How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning
Authors: Hang Chen
Xinyu Yang
Jing Luo
Wenjing Zhu
Venue: EMNLP 2023 Main (2023)
Keywords: Causal Discrimination
Conversation
Independent Noise
SCM
TL;DR: We induce the i.i.d. noise into embedding to enhance the causal discrimination of models in the case of the affective reasoning task

Abstract: Our investigation into the Affective Reasoning in Conversation (ARC) task highlights the challenge of causal discrimination. Almost all existing models, including large language models (LLMs), excel at capturing semantic correlations within utterance embeddings but fall short in determining the specific causal relationships. To overcome this limitation, we propose the incorporation of \textit{i.i.d.} noise terms into the conversation process, thereby constructing a structural causal model (SCM). It explores how distinct causal relationships of fitted embeddings can be discerned through independent conditions. To facilitate the implementation of deep learning, we introduce the cogn frameworks to handle unstructured conversation data, and employ an autoencoder architecture to regard the unobservable noise as learnable ``implicit causes.'' Moreover, we curate a synthetic dataset that includes i.i.d. noise. Through comprehensive experiments, we validate the effectiveness and interpretability of our approach. Our code is available in https://github.com/Zodiark-ch/mater-of-our-EMNLP2023-paper.

How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning
Hang Chen and Xinyu Yang and Jing Luo
Wenjing Zhu
Xi'an Jiaotong University {albert2123, www.looojnngeee xjtu. edu. cn yxyphdemail. xjtu. edu. cn
Du Xiao Man Inc.
zhuvernjingg2euuxiiomm..ccm com
Abstract
their embeddings can be fitted, various possible relationships exist: A acts as the cause of B (A + B), acts as the outcome of B (A + B), or more complex, A and B are both influenced by a common cause (A < C + B), and so on. Partic- ularly in reasoning tasks (Uymaz and Metin, 2022; Feng et al., 2022), it is crucial for these methods to transcend the mere fitting of embeddings and possess the capacity to discriminate diverse causal relationships. (i.e., the ability of causal diserimi- nation) (Bao et al., 2022: Shirai et al., 2023).
Our investigation into the Affective Reason- ing in Conversation (ARC) task highlights the challenge of causal discrimination. Almost all existing models, including large language mod- els (LLMs). excel at capturing semantic correla- tions within utterance embeddings but fall short in determining the specific causal re relationships. To overcome this limitation, we propose the in- corporation of i..dd noise terms into the conver- sation process, thereby constructing structural causal model (SCM). It explores how distinct causal relationships of fitted emheddings he discerned through independent conditions. To facilitate the implementation deep learning, we introduce the cogn frameworks handle un- structured conversation data, and employ an au- toencoder architecture regard the unobserv- able noise as learnable "implicit causes." More- over, we curate a synthetic dataset that includes i.idd noise. Through comprehensive experi- ments, we validate the effectiveness and inter- pretability of our approach. Our code is avail- able in https://githi:: com/ Zodi ar paper.
To specifically investigate the causal discrimi- nation capability of existing methods in conversa- tion, we narrow down our research particular task: Affective Reasoning in Conversation (ARC), which has included Emotion Eenotion-Cause Pair Extraction (ECPE) Xia and Ding (2019) and Emotion- Cause Span Recognition (ECSR) Poria et al. (2021).
We begin with conducting tests to evaluate the causal discrimination of existing methods includ- ing the large language models (LLMs) (Kasneci et al., 2023). One typical evaluation involves the causal reversal test: for emotion-caus, utterance pairs with true labels (A, B) representing a causal relationship of B - A. we scrutinize the predic- tions generated by the existing methods using both positive pairs (A, B) and negative pairs (B, A). The results reveal that all the examined methods performed similarly across the two sample types. As we are concerned, they lacked causal discrim- inability. (Details are shown in Section 2.3)
1 Introduction
Nowadays, numerous conversation recognition tasks (such as Emotion Recognition in Conversa- tion (ERC) task (Pereira et al., 2023; Thakur et al., 2023), Intent Recognition (IR) task eYe al., 2023; Ni, 2023) and Dialogue Act Recognition (DAR) task (Arora al., 2023)) have shown promising performance specialized supervised and unsu- pervised methods. Considering the RoBERTa pre- trained model (Liu et al., 2019) as the examples, "My eyelids are fighting" and "I want to sleep." which have similar semantics but different tokens can be well fitted within embeddings. (i.e., these two embeddings exhibit a strong resemblance via certain metries such as cosine similarity.)
In order to discriminate different causal relation- ships between two similar embeddings, we con- struct the dialogue process as a Structural Causal Model (SCM). Many endeavors (Cheng et al., 2022; Nogueira et al., 2022) supporting that i.i.d. noise of SCM could facilitate the discrimination of causal relationships when fitting two variables. Under the presence of noise, each utterance is not only explicitly influenced by the other utterances but also implicitly influenced by the i.i.d. exoge-
However, when it comes to the relationship be- tween two utterances, denoted as and B, wherein

