prone to the severe error propaga- tion issue (Wang al., 2021), ), the learned rep- resentations relied on historical predictions. This observation inspired to formalize DDP from a graph perspective, where the prediction of links and relations for each pair of utterances is independent of the others.
1 Introduction
Dialogue discourse parsing (DDP) plays an essen- tial role in the field natural language process- ing (NLP). serving as a foundational task and re- ceiving increasing attention from the research com- munity (Shi and Huang, 2019; Yang et al., 2021; Yu et al., 2022). The formulation of the DDP task is rooted in the Segmented Discourse Relation The- ory (Asher and Lascarides. 2003), distinguishing from the Rhetorical Structure Theory (Mann and Thompson, 1988) and the Penn Discourse Relation Theory (Prasad et al., 2008) that primarily under- pins text-level discourse parsing (Afantenos et al., 2015). The primary objective of DDP is to recog- nize the links and relations between utterances in dialogues.
Existing methods (Wang et al., 2021) resorted to the bottom-up strategy for DDP, where for each dependent utterance, the parser retrieved only one head utterance. These models (He et al., 2021) may suffer from measurement bias (Mehrabi et al., 2021) the training labels are distorted with the one-head retrieval for samples with multi-head la- bels. Furthermore, these multi-head labels serve as long-tailed data, and overlooking the long-tailed data may result in a biased distribution estimation during training (Wang et al., 2022).
hnttps://githee httpe///...cmmsmmiiimttt///allaa

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inspires us to keep the multi-head labels for bottom-up strategy training. Moreover, Yang et al. (2021) and Yu et al. (2022) used external knowl- edge or other NLP tasks to mitigate algorithmic bias (Mehrabi et al., 2021) during training. Nev- ertheless, they omitted the potential of leveraging internal structure for regularization. While the top- down strategy can be effective in sentence pars- ing (Koto al., 2021), it was rarely exploited in DDP due the requirement of predicting multiple dependents for each head in dialogues. Despite the opposite directions of the two strategies, there exists strong correlation between the relevance scores of the same utterance pair in both strategies. It leaves room for reciprocal structural supervision between bottom-up and top-down strategies.
To enhance the adaptability of DialogDP for downstream tasks, we propose novel paradigm in- volving fine-tuning with task-specific re-annotated relations. We validate the effectiveness of our task- aware paradigm on downstream ERC. The contri- butions of this paper are:
We propose new DDP model that explicitly captures structures of dependency graphs with bottom-up and top-down strategies, avoiding sequential error propagation.
Bidirectional self-supervision with an SWT mask is devised to alleviate the learning bias.
Our parser surpasses baselines on benchmark datasets and task-aware DialogDP demon- strates superior effectiveness in handling downstream tasks.
Mcanwhile, as a foundational NLP task, DDP has proved beneficial for downstream tasks in- cluding dialogue summarization (Chen and Yang, 2021) and emotion recognition in conversa- tions (ERC) (Li et al., 2022, 2023). Neverthe- less, existing parsers are constrained by predefined relation types, posing potential obstacle to the parser's aduptability for downstream tasks. For example, existing relations like narration and back- ground do not/ or rarely exist in the ERC datasets. addition, a well-designed DDP fine-tuned with twsr- dependency labels can capture emotion shifts, which benefits the downstream ERC task.
2 Related Work
DDP aims to analyze a conversation between two or more speakers to recognize the dependency struc- ture of a dialogue. Compared with the general text-level discourse parsing (Mann and Thompson, 1988; Prasad et al., 2008; Li et al.. 2014b; Afan- tenos et al., 2015), DDP provides significant im- provement for many dialogue-related downstream tasks (Ma et al., 2023; Zhang and Zhao, 2021; Chen and Yang, 2021) via introducing symbolic dialogue structure information into the modeling process. Existing works mainly focused on applying neu- ral models handle problems in DDP. In de- tail, Shi and Huang (2019) formalized DDP as a dependency-basee rather than a constituency- based (Li et al., 2014a) parsing. However, their se- quential scan method introduced error propagation. Wang et al. (2021) proposed structure self-aware model, producing representations independently of historical predictions to handle error propagation, yet still encounters learning bias. Recent methods used external knowledge or other NLP tasks to mit- igate bias during training. Liu and Chen (2021) utilized domain adaptation techniques to produce enhanced training data and thus improved the DDP model's 's performance. Yu et al. (2022) pointed out the lack of modeling speaker interactions in previ- ous works and proposed a joint learning model. Fan et al. (2022) presented a distance-aware multitask framework combining both transition- and graph- based paradigms. Nevertheless, they overlooked the potential of leveraging the internal structure for regularization.
Overall, the previous methods have three lim- itations, i.e., error propagation, learning bias of distorted training labels and a single strategy, and incompatibility of predefined relations with down- stream tasks. To this end, we propose a task-aware sulf-super framework for DDP task. Con- cretely, a graph-based model DP,logDP, utilizing biaffine mechanism (Dozat and Manning, 2017). is designed for DDP, avoiding sequential error propa- gation. The model consists of symmetric parsers, si- multaneously performing bottom-up and top-down parsing.
We investigate parsed links and relation graphs of the two strategies and design bidirectional self- supervision mechanism encouraging the two strate- gies to acquire similar graphs through mutual learn- ing. Moreover, propose soft-window trian- gular (SWT) mask incorporating soft constraint, opposed to hard constraint (Shi and Huang, 2019), to guide the parsers. SWT mask encour- ages parsers to prioritize candidate links within a flexible window for each utterance.
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In summary, despite the good performance, the existing methods still have problems in modeling and application. Modeling-wise, the DDP cur- rently limited to either a top-down or bottom-up manner, leading to a gap in achieving bidirection- ality. application-wise the issue arises due to the constraints imposed by predefined relation types, thereby limiting the benefits to only tasks directly associated with these pre-defined relation types. Consequently, there is need establish connec- tions between the parsed dependency relations and downstream tasks. this paper, we propose a new framework to address these important issues.
tions (Appendix A), specified by (Asher al., 2016). Following (Li et al., 2014b), we add a root node, denoted as uo- An utterance linked to uo. not connected to preceding utterances.
3.2 Model Overview
To tackle error propagation in sequential scan, we re-formalized the DDP in graph-based man- ner, where link graph and a dependency-type graph are built based on the scores computed by the biaffine (Dozat and Manning, 2017: Zhang et al., 2020) mechanism. The parsed dependency tree can be obtained by jointly decoding the two graphs. Fig. 2 illustrates the structure of our pro- posed DialogDP model. First, the pre-trained large language model BERT (Devlin et al., 2018) 8) is employed to generate speaker-aware and context- aware utterance-level representations. Second, bidirectional self-supervision mechanism is de- signed capture the links and relations between utterances, and an SWT mask applied to regular- izing the learned graphs an explainable manner.
3 Methodology
Di tailored for integral graph-driven discourse parsing, which is more computation- efficient and avoids error propagation in the train- ing process. Specifically, the bidirectional self- supervision allows the parser to parse the dialogue with both top-down and bottom-up strategies. The employed strategies in both directions can mutually reinforce and guide each other. Moreover, the SWT mask guides the two strategies and imposes soft constraints on the integral learned graph, prompt- ing the parser to prioritize the candidate links of each utterance within a flexible window.
3.3 Speaker-Context Encoder
Given a dialogue U = {ui, {u,,u, u2...., un with a corresponding speaker list Sp [Sp1, Sp,. Spn). we concatenate the whole dialogue in {[cls). [spi]. un, [sp2], u2,.... [spm], Un where [cls] and [sps] are special tokens of start of the sequence and speaker Sp,, respectively. Then speaker-context integrated embeddings e can be obtained through PLM(x). e = PLM((), is the speaker-context integrated embedding of utterance of speaker Sp,, where j is obtained when Tj [sp:].
single sequence
3.1 Task Definition
Given multi-turn multi-party (or dyadic) dia- logue U consists of sequence of utterances [u1, u2, ( the goal of the DDP task is iden- tify links and the corresponding dependency types [lu uss rji)li A i} between utterances, where (uy, un rye) represents a dependency with type rjs from dependent utterance uy to head utterance us. We formulate the DDP as graph spanning process, where the dependency link of the current utterance is predicted by calculating probability distribu- tion P(u,/u, j # over other utterances. Depen- dency relation type prediction is formulated a multi-class classification task, where the probabil- ity distribution is computed as P(I)u; uj, E C). |C| is the number of pre-defined relation types.
3,4 Bidirectional Self-Supervision
DialogDP is composed of two symmetric compo- nents, bottom-up parser and a top-down parser, The two parsers are designed on the basis of the biaffine mechanism (Dozat and Manning. 2017) which is proved to be effective on sentence-level de- pendency parsing. The bottom-up strategy involves the parser calculating the biaffine attention score between a dependent and head, and selecting the head with the highest score for each dependent. In contrast, the top-down parser identifies the depen- dents with high scores for each head. Subsequently, both parsers build a link graph and a relation graph, wherein each node represents an utterance, and the ares connect pairs nodes within the graph.
The parsed dependencies between utterances constitute DAG (Shi and Huang, 2019). How- ever, due to the limited presence of multiple in- coming relations in STAC and Molweni datasets, most existing methods parse a dependency tree (Liu and Chen, 2021) which is a special type of DAG. The dependency types are predefined as 16 rela-
3

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Hadamand product
Biaffine Attention Mechanism
Soft- Window Triangular Mask
Decoding
Mairis multiplicstion
Relatuee
Ohi
Context-Speaker Encoder
Link
AT
10
[C]
[dst
#
A2
PP
HP
Top-down Strategy
Rolation
Supervision
S5
0
[6]
H
Hottom-pp Strategy
Mas
AM
M
Maa
Ma
Figure 2: Architecture of DialogDP.
We calculate the are scores (Shy [8 8f: ..., mn) and relation label scores (S) = [s], s/ ... s bbw with bottom-up strategy
The symmetric self-supervision losses are
C KL ( (ta' (1)
(3)
through:
C² (L 5 (L (1S)....
(4)
Here KI(PIIQ) = E P Plog( {) is the Kull- back-Leibler divergence (Kullback and Leibler, 1951) between the distributions. P and Q refer to the aforementioned paired attention scores.
w: HP + ! HP
HP
(1)
$/ o w( r + (r (y +b.
(2)
Here, HP Rdxx and py. E Rdx1 are the head hid- den states from two different multiple layer percep- trons (MLPs), while hnl e lxdd and r!" e IR 1xd are the dependent hidden states from another two different MLPs. oft Rlxn is the attention scores between the i-th dependent and n heads. sf E [XX[|| is the likelihoods of each class given the i-th dependent and n heads. Similarly, the are scores (S))) and relation label scores (S))) with be obtained by exchanging the positions of head hidden states and dependent hidden states in the above formulas. wa E RR xxd w w(2) Iddxd and w( Rdx[C[xd are learnable parameters of the biaffine mechanism.
3.5 Soft-Window Triangular Mask
In this work, we assume that all the head utterances appear before the dependent utterances. This aligns with our intuition that the current utterance can- not be induced by the preceding utterance. As we can see in Fig. 2, the feasible attention scores, for bottom-up strategy, between each head and the can- didate dependents distributed in the lower triangu- lar matrix M [m/]] such that mly 0 for i < j. as bottom-up strategy to retrieve one head for each dependent. Similarly, top-down strategy corre- sponds to the upper triangular matrix M = [m]]] such that m = for i > j. as it identifies the dependents for each head.
top-down strategy
By observing the are (relation) scores of both the top-down strategy and the bottom-up strategy, we can readily infer their high relevance. In other words, S x Sa and Sy x Syy Hence, designed a bidirectional self-supervision mecha- nism for regularizing the bidirectional strategies by assuming Sh. Sp and Stm. Sta"
The previous method (Shi and Huang, 2019) im- plemented hard window constraint, compelling the model to exclusively select links within pre- determined distance range. However, the hard win- dow excluded all candidates whose distance from the current utterance exceeds the predefined win- dow size.
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This limitation hampers the generalization capa- bility in real-world scenarios. To this end, we devise a soft-window mechanism that can be implemented by a carefully designed mask. Each element mij of the mask MM is calculated by:
3.7 Loss Function
We use binary cross entropy (BCE) loss for multi- label classification as link prediction loss for both strategies, and adopt cross entropy (CE) loss for relation classification. Here we show the classifica- tion loss functions of the top-down strategy, the classification losses of both strategies are similar.
1,
dij Bo
f(log( P:z), am, 1), f(log(P (o.g(P)))))))))) an, an):
Bo dig B1
m
B1 dij B2
n
Lid n(n - i)
BCB (CB(oig11..))) 16.)) (10)
.22
dij 2 B2
where f(,, min, mar) is the Max-Min scaling that scales each input feature to a given range [min, max]. di, denotes / - jo- Bo. B1, and B2 are the distance boundaries suggested by the prior distribution of head- deaeddeenddnnt distances, of which the distribution plot is in Appendix A. 0g = 0.95, a1 0.85 and 0g = 0.7 are the hyper-parameters. Combining the triangular mask with the soft- window mask, SWT masks for bottom-up and top- down strategies, Mb and Mia are obtained by
cla mi|CI
Σ CEC
CE(OL virs) (11)
For u, and Uj, Vig is binary label for link and bhy is a relation label. Here ny is the number of existing links in a dialogue. While the bottom- up strategy in decoding selects just one head for each dependent, it is trained using a multi-label classification approach. This is essential because a small set of dependents may have multiple ground- truth heads. Our method capitalizes this to fully exploit the instructive dependent-heead label infor- mation, which is often ignored by other methods. In summary, the total loss of the framework is the weighted summation of classification and supervi- sion losses.
Mbu M M
(5)
Ma M"
(6)
The scores of ares and relations after the soft- window triangular masking are Oo E Raxn and O E Ruxax CI respectively. We denote of [0%], and j. == =
= Cin + C) + Cia + Cla + Nal2 + ArC' (12)
3.6 Link and Relation Decoding
Here A₂ and \ are trade-off parameters.
the decoding process, the parser leverages the bottom-up strategy to predict the link and the corre- sponding relations. Concretely, for each dependent utterance us the parser predicts its head Uy and then recognizes the relation of the predicted link.
3.8 Task-Aware Dialogue Discourse Parsing
Previous researches on DDP followed the original definition 16 relations, regardless of application scenarios. However, the existing relation taxonomy may not fit the downstream tasks, c.g., ERC, Hence, we propose task-aware DDP paradigm to improve its adaptability to downstream tasks. In Fig. 3, the task-aware DialogDP bridges the gap between the fundamental DDP with downstream tasks through fine-tuning Di with task-aware aware relation an- notations. The main steps include:
exp(of,) Eneres exp(
P(i.j 1/2i, Uci)
(7)
3. denotes whether the parser predicts a link be- tween u, and uy. At < i indicates that the predicted head should be the preceding utterance before the current dependent utterance, which is achieved by applying the triangular mask. Then, the j-th utter- ance is determined as the head through
(a) Train DialogDP on existing DDP datasets with the default taxonomy of dependency relations.
(b) Predict dependency links A of the dialogue
arg max P(Oi5 10, U..). j<i
(8)
Ud in the downstream task training dataset.
(c) Automatically re-annotate relations 7' for predicted links A with rule-based methods based on target labels for the downstream task, which is detailed in Algorithm 1.
Similarly, the parser predicts the relation between in and its selected head uy. The probability distri- bution over all relations calculated as follows:
explos.)
(d) Fine-tune DialogDP on downstream training dataset with and Td
P(I uj,t CC =C)== E.Ec exp(o)
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Task-uware DialogDP
Di
DIS Task
task-aware dependency
dialogue
dependency
Framework of task-aware DDP. D/S denotes downstream. The X means not using the predefined relation labels. The backward arrow from D/S Task to Task-aware Di corresponds to step (c) in subsection 3.8.
Figure 3:
(e) Predict dependency links and relations with Di for test dataset and incorporate results into input features of downstream tasks.
an online game, The Settlers of Catan. It con- sists of 1, 173 annotated dialogues, divided into two sets, i.e., 1, 062 dialogues for training and 111 for testing. Molweni is collected from Ubuntu Chat Corpus (Lowe et al., 2015). The dataset consists of 9, 000 annotated training instances, along with 500 instances allocated for development and an- other 500 instances for testing. We pre- ree-proessedd datasets following (Shi and Huang, 2019).