---

nous noise. Consequently, this framework ensures that two fitted embeddings result in diverse causal relationships, which are determined by correspond- ing independent conditions between the residual terms and embeddings. For simplicity, we refer to other utterances as explicit causes and exogenous noise as implicit causes.
2 Related Works and Challenges
2.1 Task Definition
For notational consistency, we use the following ter- minology. The target utterance U3 is the t utter- ances of a conversation D = (U,, U2, U3.....UU Un) where N is the maximum number of utterances in this conversation and 0 < t N. The emo- tion label Emo, denotes the emotion type of Ug. The emotion-cause pair (ECP) a pair (Uz, U?), where Ug is the 5th utterance of this conversation. In the ECP, U, represents the emotion utterance and U, is the corresponding cause utterance. Moreover, the cause label Ct, denotes the cause span type of the ECP (U,, Ut)-
Furthermore, to enable the learnability of such causal discrimination within embeddings, we pro- pose common skeleton, named centering one graph node (cogn) skeleton for each utterance de- rived from some broadly accepted prior hypothe- It can address the challenges arising from variable-length and unstructured dialogue samples. Subsequently, we develop an autoencoder archi- tecture learn the unobservable implicit causes. Specifically, we consider the implicit causes as la- tent variables and utilize a graph attention network (GAT) (Veličković et al., 2017) to encode its repre- sentation. Additionally, the decoder leverages the inverse matrix of the causal strength, ensuring accurate retrieval of the causal relationships.
Thus, in given text, ERC is the task of identi- fying all Emo; Moreover, ECPE aims to extract set of ECPs and ECSR aims to identify all Ct
2.2 Affective Reasoning in Conversation
Chen al. (2018) introduced the pioneering work on ERC due the growing availability of public conversational data. Building upon this, Xia and Ding (2019) further advanced the field by propos- ing the ECPE that jointly identifies both emotions and their corresponding causes. Moreover, Poria et al. (2021) has extended ECPE into conversations and proposed novel ECSR task, specifically de- signed to identify ECP spans within conversation contexts. More recently, increasing works have indicated the crucial role played by accurate in- ference models in facilitating complex reasoning within these tasks, such as the assumption about interlocutors (Zhang et al., 2019; Lian et al., 2021; Shen et al., 2021) and context (Ghosal et al., 2019; Shen et al., 2022; Chen et al., 2023).
Finally, we conduct extensive experimental eval- uations: 1) our approach significantly outperforms existing methods including prominent LLMs (GPT- 3.5 and GPT-4) in two affective reasoning tasks (ECPE and ECSR) and one emotion recognition task (ERC), demonstrating its effectiveness affec- tive reasoning. 2) our method exhibits significant reduction in false predictions for negative samples across three causal discrimination scenarios. 3) we curate a synthetic dataset with implicit causes to visualize the latent variable in our implementation. Our contribution four-fold:
We formulated the dialogue process as am SCM and analyzed the causal relationships represented by different independent condi- tions.
2.3 Challenge of Affective Reasoning
We examined the performance of a range of meth- ods for addressing affective reasoning in conver- sations, including both unsupervised approaches (large language models (LLMs), BERT-based pre- trained models) and supervised approaches (task- related approaches).
We devised the cogn skeleton to address the problems of variable-length and unstructured dialogue samples.
Overall, all the methods demonstrated a lack of discriminabiiity on two types challenges:
We adopted an autoencoder architecture to overcome the unobservability of implicit causes and make it learnable.
Samples where emotional utterances and causal utterances are interchanged. For a di- alogue instance, if the ECP is (U, (U2 is the cause of U1), the prediction results ob- tained by the existing methods tend to include both (U,, U2) and (Uz, U1).
We constructed synthetic dataset with im- plicit causes and conducted extensive evalua- tions of our proposed method.