Algorithm aware annotation
Data: downstream dialogue Udd with labels
E N+ (0). predicted links
Result: task-aware relations 7
define state transition mapping g(4,4) (4,4 1, L);
1 while y in Y and yi., of 0 do
K. 9(4,,1);;
end
4.2 Setups and Metries
We used BERT-large from HuggingFace the speaker-context encoder. The optimizer is AdamW (Loshchilov and Hutter, 2018) with initial learning rates of le-5 and 5e-6 for DialogDP and BERT encoder, respectively. The maximum value of gradient clipping 10. Both A and \ are con- figured with 0.05. The dropout rate is configured to 0.33, adhering to the default setting of biaffine. Following Shi and Huang (2019), we report micro Fl scores for LINK prediction and L& R prediction, respectively. In L&R, both the link and its cor- responding relation should be correctly classified. The experiments were performed on V100 GPU with 16 GB of memory. To provide accurate results, we conducted three random runs on test sets and reported the average score.
Taking the downstream ERC task as an example, task-aware DDP leverages the state transition to represent emotion shift which can enhance ERC performance (Gao et al., 2022) and contribute to the research in the psychology domain (Winkler et al., 2023). The state transition mapping g (eq. 13) from head ue uz is obtained based their tar- get labels 4 and Ij. where 4, Ij {0, 1, corre- sponding to (negative, neutral, positive) sentiment polarities.
2
94,44) = 3
4 0
(13)
5
The numbers from 0 to 5 in g are the rela- tion types for ERC task-aware DDP, referring to "same_polariyy "sentiment to neutral", "neg- tivv__to_positive, "neutral_to statcall_tt_eeeeee _o____atieeeee", "neu- tral_to _o__tttt__ivee and "positive_to _or_attv__evvvee,,__________ re- spectively. In this case, the task-aware DialogDP fine-tuned with re-annotated relations is capable of capturing sentiment shifting between utterances.
4.3 Baselines
Shi and Huang (2019) proposed DeepSequential, which sequentially scans the utterance and predicts the link and its corresponding relation type sequen- tially. Wang et al. (2021) presented a structure self-aware model, which adopts edge-centric graph neural network. Liu and Chen (2021) put forward framework leveraging cross domain data to improve the generalization ability the neural parser. Li et al. (2020) built the Molweni dataset and proposed a parser on the basis of DeepSequen- tial. He et al. (2021) advanced multitask learning framework to jointly learn -antterrnnnnweringgg. and discourse parsing tasks.
4 Experiment Setups
4.1 Dataset
We evaluated DP on two publicly available datasets, i.e., STAC² (Asher et al., 2016) and Mol- weni (Li et al., 2020). STAC is collected from
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STAC
Molweni
STAC
Molweni
MODELS
MODELS
Link
L&R
Link
L&R
Link
L&R
Link
L&R
Shi and Huang (2019)
73.2
55.7
77.9
54.4
Di
73.0
58.5
83.2
59.8
Li et al. (2020)
78.1
54.8
w/o Link_Spp
72.0
57.011.s
83.1
58.511a
Wang et al. (2021)
73.5
57.3
81.6
58.5
w/o Rel Sup
72.5
55.612.9
82.111.1
58.711.1
Liu and Chen (2021)
75.3
56.9
79.7
55.9
w/o L&R_Sup
72.0
56.1
83,5to.3
58.5
He et al. (2021)"
80.0
57.0
w/o SWM
72.4.0.6
55.812.7
83.0ja.2
58.7
Yang et al. (2021)
74.1
57.0
Biatfine
71.2u1.8
55.313.2
82.5
58.1
Fan al. (2022)
73.6
57.4
82.5
58.9
Supar
71.5u1.5
55.513.0
82,9,0.3
58.5
Yu al. (2022)
73.0
57.4
83.7
59.4
Supm
71.941.1
56.512.0
83.1 58.1
Di
73.0
58.5
83.2
59.8
Table Ablation study results. Link_Sup, Rel_Sup, L&R Sup and SWM corresponds to link supervision. relation supervision, link and relation supervision, and soft-window mask, respectively. Biaffine refers variant without self-supervisio or soft- witddo mask. Supsu (Supro) refers to a single supervision mechanism where only bottom-up (top-down) strategy is supervised by top-down (bottom-up) strategy.
Table 1: Main results on STAC and Molweni datasets. denotes the re-run results. L&R refers to LINK&RELATION, The bold text reveals the best per- formance and the underlined indicates the second best.
Yang et al. (2021) presented unified frame- work DiscProReco jointly learn dropped pro- noun recovery and DDP. Yu et al. (2022) pro- posed a speaker-context interaction joint encoding model, taking the interactions between different speakers into account. Fan et al. (2022) combined the advantages of both transition- and graph-based paradigms.
mechanism and soft mask. Specifically, if L&R_Sup is removed from DialogDP, the FI scores on the four indices drop 1.1% on average. Without SWM, the performance decreases 1.15%
on average. The results of the Biaffine model high- light the significance of incorporating both bidi- rectional self-supervision and SWM mechanisms. The average FI score drops of w/o Link_ on both (INK(0.55%) and L&R(1.49)) reveals that link supervision benefits both link prediction and overall dependency parsing. This observation is in line with our intuition, as the accuracy of sub- sequent relation classification depends on that of the predicted links. Furthermore, we observe that the inclusion of relation supervision not only im- proves link prediction but also suggests reciprocal influence between link prediction and relation clas- sification. The results of Supau and SupIo prove DialogDP is regularized by the bidirectional self- supervision, which reduces the training bias.
4.4 Main Results
We report the DDP results of our Di and baselines on STAC and Molweni datasets. In Ta- ble 1, DialogDP outperforms all the baselines on L&R and achieves comparable LINK prediction re- sults. We believe that the weak L&R of baselines be attributed two factors, i.e., link predictor and relation classifier. The F1 scores on Molweni demonstrate that the majority of the baselines ex- hibit weaknesses in both link prediction and rela- tion classification. The results on STAC indicate that some baselines have relatively stronger link predictor yet obtain a poor performance on L& R. This is because their weaker relation classifiers may fail to identify the relations of predicted links, even if the links determined accurately. Shi and Huang (2019) set fixed window in their model, which reduced the complexity of link prediction DDP yet compromised the model's 's capacity to capture relations in long-range dependencies.
4.6 TA-DialogDP ERC task
We conducted experiments on an ERC dataset MELD (Poria al., 2019) to investigate the pro- posed task-aware paradigm. Specifically, we se- lected SKIER (Li et al., 2023) as the backbone to verify the effectiveness of the generated dialogue dependency graph by the task-aware mechanism, as it explicitly leveraged parsed trees for ERC. In Table 3, the DialogDP-basee models significantly outperform SKIER w/o TL and DialogDP models in task-aware setup even achieve comparable re- sults with SKIER, especially on 3-class sentiment analysis.
4.5 Ablation Study
We conducted ablation studies to further explore the functions of essential components in DI. Table 2 demonstrates that the excellent perfor- mance of Di can be attributed to the inclu- sion of the proposed bidirectional supf-superviiion
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Anyune have trade?
A
sorry
8
sry
you wood?
D
yep
cheers
QAP
Communi
Q-Elab
Answer
Acknowlodgement
Ground-Truth
DeepSequential
CbarGPT
DialogDP
Figure 4: Case studies of STAC dialogue. In the lower triangular attention matrix DialogDI, the shades varying from light blue to dark blue depiet the link prediction scores ranging from 0 to
Our observations indicate, in general, the task- aware setup provides a slight benefit to DialogDP. except for TA-Dialos DAADDillgDPPsTaa We believe this may be attributed to the fact that the task-aware relation annotation specifically fo- cuses on polarity shifting without differentiating emotion shifting. We also employed ChatGPT C for ERC. Overall, the performance of ChatGPT lags significantly behind that of SKIER, primarily due to the zero-shot setting. However, the inclusion task-aware prompts helps to align the ChatGPT with downstream ERC through task-specific rela- tions, whereas prompts integrated with DDP may present a challenge for ChatGPT in comprehending fundamental relation types.
4.7
Case Study
As shown in Fig 4, our model surpasses Chat- GPT in L&R. In detail, A(0)->B(2), A(0)->D(4), D(4)->D(6) do not appear in ground truth but are indicated by ChatGPT. Morcover, ChatGPT fails on all QAP (label 1) pairs. The atten- tion scores of DialogDP exhibit notable dis- tinction between correct links (dark) and incor- rect links (light). Compared with DeepSequen- tial, our graph-based DialogDP avoids sequential error propagation through parallel attention mech- anism.
in 7-class ERC task.
4.8 Effeet of Dialogue Length
We examined the effect of dialogue length on the parsing performance. As shown in Fig. (a), the trend on STAC is gradual decline in performance the dialogue length increases. The reason is that long-range dialogues encompass greater num- ber of links and relation types, resulting more complicated parsing tree. Consequently, the perfor- mance of the parser is adversely affected, resulting in lower F1 scores. Additionally, the performance of L&R declines more rapidly compared to that of Link prediction. This suggests that the exis- tence of long-term term-termma dependencies also presents sig- nificant challenges for the relation classifier. In Fig. (b), we did not observe comparable trend, as the length of Molweni dialogues demonstrates a concentrated distribution within a limited range [7, 14). This may benefit the parser.
MELD
Methods
3-cls
7-cls
ChatGPT
50.20
51.60
CharGPT (MOLWEN))
50.26
44.65
TA-ChaiGPT, (MLLWRI))
52.98
53.54
RoBERTa
72.14
63.61
SKIER wL.
65.87
SKIER"
75.05
67.39
DialogDP (STAC)
74.38
66.69
DialogDP DialogDPrMauwen (Mowwan))
74.26
66.70
TA-DialogDP (STAC)
74.50
66.55
TA-DialogDP (MoLwaN))
74.95
66.84
Table 3: Results on the downstream ERC task. The metric used is the weighted average Fl score. The bold text reveals the best performance, and the underlined in- dicates the second best. Subscripts between brackets denote the corpus used to train the parser. means the model was fine-tuned on manually annotated relations. Here, the results of SKIER* serve reference point, not for direct comparison. w/o TI. means without man- ually annotated relations.
4.9 Effect of Dialogue Turn
We further investigated the performance of base- lines and our DialogDP at different dialogue turns on the Molweni dataset. In Fig. 6, we observed
8

---

100
70-
SeepSqquettil
Wang al.
(%)
80
Our DialogDP
Accuracy (%)
Link
40
L&R
10
11
13
(a) Dialogue Length (STAC)
40
100
10
Dialogue Turn
80
Figure Comparison of prediction accuracy between typical methods at different dialogue turns.
60
PI
Link
L&R
task-aware paradigm combining DDP and down- stream task ERC. First, we designed two graph- based parsers leveraging both bottom-up and top- down parsing strategies and eliminating sequen- tial error propagation. Then bidirectional self- supervision mechanism designed to reduce the learning bias by exploiting the structural symmetry of two strategies, and thus avoid the reliance on external knowledge. Furthermore, soft-window triangular mask, tailored with statistical informa- tion, is utilized effectively handle long-term de- pendencies. Second, presented task-aware paradigm bridging the gap between the founda- tional DDP with downstream tasks through fine- tuning DialogDP with task-aware dependency re- lation annotations. Empirical studies on DDP and downstream ERC show the superiority and adapt- ability of our DialogDP and task-awar paradigm.
(b) Dialogue Length (Molweni)
Figure 5: The predicted FI scores of DP the STAC and Molweni testsets.
a downward trend in all models. As the dialogue turn increases, the search space of bottom-uppbased methods expands, leading to decline in the accu- racy of L&R prediction. DeepSequential displays the consistently lowest accuracy across all dialogue turns, largely attributed to the issue of error prop- agation. Meanwhile, the other two models elimi- nated this issue as they avoided using historical pre- dictions for processing the current one. Different from the model of Wang et al. (2021) in Fig. 6, Di- DP shows gentle-slope downward trend. Our method exceeds the model of Wang et al. (2021) in all the dialogue turns except the 9th turn. Addition- ally, DialogDP has two local minimum points at the 5th turn (48.0%) and 9th turn (43. 1 %), both of which performs better than those the 5th (40.0%) and 8th (41.5%) turns. Given that the model pro- posed by Wang et al. (2021) employed graph-based techniques (Cai and Lam, 2020) to mitigate error propagation, our model demonstrates superior per- formance in addressing this issue since the bidi- rectional self-supervision improves the stability of prediction results by jointly utilizing the top-down and bottom-up dependency parsing graphs.
Limitations
Due the lack of theoretical support, it is chal- lenging design task-aware dependency relations. The design of task-specific dependency relations is expected to exert a substantial influence on the per- formance of downstream tasks that are integrated with DDP. Hence, is encouraged to undertake the- oretical analysis in order to devise task-specific relations that are better suited for the intended downstream tasks. This paper primarily concen- trates the ERC task to serve as an illustrative example for validating the effectiveness of the task- aware paradigm. However, additional comprehen- sive evaluations would be beneficial for thoroughly assessing the proposed task-aware paradigm.
5 Conclusion
this paper, we proposed a bidiretional self- supervised dialogue discourse parser DP and
9

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Acknowledgements
detection based on multi-task learning, Knowledge- Based Systems, 248:108865 108861.
This research is supported by the Agency for Sci- ence, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A #A18A2b0046).
Yuchen He, Zhuosheng Zhang, and Hai Zhao. 2021. Multi-tasking dialogue comprehension with dis- course parsing. In Proceedings of the 35th Pacific Asia Conference on Language, Information and Com- putation, pages 69-79.
Ethics Statement
Fajri Koto, Jey Han Lau, and Timothy Baldwin. 2021. Top-down discourse parsing via sequence labelling. In Proceedings of the 16th Conference of the Turo- pean Chapter of the Association for Computational Linguistics: Main Volume, pages 715-726.
The research conducted strictly adheres to ethical guidelines of EMNLP, ensuring privacy, fairness, transparency, responsible use, and continuous im- provement.
Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. The annals of mathe- matical statistics, 22(1):79-86
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In this section, we display the statistical informa- tion of STAC and Molweni datasets. The x-axis represents the distance between a pair of dependent utterances, and the y-axis represents the propor- tions of different distances. is evident that the distance between dependent utterances follows a long-tailed distribution. In STAC training set, more than 90% of the dependencies are within 10 dia- logue turns; More than 99% of the dependencies within 20 dialogue turns; No dependency exists beyond 26 dialogue turns. Hence, we set hyper- parameters Bo 10, B₁ 20, B3 89 for task on the STAC dataset. Similarly, we set hyper- parameters Bo = 8, B1 = 10, B2 = 14 for task on the Molweni dataset.
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50
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Percent
80
Julia R Winkler, Markus Appel. Marie-Luise CR Schmidt, and Tobias Richter. 2023. The experience of emotional shifts in narrative persuasion. Media Psychology, 26(2): 141-711
10
Distance
Jingxuan Yang. Kerui Xu, Jun Xu. Si Li. Sheng Gao, Jun Guo, Nianwen Xue, and Ji- Ji-Rong Wen. 2021. joint model for dropped pronoun recovery and conver- sational discourse parsing in chinese conversational speech. In Proceedings of the 59th Annual Meet- ing of the Association for Computational Linguistics
Figure 7: The distance distribution STAC dataset
The pre-defined 16 dependency relations as shown as below:

---

There are 16 pre-defined dependency relations. They are ['Continuation' 'Explnnation, 'Comment', 'Clarifica- tion cion__uuetion', 'Question- paaasssrrrrrr,,'' 'Correction', 'Contrast', AAcnnwledgge- ment', 'Background', 'Result', 'Elabo- ration', 'Conditional', 'Narration', 'Q- Elab', 'Parallel', 'lternation'' predicted dependencies are in the form of: (0,1, relation: Contrast), (0,2, relation: Ex- planation). (0,3, relation: Alternation), (3,4, relation: (4,5, relation: Continuation), (3,6, relation: Acknowl-
Percent
10
12
Distance
Figure 8: The distance distribution of Molweni dataset
edgement), (2,7, relation: Acknowl-
Dependency Relations
Comment
Q-Elab Elaboration
Conditional
edgement), (3,7, relation: Acknowledge- ment).
Acknowledgement
Background
Contrast
Alteraatin
Narrttion
Correction
Parallel
Continuation
C
The application of ChatGPT to ERC task with dependencies from task-aware DialogDP
QAP
Explanation
Result
Clarification
Table 4: The Predefined Dependency Relations
Input of System content: "You are an expert in sentiment analysis. The given head utterances may influence the emo- tion of the current utterance. Please iden- tify the emotion label for the current ut- terance with of the pre-defined emo- tion labels. The emotion labels are [neu- tral, surprise, fear, sadness, joy, disgust, anger]."