---

Methods
Challenge
Challenge
nother speaker home late and forgot call.
(U%.UG)
(Ua.U,)
(U,.5a)
(U2,55)
[ts my bad not calling. [5ad]
[Ejlmpii cause
G.T
112
102
108
114
109
GPT-4
127
114
111
105
103
[U (U,Don't bother coming home. JAngry]
RoBERTa
97
83
Jmplicck cause
RaBERTa"
97
91
105
101
106
[Uu]You're going to KICK me out? (Spprizdd
RANK-CP
142
125
129
131
[Eylimpiict cause
ECPE-2D
151
153
142
138
146
EGAT
166
154
157
139
148
JE xacily. JAnany]
E-Jigiic cause
Table 1: For the two challenges mentioned in Sec- tion 2.3, we conducted tests on subset 200 samples from the RECCON dataset. We recorded the number of samples identified by above methods. In the second row of Challenge we showed the count of samples where these methods extracted the negative pairs in reverse cause order. Similarly, in the third row of Challenge 2. showed the count of samples where these methods extracted negative indirect pairs.
[U,]] won't have isten you. (argyy
Eylmpict cause
ie.. This speaker w wanted his rules to respected but broken now.
E
E3
U==E 12= +E2
Ug
U3= + + = U Sicssb+taace + +
U2
U4
The conversation case with five utterances. In the SCM, we assume that each utterance U, has a corresponding implicit cause E,, and has several explicit causes. i.e., U, has an implicit cause Eg and two explicit causes U3 and U2. In the lower part of the figure, SCM adopts U, = Σ aaU + E, to denote these relationships and formalize the conversation as DAG.
Figure I:
Samples with indirect connections. For exam- ple, if the ECPs in a conversation are (U1, U2) and (U2, U3), the prediction results obtained by the methods often include an additional pair (U1, Ug).
We evaluated the performance of cxisting methods these two challenges, and the detailed results are shown in Table All evaluated methods ex- tracted nearly equal number of negative samples as positive samples. Considering their performance in broad research domains, both unsupervised and supervised methods could demonstrate a desirable fitting ability to capture the semantic similarity be- tween two utterances. This often apparently results in satisfactory performance in most tasks. How- ever, when it comes to more specific causal re- lationships within semantically similar sentences (such as reasoning tasks), they may not exhibit the same level of "intelligence" and output some reseudo-correlation"
The noise terms (also called exogenous variables) for each variable, enables methods such Indepen- dent Component Analysis (ICA) identify more comprehensive causal relationships between the two fitted variables.
3 Methodology
In this section, we begin by outlining incorporat- ing i.i.d. noise terms into dialogue model to construct an SCM in Section 3.1, demonstrating independent residual allowing for the identification of more specific causal relations within pairs of fit- ted utterances. Next, to mitigate conflicts between SCM models and dialogue data, we designed cogn skeletons with six instantiations in Section 3.2. Fi- nally, we propose deep learning implementation to tackle the issue of noise being unknown in dia- logue data in Section 3.3.
In the area of causal discovery, Causal Markov and Faithfulness Assumptions (Spirtes al., 2000; Colombo et al., 2012: Ogarrio et al., 2016). provide insights into capturing more specific causal rela- tionships the situation of the above challenges. Considering two similar variables: and B that can be fitted, the independence tests enable the de- termination of specific causal relationships, such
3.1 Structural Causal Model
In order to imbue causal discriminability into the fitting process of two relevant utterances, we al- gebraically construct the conversation model as a Structural Causal Model (SCM).
as "Λ > B," "B > A," or 11 - I. + B". More recently, the Structural Causal Model (SCM) (Shimizu al., 2006; Shimizu and Bollen, 2014; Samchez-Romero et al., 2019) built upon the inde- pendent noise assumptions has emerged as an effec- tive approach to the limitation of Markov equiva- Ience classes in distinguishing causal relationships.
Definition An SCM of a dialogue is 3 ple (U, F,F),, where U is the set of utterances Ji E the set exogenous noises E (E)) corresponding to each U, N is
U = [U:)