B
The application of ChatGPT to DDP task
This section details the usage of ChatGPT on the DDP task. To prompt ChatGPT to parse dialogue based on specific requirements, we begin by provid- ing an example dialogue that includes pre-defined dependency relations and parsed dependency tree. Subsequently, we input dialogue where we ask ChatGPT to parse it accordingly. Below is the prompting example:
Task aware dependency-
prompt: prompt = "Dialogue History: U, Rela- tion: Head: uj. Current utterance: Emotion label for current utterance is:".
Given the dialogue history, please predict the discourse parsing based their semantic relevance and logic flow as follows: speaker: A, text: have enough to build settlement too now... ?, turn: 0 speaker: A, text: and won't let me turn:
speaker: B, text: because you have no good roads!, turn: 2
speaker: C, text: youre like me, turn: 3
speaker: C, text: you need to build another road, turn: 4
speaker: C, text: it needs be least roads away from another of your settlements, turn:
speaker: A, text: okcydoke, turn: 6 speaker: A, text: thanks guys, turn: 7
12


Reviews:
[{"review_id": "4bPZDk6CPD", "reviewer": "Reviewer_okwz", "summary": "", "questions": "(A) Does the self supervision part happen before the triangular mask or after the triangular mask? In text it is presented before the triangular mask section, but in figure 2 the self-supervision arrow is shown after the soft-window triangular mask.\n\n(B) What are Ua and Ul in equations 1 and 2? There isn't any text description of their definitions. Are they relevant to the U defined in line 223? In general, the uses of all these U in the 2 equations are quite confusing, as some of them are upper-case and some are lower case, some are italicized and some are not, and the casing/italicizing do not match with those corresponding parts in Figure 2.\n\n(C) What is the definition of dij in equation on line 307? Is it simply | i - j |?\n\n(D) In section 3.7, why do we only allow choosing one head if sometimes there are multiple ground-truth heads? Since we are already training it as a multi-label classification problem, why can't we choose the heads via multi-label classification?\n\n(E) In section 4.6, what do you mean by \"using SKIER as backbone\"? Do you mean using it as a baseline? Also, in Table 3, is SKIER* the only method that uses manually annotated relations for finetuning?", "limitations": "", "rating": 3, "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": "**We are grateful for your valuable comments, which will definitely help improve our work.**\n\n**Reproduction of the method could be difficult as some key methodology details and hyperparameters are missing. For example, the lambda\\_a and lambda\\_l of the objective (equation 12) are not provided in the main text or in appendix. I included more questions about some methodology details in the Questions section.**\n\nRe: Thanks for your comment. As we mentioned in the first footnote, the code will be available for public access once accepted. Besides, we will include more details in the appendix to help readers reproduce/ understand our proposed framework. lambda\\_a and lambda\\_l are trade-off parameters and are set to 0.05 in this paper.\n\n**While the new framework did achieve great performance in both experiments, the improvements are quite marginal (especially in STAC and Molweni experiments) and thus made the results less exciting.**\n\nRe: Thanks for your comment. Since dialogue discourse parsing is a very challenging task, it is hard to gain significant performance improvements. For example, the performance improvements of the baseline methods are quite limited (e.g., Yu et al. 2022, Yang et al. 2021, Li et al. 2020). Even though, we still observe a relatively significant improvement on the STAC dataset (`1.1\\% in L\\&R`). In general, our method demonstrates a slightly better performance than the strongest baseline Yu et al. 2022. On the other hand, the main contributions we have claimed are the self-supervision structure which improves the performance of DDP without joint training of the auxiliary task and external corpus, and the task-aware mechanism which benefits downstream tasks. Incorporating the auxiliary task or an external corpus would lead to additional enhancements in the performance of our proposed approach. As for the experiments on the downstream ERC task, it is worth noting that the SKIER with a \\* mark denoting that the model was fine-tuned on `human-annotated` relations. Consequently, a direct comparison between the outcomes of our approaches and SKIER* would be inequitable. On average, our proposed methods outperform SKEIR without TL by 0.83 and 0.86 in terms of 7-cls and 3-cls, respectively. \n\n**While the ablation studies justified many design choices (such as using the soft window), some choices still seems quite arbitrary, such as selection of specific values of a0,a1,a2, B0,B1,B2 for the soft window.**\n\nRe: Thanks for your valuable comment. As we mentioned in the Appendix A, the selection of values of a0, a1, a2, b0,b1, b2 are mainly based on the `statistical information` of the two datasets. For example, In STAC training set, more than `90\\%` of the dependencies are within `10` dialogue turns; More than `99\\%` of the dependencies are within `20` dialogue turns; No dependency exists beyond 26 dialogue turns. The results in Figure 5 (a) indicate that the performance drops fast when the length of dialogue exceeds 10, which coincides with our expectations. On the other hand, a parameter analysis is necessary for the selection of hyperparameters. We conducted experiments to further determine the specific values of such hyperparameters. We will give more justifications for the selection of hyperparameters in the appendix.\n\n**(A) Does the self supervision part happen before the triangular mask or after the triangular mask? In text it is presented before the triangular mask section, but in figure 2 the self-supervision arrow is shown after the soft-window triangular mask.**\n\nRe: Thanks for this question. As illustrated in Figure 2, the self-supervision part happens after the soft-window triangular mask. As for the presentation, we introduce self-supervision before the soft-window triangular mask as it is the core part of our proposed framework and the SWT mask can be seen as an essential component of the self-supervision mechanism.\n\n**(B) What are Ua and Ul in equations 1 and 2? There isn't any text description of their definitions. Are they relevant to the U defined in line 223? In general, the uses of all these U in the 2 equations are quite confusing, as some of them are upper-case and some are lower case, some are italicized and some are not, and the casing/italicizing do not match with those corresponding parts in Figure 2.**\n\nRe: Thank for your question. We are sorry that we should have added a clear description of the two notations. $u_a^{(1)}$ (dim is $1\\times d$), $U_a^{(1)}$ (dim is $d \\times d$) and $\\textbf{U}_l^{(1)}$ (dim is $d\\times|C|\\times d$) in equations 1 and 2 are learnable parameters of the biaffine mechanism. They are not relevant to the U defined in line 223. We will replace $U$ and $u$ with $W$ and $w$, respectively, in equations 1 and 2 to make it clear. \n\nIn this paper, we use the lowercase letter to represent a vector, use the italicized uppercase letter to represent a matrix, and use the bold uppercase letter to represent a 3-dimensional tensor.\n\n**(C) What is the definition of dij in equation on line 307? Is it simply $| i - j |$?**\n\nRe: Yes, $d_{i,j}$ is simply $| i - j |$. We will add an introduction of it.\n\n**(D) In section 3.7, why do we only allow choosing one head if sometimes there are multiple ground-truth heads? Since we are already training it as a multi-label classification problem, why can't we choose the heads via multi-label classification?**\n\nRe: Thanks for your question. According to Shi and Huang (2019), only around 6\\% of the utterances have multiple heads. Almost all the previous methods select only one head during training. We consider multi-heads during training to `fully exploit` this useful dependent-head label information which is usually `ignored` by the other methods. However, data with 6\\% percent multi-head labels are not sufficient/enough to train a multi-label classifier. On the other hand, as all the baselines only select one head for each dependent, we also select one head for a fair comparison.\n\n**(E) In section 4.6, what do you mean by \"using SKIER as backbone\"? Do you mean using it as a baseline? Also, in Table 3, is SKIER\\* the only method that uses manually annotated relations for finetuning?**\n\nRe: Thanks for your question. We want to test the effect of task-aware dialogue discourse parsing on downstream task, i.e., ERC. Meanwhile, we found SKIER `explicitly leverages` the dialogue dependency graph for ERC task. Therefore, we use SKIER as a backbone to utilize the dialogue dependency graph generated by our Task-aware DialogDP for downstream ERC.\n\nBesides, we also use SKIER w/o TL and SKIER\\* as the baseline models utilizing dialogue dependency graphs which contain the general dependency relations instead of task-aware dependency labels.\n\nWe will add more descriptions to clarify the usage of the SKIER model. Yes, SKIER\\* is the only one that uses manually annotated relations for finetuning. We present the outcome of SKIER\\* not with the intention of making a comparison, but rather as a point of reference.\n\n**Line 134 and 393 \"DPP\" should be \"DDP\"**\n\nRe: We will fix it. Thank you.\n\n**Equation 1,2 and Figure 2: The casing and italicizing of \"Ua\" and \"Ul\" are all different and not consistent.**\n\nRe: We explained this in question B. $U^{(1)}$ and $u^{(2)}$ in Fig.2 corresponds to $U^{(1)}_a$ and $u^{(2)}_a$ respectively. We omitted the subscript $l$ in Fig.2 for simplicity as they appear in the Biaffine Attention Mechanism for Link prediction. We will add the subscript $l$ to make keep them consistent. Thank you!\n\n**Some parts of Figure 3 are quite confusing. For example, why is there an arrow from dependency to D/S task with an \"X\" over it? What does the backwards arrow from D/S task to Task-aware DialogDP stand for? Perhaps adding some explanations to the caption will help.**\n\nRe: In the previous research, the dialogue dependency graph with general relation labels is directly used for downstream task, i.e., ERC. However, in the framework of task-aware DDP, we do not use the general relation labels (corresponds to the \"X\" in the Figure 3); Instead, we `automatically re-annotate` relations based on the downstream task (`step (c)` in line 377). Then we `finetune` Task-aware DialogDP (`step (d)` in line 381) and predict the dependency links and relations with the finetuned task-aware DialogDP for downstream tasks.\n\nHere, the backwards arrow from D/S task to Task-aware DialogDP corresponds to the step (c) in line 377.\n\nThanks for the suggestion. We will add the explanations to the caption and Fig.3 to facilitate the comprehension.", "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 paper presents a new framework for dialog discourse parsing, with several interesting novel ideas including a self-supervision objective between top-down and bottom-up approaches and a soft window mask. These ideas could inspire new approaches not only within DDP but also DAG-parsing tasks in general.\n\nThe authors demonstrated the strength of their framework in the experiments, as the framework outperformed all baselines in accuracy of Link and Relations on two datasets. The new framework also outperformed baselines in downstream ERC task, thus demonstrating the benefit of task-aware setups.\n\nThe authors conducted comprehensive ablation studies to justify the various design choices for the framework.", "reasons_to_reject": "Reproduction of the method could be difficult as some key methodology details and hyperparameters are missing. For example, the lambda_a and lambda_l of the objective (equation 12) are not provided in the main text or in appendix. I included more questions about some methodology details in the Questions section.\n\nWhile the new framework did achieve great performance in both experiments, the improvements are quite marginal (especially in STAC and Molweni experiments) and thus made the results less exciting.\n\nWhile the ablation studies justified many design choices (such as using the soft window), some choices still seems quite arbitrary, such as selection of specific values of a0,a1,a2,B0,B1,B2 for the soft window.", "questions_to_authors": "(A) Does the self supervision part happen before the triangular mask or after the triangular mask? In text it is presented before the triangular mask section, but in figure 2 the self-supervision arrow is shown after the soft-window triangular mask.\n\n(B) What are Ua and Ul in equations 1 and 2? There isn't any text description of their definitions. Are they relevant to the U defined in line 223? In general, the uses of all these U in the 2 equations are quite confusing, as some of them are upper-case and some are lower case, some are italicized and some are not, and the casing/italicizing do not match with those corresponding parts in Figure 2.\n\n(C) What is the definition of dij in equation on line 307? Is it simply | i - j |?\n\n(D) In section 3.7, why do we only allow choosing one head if sometimes there are multiple ground-truth heads? Since we are already training it as a multi-label classification problem, why can't we choose the heads via multi-label classification?\n\n(E) In section 4.6, what do you mean by \"using SKIER as backbone\"? Do you mean using it as a baseline? Also, in Table 3, is SKIER* the only method that uses manually annotated relations for finetuning?", "paper_topic_and_main_contributions": "In this paper, the authors presents a new framework for task-aware dialog discourse parsing. The framework involves a biaffine attention mechanism to produce probabilities for a dependency to be present between two utterances as well as the types of dependencies, then applies a soft-window triangular mask to mask the probabilities before predicting the final dependencies and types via softmax over the probabilities. The main novelties of the framework includes a self-supervision objective between separate top-down and bottom-up biaffine attention outputs, and using a soft-window mask based on relative utterance distance instead of a hard window. The authors conducted experiments on two dialog discourse parsing datasets: STAC and Molweni, and outperforming all baselines when evaluated on Link and Relation correctness. The authors also performed ablation studies and demonstrated the ability to extend the trained framework to downstream tasks such as ERC.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A", "Justification_for_Ethical_Concerns": "N/A"}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "Line 134 and 393  \"DPP\" should be \"DDP\"\n\nEquation 1,2 and Figure 2: The casing and italicizing of \"Ua\" and \"Ul\" are all different and not consistent.\n\nSome parts of Figure 3 are quite confusing. For example, why is there an arrow from dependency to D/S task with an \"X\" over it? What does the backwards arrow from D/S task to Task-aware DialogDP stand for? Perhaps adding some explanations to the caption will help."}}, {"review_id": "jfljzQD69X", "reviewer": "Reviewer_jNsR", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**We are grateful for your valuable comments, which will definitely help improve our work.**\n\n**1. The paper lacks sufficient analysis of the experimental results, especially on the Molweni dataset.**\n\nRe: Thanks for your valuable comment. Both the main results (Table 1) and the Ablation Study (Table 2) actually directly demonstrate the model's performance on STAC and Molweni. Section 4.6, TA-DialogDP on the ERC task, indirectly showcases the contributions of TA-DialogDP trained on STAC and Molweni to downstream tasks. Since the results on both datasets are similar, we mainly analyze the `overall trend` to keep the explanation concise. In Section 4.8, the results from the two datasets are presented separately due to their notable differences. Nevertheless, we will include an in-depth analysis of the results from the Molweni dataset in subsections 4.4, 4,5 and 4.6. \n\n**2. There is some overlap between the relevant work section and the introduction section.''**\n\nRe: Thanks for your valuable comment. We will revise the Introduction and related work to avoid overlap. Specifically, we will remove some references that already appeared in the introduction and add reviews on other relevant research in the related work section.", "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. Introducing a task-aware paradigm to improve the versatility of the dialogue discourse parser for downstream tasks.\n2. Designing a graph-based discourse parsing model called DialogDP to alleviate error propagation and learning bias.\n3. Demonstrating the effectiveness and flexibility of the proposed framework through empirical studies on dialogue discourse parsing datasets and a downstream task.\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. Advancing the state-of-the-art in dialogue discourse parsing and improving the adaptability of the parser for downstream tasks.\n2. Providing a novel approach to address issues with existing parsing approaches and demonstrating the effectiveness and flexibility of the proposed framework through empirical studies.", "reasons_to_reject": "1. The paper lacks sufficient analysis of the experimental results, especially on the Molweni dataset.\n2. There is some overlap between the relevant work section and the introduction section.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a task-aware self-supervised framework for dialogue discourse parsing, which is a fundamental natural language processing task. The paper addresses the issue of existing parsing approaches being constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks. The main contributions of the paper are:\n\n1. Introducing a task-aware paradigm to improve the versatility of the dialogue discourse parser for downstream tasks.\n2. Designing a graph-based discourse parsing model called DialogDP to alleviate error propagation and learning bias.\n3. Developing an innovative self-supervised mechanism that leverages both bottom-up and top-down parsing strategies to allow the parsing graphs to mutually regularize and enhance each other.\n4. Demonstrating the effectiveness and flexibility of the proposed framework through empirical studies on dialogue discourse parsing datasets and a downstream task.\n\nIn summary, this paper proposes a novel approach to dialogue discourse parsing that improves the adaptability of the parser for downstream tasks and addresses issues with existing parsing approaches.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "SmbQ1yFISz", "reviewer": "Reviewer_yNEK", "summary": "", "questions": "A. In ChatGPT baseline, how to select the demonstrations?  How to deal with the occasional failure of ChatGPT to follow instructions?", "limitations": "", "rating": 2, "confidence": 5, "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": "**We are grateful for your valuable comments, which will definitely help improve our work. We tried our best to avoid any misunderstandings. It might take you some time to read the response. We sincerely hope you could re-consider our paper after your reading.**\n\n**A. The example in Figure 1 lacks the data source. It does not come from either Molweni or STAC used in this paper.