---

the number of utterances. Note that each E; is independent in the SCM. Structural equations = [fi)]=1 are functions that determine U with U, = -f((re)y,) + E,, where rely, denotes a set of utterances that point to the Uz-
learning strategies for recovering the relationships between variables. However, utterances differ
from the variables that causal discovery often uses. Specifically, each conversation has dif- ferent amount (N) of utterances, and different inter-utterances relationships related to the context. Hence, it is intractable to build general causal skeleton with fixed nodes and edges to describe all conversation samples.
Definition establishes the construction of novel computational model for dialogue process, exemplified in Figure In such computa- tional model, each utterance is endogenous and influenced by an independent exogenous variable. For simplicity, we refer to the variable U as the explicit causes and the variable E as the implicit causes. The independence of the implicit causes makes the residual terms meaningful during the fitting of any two utterances.
Fortunately, several published GNN-based ap- proaches (Shen et al., 2021; Ishiwatari et al., 2021; Ghosal et al., 2019; Lian et al., 2021; Zhang et al.. 2019) in ERC have proposed and verified a com- mon hypothesis to settle down this issue. The Hy- potheses are elaborated on in Appendix B.
Definition 2: The relationship of two utterances X and Y in dialogue is causal discriminable. from the independent conditions:
Ex Il E, X
0
00
10
20
IV
VI
11 x + X
1 X + X
Ex IL E, 11 x = X L, - L
1
10
Figure Six cogn skeletons from a conversation case with 12 utterances. We adopted the 7-th utterance the target utterance (Red). Orange nodes denote the utterances of the same speaker the target utterance, and blue ones denote those belonging other speakers. Arrow represents the information propagated from one utterance to another, and the bi-way arrow represents the influence- relationship. The black dash box represents slide windows.
where Σ represents the residual terms in fitting process. (The proof is shown in Appendix A.) Example 1: A 4-utterance dialogue SCM D {UU,, U2 Us Ua), {E1, E2, Ez, Ea), {a, b, c}} with the true relationships are U1 = Fr.
Un = aU1 + Eg. U3- = bU1 + Es. U== = cU3 + Eq. The fitting of U2 with U3 and U4 yield U2 4Ug- + 004 Eu., while the fitting of Uz with U2 and U4 yield U3- U + L + Eog- Additionally, the fitting of U4 with U2 and U3 lead to U4 0U2 + cU3- + Ev,-
Figure 2 showcases the design of six cogn skele- tons, derived from the latest works that have em- ployed one or more of these hypotheses. The statis- tic and specific algorithms are also shown in Ap- pendix B. Note that only conduct experiments for II-I because structure is hard to apply with the recurrent-based skeleton.
In Example 1, it is observed that any two utter- ances can be fitted together as they are mutually dependent. However, causal discriminability can be employed to differentiate their distinct causal structures. For instance, the residual term Ev, is not independent of U4, while ΣV, is independent U3. indicating that U3 a cause of U4. Further- more, the residual term Σv, is not independent of Et,, and Σv, is not independent of U3. implying the presence of common cause (U,) between U2
3.3 Approach Implementation
From a given causal skeleton, a linear SCM can be equivalently represented as:
U, = Σ on Er
(I)
and Ug.
iErel
3.2 Causal Skeleton Estimation
where rel, denotes a set of utterances that point the U, (7-th utterance) in Figure 2, E, represents the exogenous variable towards the variable U, in
Establishing a skeleton is the first step in causal discovery, as different skeletons provide distinct