**\n\nRe: Thanks for your comment. We should have added the data source of the example. The example in Figure 1 is used to illustrate the dialogue dependency graph. Hence, we select a conversation snippet (the id of this conversation is tr\\_c2969) from dailydialog dataset (a widely used English dialogue dataset) with the parsing information by Shi and Huang (2019) [1]. We manually checked the parsed dependency graph. We will add the data source of this example in the paper.\n\n[1] Shi Z, Huang M. A deep sequential model for discourse parsing on multi-party dialogues. In Proceedings of the AAAI Conference on Artificial Intelligence 2019 Jul 17 (Vol. 33, No. 01, pp. 7007-7014).\n\n**B. Lack of a clear motivation. This paper argued that there are three problems, including error propagation, learning bias, and incompatibility of predefined relations, that need to be addressed. However, it doesn't give a clear explanation of why these problems need to be solved. For example, why is learning bias an issue worth considering, given that Molweni and STAC have few multi-head dependencies? What impact does the incompatibility of predefined relations have on downstream tasks?**\n\nRe: Thanks for your valuable comment. We will explain this question from the following three aspects and will revise the paper according to your suggestions.\n\n_Response to question B (1) (error propagation)_:\n\nWe mentioned in the second paragraph of the Introduction that error propagation is caused by `sequentially scanning` the utterances in dialogues and subsequently predicting dependency links and corresponding relation types, which is believed to be prone to a severe error propagation issue [1]. Taking paper of (Shi and Huang) [2] as an example, if the wrong head is selected for a dependent utterance then the wrong head would have a negative effect on the representation of the dependent utterance and the sequential scan mechanism makes this error propagate to the following utterances. This error propagation problem has been defined in paper [1] and paper [1] made efforts to alleviate this issue. In our paper, we avoid the error propagation problem by the graph mechanism. \n\n_Response to question B (2) (learning bias)_:\n\nThe existing models that select only one head from multiple heads for training introduced false information. Our model is designed to fully utilize multi-head dependencies for training, instead of distorting the labels. This can be applied to any discoursing parsing task of which the newly constructed dataset contains multiple heads, not only STAC and Molweni. Additionally, according to paper [4], these long-tailed data are worth studying; Overlooking the long-tailed data may result in a biased distribution estimation during training [5]. \n\nIn this paper, we mainly consider two types of learning biases, i.e., `measurement bias` and `algorithmic bias` [6]. Shi and Huang[2] selected only one head from multiple heads for each dependent during the training process. This inevitably caused measurement bias since some true head labels were set to `false` labels, introducing `errors` to the training data. Our method takes multiple heads into account during the training process. Our model fundamentally avoids such `measurement bias` through the aforementioned training setting. Additionally, as we mentioned in the paper, the bidirectional self-supervision mechanism serves as regularization which helps to minimize the `algorithmic bias`.\n\n_Response to question B (3) (incompatibility)_:\n\nAccording to paper [3], many existing relations(e.g., narration, background) `do not/ or rarely` exist in the ERC datasets. Meanwhile, we found that a relation type may correspond to multiple sentiment shifts. For example, question\\_answer\\_pair may correspond to negative\\_to\\_positive, positive\\_to\\_negative, neutral\\_to\\_negative, etc., which may not provide sufficient information for the ERC task. In this case, the existing relations are not compatible with the ERC task. Moreover, we want to capture the emotion shift in the dependency graph. In summary, it is essential to design a mechanism that is compatible with downstream tasks.\n\n[1] Ante Wang, Linfeng Song, Hui Jiang, Shaopeng Lai, Junfeng Yao, Min Zhang, and Jinsong Su. 2021. A structure self-aware model for discourse parsing on multi-party dialogues. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 3943–3949. International Joint Conferences on Artificial Intelligence Organization.\n\n[2] Shi Z, Huang M. A deep sequential model for discourse parsing on multi-party dialogues. In Proceedings of the AAAI Conference on Artificial Intelligence 2019 Jul 17 (Vol. 33, No. 01, pp. 7007-7014).\n\n[3] Li, W., Zhu, L., Mao, R., \\& Cambria, E. (2023). Skier: A symbolic knowledge integrated model for conversational emotion recognition. In Proceedings of the AAAI Conference on Artificial Intelligence.\n\n[4] S.Bengio. The battle against the long tail.Talk on Workshop on Big Data and Statistical Machine Learning. 1, 2.\n\n[5] Wang C, Gao S, Wang P, Gao C, Pei W, Pan L, Xu Z. Label-aware distribution calibration for long-tailed classification. IEEE Transactions on Neural Networks and Learning Systems. 2022 Oct 25(99):1-3.\n\n[6] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., \\& Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6), 1-35.\n\n**C. Unclear experimental settings. In Section 3.3, how to solve the BERT token limitation, since the whole dialogue is concatenated? In dataset processing, if you followed Shi et al. to pre-process a dataset that doesn't consider multi-head dependencies, what is the effect of your top-down and bottom-up strategy that considers multi-head dependencies?**\n\nRe: Thanks for the question. There are two or three conversations that exceed the token limitation of BERT. We removed punctuation and stop words to make the conversations fit for the token limitation. We mentioned that the code would be available for public access and more processing details would be released in the code repository. Also, we will include more experimental settings in the appendix. \n\nThe pre-processed dataset by Shi et al. contains multi-head dependencies. However, in their DeepSequential model, they just select one head for loss calculation. We consider multi-heads during training to `fully exploit` this useful dependent-head label information which is usually ignored by the other methods. However, data with few multi-head labels (6\\% multi-head labels in STAC) are not sufficient/enough to train a multi-label classifier. Besides, considering that baseline methods select one head for each dependent, we select one head in the decoding stage. \n\n**D. Unclear description in Section 3.8. What is the motivation for transition? Why does the existing relation taxonomy may not fit the ERC tasks? Why could this transition better facilitate ERC tasks? What principles is the transition based on?**\n\nRe: Thanks for your valuable comments. We explain this question from the following two aspects and will add more theoretical justifications according to your comments.\n\n_Responses to question D (1)(3)(4)_:\n\nThe state transition mapping aims to represent the `emotion shift` between two utterances which are connected by a dependency link. According to the following two research papers [1][2], emotion shift detection can `enhance` the performance of ERC and is defined as a task in the sentiment analysis domain. Therefore, we believe that state transition can facilitate the ERC task. We will include the theoretical support in subsection 3.8 to justify our motivation and principle for state transition (emotion shift). Besides, research [3] in the psychology domain also implements similar methods to represent the state transition (emotion shift).\n\n_Responses to question D (2)_:\n\nThe existing relation taxonomy can be applied to multiple downstream NLP tasks [4] including question-answering, dialogue summarization, and machine reading comprehension, which means the taxonomy is `not specifically tailored` for the ERC task. According to [5], many relations(e.g., narration, background) `do not/ or rarely exist` in the ERC datasets. Meanwhile, we found that a relation type may correspond to `multiple` sentiment shifts. For example, question\\_answer\\_pair may correspond to negative\\_to\\_positive, positive\\_to\\_negative, neutral\\_to\\_negative, etc., which may not provide sufficient information for the ERC task. Moreover, we want to capture the emotion shift in the dependency graph. Hence, we design a task-aware mechanism with the proposed relation taxonomy. We will add more explanations in the first paragraph of subsection 3.8 for this question.\n\n[1] Agarwal, H., Bansal, K., Joshi, A., \\& Modi, A. (2022). Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts. ACL 2022, 44.\n\n[2] Gao, Q., Cao, B., Guan, X., Gu, T., Bao, X., Wu, J., Liu B., \\& Cao, J. (2022). Emotion recognition in conversations with emotion shift detection based on multi-task learning. Knowledge-Based Systems, 248, 108861.\n\n[3] Winkler, J. R., Appel, M., Schmidt, M. L. C., \\& Richter, T. (2023). The experience of emotional shifts in narrative persuasion. Media Psychology, 26(2), 141-171.\n\n[4] Li, J., Liu, M., Qin, B., \\& Liu, T. (2022). A survey of discourse parsing. Frontiers of Computer Science, 16(5), 165329.\n\n[5] Li, W., Zhu, L., Mao, R., \\& Cambria, E. (2023). Skier: A symbolic knowledge integrated model for conversational emotion recognition. In Proceedings of the AAAI Conference on Artificial Intelligence.\n\n**E. Unfair comparison. This paper adopted the BERT-large, which is not fair to directly compare to the works adopting the base version. Therefore, it is not clear whether the performance improvement comes from the proposed method or from using a larger pre-trained model.