---

XL
X
A Mask
(1-A)
Mank Mank
masiedd I-A
masked (1-A fra
Up
Matrix
SCM
Uz
inverse
Us
Causal representation
Up
Up
Graph Attention
Up ....
Hypothesss
3,4
Word embedding
GNN
GNN
H
E
H
Causal Skeleton
Causal Encoder
Causal Decoder
Figure 3: Processing of our approaches, with ux-uuteranccss conversation case the input. Causal skeleton indicates which utterances (nodes) are used for aggregation. For each layer f, we collect representations H for all utterances where each row represents one utterance. Causal Encoder yields implicit causes E², the input for Decoder learning the causal representation. In all matrices, light gray nodes represent the masked part.
SCM, as well as the implicit cause towards the ut- terance U, conversation. Furthermore, we denote
represents the layer of GNN. Thus, for each utterance at ê-th layer, the A computed by attention mechanism is a weighted combination of h for each directly related utter- ance U.(i E rel,):
= 1,2,
the word embedding of U by H = h1, h2,.... ....... ,,,, and the relationships between utterances in rows can also be written as: H = A"H H + E, where Ai + 0 stands for directed edge from U; to Ur in SCM. Thus can define the Graph 9 = (V,E) with adjacency matrix A,; for all i. However, in this equation, only H known. The unknown variable typically the target of inference, while the unknown variable represents exogenous variables that implicitly influence each utterance, such as the speaker's s memory, experi- ences, or desires. (Krych-Appelbaum et al., 2007; Sidera al., 2018) These factors are typically miss- ing in existing conversation resources. Therefore, determining A based on completely unknown E is another problem we aim address.
Leaky LeakyReLU((L))
A Ejerel LeakyReLUUe,,
(4)
e0 = T Wi s(row) + ((co)))
+he b(oo)))
(5)
where W row E R and W cat E R are the learnable parameters in the graph attention. More- over, the GNN aggregates the information from the neighbor utterances as following:
H1 = e - (A9))
(6)
Hence, we treat AT as an autoregression matrix of the 9, and then E can be yielded by an auto- encoder model. The whole process reads:
where 8 stands for parameters in the correspond- ing layer. From the final layer of the evaluation pro- cess, by extracting A computed in Equation 4, (2) the marginal or conditional "distribution" of is obtained, showing how to discover Causal Graph g from D. Besides, by extracting H in Equation 6, we can obtain the independent embedding for the implicit causes E (
E == (H)
á - g A") 'E)
(3)
where f(.) and g(-) represent the encoder and decoder neural networks respectively. Encoder aims to generate an implicit cause E, and De- coder devotes to yielding a causal representation H. From Equation I, causal representation H, rea- sons about the fusion relations of heterogencous explicit causes Eicrel. H; and implicit cause E,. The details of this process shown in Figure 3. Encoder. We use the graph attention mecha- nism to learn the adjacency matrix A and con- struct hierarchical GNN to instantiate the f()).
Decoder. We utilize the A and E computed from Encoder generate the causal representation स. With a fixed adjacency matrix A, the GNN aggregates the information of implicit causes from neighbor nodes as follows:
Be+1 = - ((4(((((())))) (7)
where A is parameters in the corresponding layer. As the same architecture as the encoder, A

---

MLP(EF). Additionally, the plug-in RNN is in- tegrated with GNN to address the appetite of Hy- pothesis
conversations
tasks
Dataset
Train
Val
Test
ERC
ECPE
BCSR
DailyDialog
11118
1000
1000
MELD
1038
114
280
EmoryNLP
713
99
85
1 GRU p
(8)
RECCON-DD
IEMOCAP
833
100
20
225
31
RECCON-IE
16
where of is the state of GRU model, with com- puted by self-attention proposed by Thost and Chen (2021).
Symthtic data
Table 2: The statistics seven datasets
3.4 Optimization
4.1 Datasets, Implementation and Baselines
In our approach, H and H acts identically under the linear SCM model. Similarly, H should be aligned with H in emotion dimensions under the non-linear SCM model. In short, adopt an auxiliary loss measuring the Kullback-Leibler (KL) divergence (Joyce, 2011) of H and H mapped into the exact emotion dimensions. Moreover, implicit causes E is one of the crucial influence factors on H, so that the loss aims to impose the constraint that H is the embedding of our need to ensure generating correct E.
We use six real datasets for three affective reason- ing tasks and one synthetic dataset for justifying E in our model. The statistics of them are shown in Table 2. Appendix C depicts the detailed introduc- tions of cach dataset.
We adopt the consistent benchmarks of the SOTA methods in three tasks, including the pre- training language model, hyper-parameters, t-tests, and metrics. The implementation details are shown in Appendix D.
According to the hypotheses of these baselines, for each cogn skeleton, we choose one recent SOTA work: II: DialogXL (Shen et al., 2022). III: EGAT (Chen et al., 2023). IV: RGAT (Ishiwatari et al., 2021). V: DECN (Lian et al., 2021). VI: DAG-ERC (Shen et al., 2021).
(Ur) Pe (Ut)))
LOSSKL Σ E
Pe (U)) log
(9)
eE EmEe
where e is any emotion type in Emo,, Pe denotes the probability labeled with emotion e. In the whole process of ARC tasks, we followed (Wei et al., 2020; Poria et al., 2021) add several losses of ECPE and ECSR respectively.
4.2 Overall Performance (RQ1)
Table 3 reports the results of ECPE and ECSR, with 0.01 in the r-test, where the best improvement and best performance both concentrate on VI. With the visualization of Appendix F,, we infer that the upper triangular adjacency matrix of DAG-ERC, not restricted by the backpropagation, benefits from Hypothesis 6. Moreover, II lags farthest behind in the ECPE while achieving the second best in the ECSR, showing that the reliance on hypothesis is not equal in different tasks. Furthermore, without Hypotheses 1 and 6, III, IV, and V are far from the best performance since Hypothesis has the maxi- mum identifying space, and Hy pothesis supplies the highest number of information passing. Finally, is worth noting that three skeleton-agnosti base- lines and unsupervised methods perform poorly in the RECCON-IE dataset, indicating that our mod- els have stronger representation learning capabili- ties well highlighting the continued research value of affective reasoning tasks.
Furthermore, we would like to explain the differ- ence between our approach and Variational Auto- Encoder (VAE) (Kingma and Welling, 2014). The output of the encoder in VAE is 90( (Z). With this estimation of (Z). we can measure the variation (Z)( (also called V (?s to ob- tain the approximation estimation of (L) In contrast, our output E, fixed matrix rather than a distribution. In other words, the VAE depends on the prior distribution over the latent variables, whereas our approach has a dependency on the con- sistency of H and H, which is non-sampling and non din-distri
4 Experiments
In this section, we conduct extensive experiments to answer the 3 research questions:
RQ1: How effective is our method in affective reasoning tasks?
RQ2: How do we justify the causal discrim- inability of our method?
We further conducted six sets of ablation experi- ments to study the effects of different modules. Table 4, we summarized results under the following cases: replacing Loss I. with BC E loss function
RQ3: How do we gauge the difference between the latent variable E and designed implicit causes?