**\n\nRe: Thanks for the comment. We carefully checked the experimental setting of the baselines. We found that He et al.(2021) utilized `bert-large` and employed `multitask learning` to enhance the performance of dialogue parsing; Yang et al.(2021) attributed the good performance to the `joint training` of conversational discourse parsing and dropped pronoun recovery tasks; Yu et al.(2022) leveraged `external corpus` (the authors mentioned ``We pre-train BERT on a large-scale unlabeled dialogue corpus in the second stage. It is collected from the Ubuntu dialogue corpus (Lowe et al., 2015), containing 930,000 unlabeled dialogues.''). Our model did not introduce external knowledge/ corpus nor leverage a different auxiliary task. In this case, we think it is fair to compare our model with these baselines.\n\n**F. An in-depth analysis of each component in the proposed method is required. Since top-down and bottom-up are used to alleviate the problem of learning bias, does this strategy better identify multi-head dependencies? Since SWM is devised to alleviate the problem of long-range dependencies, does it help identify longer-distance dependencies? Besides, what problems in ERC are solved by TA-DialogDP? The overall performance is not convincing and an in-depth analysis is required.**\n\nRe: Thanks for your valuable comments. We explain this question from the following three aspects and will add more analysis of each component in the experiment section.\n\n_Response to question F (1)_:\n\nIn this paper, we mainly consider two types of learning biases, i.e., `measurement bias` and `algorithmic bias` [1]. Shi and Huang[2] selected only one head from multiple heads for each dependent during the training process. This inevitably caused measurement bias since some true head labels were set to false labels, introducing errors to the training data. Our method takes multiple heads into account during the training process. Our model fundamentally avoids such `measurement bias` through the aforementioned training setting, which does not need extra experiment analysis. Additionally, as we mentioned in the paper, the bidirectional self-supervision mechanism serves as regularization which helps to minimize the `algorithmic bias`. The ablation study showed the effectiveness of the mechanism through the results in the 1st, 2nd, 3rd, 4th, 7th and 8th lines of Table 2.\n\nThe bottom-up and top-down strategies are used for self-supervision, thus `reducing the algorithmic bias`. They are not used for identifying multi-head dependencies. According to Shi and Huang (2019)[1], only around 6\\% of the utterances have multiple heads. Although multi-head dependencies are considered during the training process, data with 6\\% percent multi-head labels are not sufficient/enough to train a multi-label classifier.\n\n_Response to question F (2)_:\n\nThe SWM helps identify long-distance dependencies. This can be verified by the comparison between our model and two baselines in papers [2] and [3]. In paper [2], the authors set a fixed window and ignored the dependencies outside the window, which means their model fails to capture long-distance dependencies. Comparing Figure 6 in paper [3] and Figure 5 in our paper, it can be observed that the performance of the model in paper[3] on STAC drops below 50\\% as the dialogue lengths increase to 13; while our model maintains the performance above 50\\% until the dialogue lengths increase to 16. Similarly, the better performance in identifying long-distance dependencies of our model can be observed on the Molweni dataset.\n\n_Response to question F (3)_:\n\nTA-DialogDP captures the state transition which can be used by the models(e.g., SKIER) employing the parsed graph on the downstream ERC task. \n\nOn average, our proposed methods outperform SKEIR without TL by 0.83 and 0.86 in terms of 7-cls and 3-cls, respectively. SKEIR without TL utilized a parsed graph with existing relation taxonomy while our method (TA-DialogDP) employed a parsed graph with task-aware relation labels. SKIER\\* denotes that the model was fine-tuned on `human-annotated` links and relations. Our TA-DialogDP achieved results comparable with SKIER\\* although it was not finetuned with human-annotated links and relations on downstream dataset.\n\nWe will add an in-depth analysis to illustrate how our model alleviates algorithmic bias, why it is skilled at identifying long-distance dependencies, and how the ERC model was enhanced by the task-aware mechanism.\n\n[1] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., \\& Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6), 1-35.\n\n[2] Shi Z, Huang M. A deep sequential model for discourse parsing on multi-party dialogues. In Proceedings of the AAAI Conference on Artificial Intelligence 2019 Jul 17 (Vol. 33, No. 01, pp. 7007-7014).\n\n[3] Yu, N., Fu, G., \\& Zhang, M. (2022, October). Speaker-Aware Discourse Parsing on Multi-Party Dialogues. In Proceedings of the 29th International Conference on Computational Linguistics (pp. 5372-5382).\n\n**A. In ChatGPT baseline, how to select the demonstrations? How to deal with the occasional failure of ChatGPT to follow instructions?**\n\nRe: Thanks for your question.\n\n_Response to question A (1)_:\n\nWe are sorry that we are not sure about what the demonstrations refer to. We will release the code of the whole project including the experiments of ChatGPT baseline.\n\n_Response to question A (2)_:\n\nWe designed a mechanism based on regular expressions to examine the retrieved results. The algorithm will repeatedly retrieve the response for the same input until the result passes the examination mechanism.\n\n**Missing References:\nA Distance-Aware Multi-Task Framework for Conversational Discourse Parsing, COLING, 2022. Structured Dialogue Discourse Parsing, SIGDIAL, 2022.**\n\nRe: Thanks for your suggestion. We will cite and discuss these papers in the related work section.", "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": "A task-aware self-supervised framework is proposed for dialogue discourse parsing.\n\nExperimental results show the effectiveness of the proposed framework.", "reasons_to_reject": "This paper has the following weaknesses:\n\nA. The example in Figure 1 lacks the data source. It does not come from either Molweni or STAC used in this paper.\n\nB. Lack of a clear motivation. This paper argued that there are three problems, including error propagation, learning bias, and incompatibility of predefined relations, that need to be addressed. However, it doesn't give a clear explanation of why these problems need to be solved.  For example, why is learning bias an issue worth considering, given that Molweni and STAC have few multi-head dependencies? What impact does the incompatibility of predefined relations have on downstream tasks?\n\nC. Unclear experimental settings. In Section 3.3, how to solve the BERT token limitation, since the whole dialogue is concatenated? In dataset processing, if you followed Shi et al. to pre-process a dataset that doesn't consider multi-head dependencies, what is the effect of your top-down and bottom-up strategy that considers multi-head dependencies?\n\nD. Unclear description in Section 3.8. What is the motivation for transition? Why does the existing relation taxonomy may not fit the ERC tasks? Why could this transition better facilitate ERC tasks? What principles is the transition based on? \n\nE. Unfair comparison. This paper adopted the BERT-large, which is not fair to directly compare to the works adopting the base version. Therefore, it is not clear whether the performance improvement comes from the proposed method or from using a larger pre-trained model.\n\nF. An in-depth analysis of each component in the proposed method is required. Since top-down and bottom-up are used to alleviate the problem of learning bias, does this strategy better identify multi-head dependencies? Since SWM is devised to alleviate the problem of long-range dependencies, does it help identify longer-distance dependencies? Besides, what problems in ERC are solved by TA-DialogDP? The overall performance is not convincing and an in-depth analysis is required.", "questions_to_authors": "A. In ChatGPT baseline, how to select the demonstrations?  How to deal with the occasional failure of ChatGPT to follow instructions?", "paper_topic_and_main_contributions": "This paper proposed a task-aware self-supervised framework for dialogue discourse parsing. It designed a graph-based model to alleviate error propagation. Besides, a bottom-up and top-down parsing strategy was introduced to alleviate the problem of learning bias. Empirical studies on dialogue discourse parsing datasets and a downstream task demonstrate the effectiveness and flexibility of the proposed framework.", "excitement": 2, "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": {"Missing_References": "A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing, COLING, 2022.\n\nStructured Dialogue Discourse Parsing, SIGDIAL, 2022."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a method to do dialog discourse parsing (DDP), which is distinct from other forms of discourse parsing and benchmarks.  (The authors present this as a "fundamental NLP task", but that would mean that most other NLP tasks require it, which is not quite the case in practice.) The method includes a combination of both bottom-up and top-down parsing, producing a pair of graphs that need joint decoding.  This is aided by a singular triangular window (SWT) mechanism to provide soft constraints on the way links are established between utterances.

The reviewers rightly pointed out the results are an improvement but rather marginal.  Also, the analysis on the Molweni task/dataset could be improved.  There were also concerns about reproducibility and comparison to GNN's, some of which were addressed in rebuttal.
Decision: Accept-Findings