---

Ski
model
ВCPE RECCON
ECSR RECCON
Methods
Reversal
Chain
Common Cause
DDC=
IE
1O
1E
Pos
Neg
Pus
Neg
Pos
Neg
GPT-3.5
38.13
39.55
10:49
5.36
GPT-3.5
45.9
41.2
43.4
44.7
41.6
36.4
GPT-4
46.29
49.32
16.81
18.39
GPT-
49.3
46.2
48.9
43.7
47.7
48.1
RoBERTa
53.91:11.5
38.77
31.52.00.8
20.16
RoBERTa
53.9
52
56.4
59.3
56.7
RoBERTa
54.62+1.1
38.26
33.28:40.7
26.37
RoBERIa
56.7
54.9
58.7
54.6
RANK-CP
63.51412.1
41.56
26.57:10.8
18.99
RANK-CP
61.7
62.5
63.4
61.5
65.9
63.7
ECPE-2D1
64.35#1.7
47.42
34,41=0.11
22.03
ECPE-2D
63.9
62.8
64.6
61.3
63.3
61.9
II
Di
6192E1.7
50.31
35.79::0.5
21.78
Di
64.8
60.8
61.9
63.8
65
66.1
+Ours
6..74
51.23
34.63-10.2
27.92
EGAT
68.7
64.3
68.8
64.8
66.2
64.3
BOAT
68.05-
53.43
29.6
16.42
III
RGAT
69.4
61.7
68.9
66.4
68.3
68.5
+Ours
69.16+1.2
53,81
3..
18.55
DECN
66.7
62.4
70,5
64.3
69.2
66.1
RGATT
69,02:199
52.48
30.39:10.4
17.49
IV
DAG-ERC
71.5
68.2
72.4
64.2
69.3
66.1
-Ours
70.12:22.1
53.93
30,24:40055
19.31
DECNT
68. 1.
51.73
30.7:10.9
18.47
Ours
76.2
46.1
73.8
41.9
77.2
48.6
V
+Ours
68.84:11.7
53.89
31.88:1-0.2
20.13
DAG-ERCT
10.36:1.5
55.7
40.12:0077
24.89
Table 5: Results of causal discriminability. The calcu-
VI
+Ours
73.17:1.11
56.67
42.14:10.1
30.41
lation results = count of predicted results that matched the samples / total number of samples *100, The three causal models are: Reversal Model: Positive samples (i. j) and negative samples (. i); Chain Model: Posi- tive samples (i, k) and (k, j), and negative samples (i, i): Common Cause Model: Positive samples (k. i) and
Table 3: Overall performance in ECPE and ECSR tasks. We additionally compare four unsupervised approaches and two baselines not belonging any skeleton: RANK- CP (Wei et al., 2020), ECPE-2D (Ding et al., 2020). The RoBERTa* represents the large RoBERTa version (1024 dimensions). The DD and IE subsets (see Appendix C).
(k, j). and negative samples (i, j) or (j. i). The met- ric "Pos" represents the percentage of positive samples, indicating the extraction capability. Higher Pos sam- ples imply better extraction capability. The metric "Neg" represents the percentage of negative samples. smaller difference between Pos and Neg indicates a weaker causal discriminability.
Categories
Model
11
III
IV
V
VI
Ours
64.74
69.16
70.12
68.84
73.17
BCE
10.62
10.04
J0.15
10.16
J0.29
w/ LossKt.
12.18
11.95
J2.42
|1.33
C1.58
w/o Decoder
13.59
42.79
J2.11
j2.83
.4144
w/o Hypo 6
C.55
w/o Hypo 5
12.34
C1.8
Furthermore, Appendix reports the results of
w/o Hypo 4
13.67
J2.72
13.15
.4.19
ERC task and sensitivity experiments to analyze how our model performs different L and k.
Table 4: Ablation results
EBC removing the LOsS (w/o LossK1); re- placing Decoder module with a Linear layer (w/o Decoder); removing the RNN module (w/o Hypo 6); adding the edges from successors to predeces- sors (w/o Hypo 5); reducing the speaker types to one (w/o Hypo 4).
4.3 Relationship analysis (RQ2)
We are also concerned about the causal discrim- inability for similar utterances. Table 5 demon- strates that in all three different causal models, none of the methods were able to distinguish between negative and positive samples. Because both neg- ative and positive samples can be fit within these three causal models, solely from an embedding similarity perspective. However, our method signif- icantly decreases the percentage negative sam- ples indicating the effectiveness of incorporating implicit cause noise to enhance causal discrimina- tive ability.
As shown in Table 4, BCoss performs simi- larly to LossKL; thus, we empirically demonstrate that our auxiliary loss is essentially different from LossKL in VAE. The Fl score decreases heavily without auxiliary loss or decoder, these two are necessary ingredients for building complete pro- cessing to learn the causal representation via E. Besides, Hypotheses 4, 5, and 6 are all critical but removing Hypothesis 4 leads to the highlight degradation in 3 skeletons. This result corrobo- rates the theory of Lian al. (2021) and Shen et al. (2021), who state that speaker identity is the strong inductive bias in conversation. Finally, it is ex- pected to see that skeleton with Hypotheses 4, 5, and 6 should be the closest to perfection while the DAG-ERC+ Ours indeed achieves the SOTA.
Additionally, we show the adjacent matrices of our model and current SOTA methods in Ap- pendix which indicates that our model can more freely explore the relationship between different ut- terances via adjacent matrices shifting rather than being limited to a fixed structure (c.g., attention module).

---

founders. In Tables 7, and 8, skeletons II, III. and IV generally lag far behind V and VI. This unsatisfactory performance of these skeletons in- dicates that excessive adding-edse edgge leads to serious confounders.
Therefore, proposed theoretical design for testifying the existing of latent confounders:
Confounding between Non-adjacent Nodes: Consider two utterances U; and Uj being non- adjacent utterances. Let Pa be the union of the parents of Ug and Uy: Pa = U, U Uj. If we per- form an intervention on Pa (i.e., do(Pu = pa)). thus have Uj IL Uj if and only if there is latent confounder L such that U; L Uj-
without noise
(b) with noise
Figure 4: Visualization of E' (upper subfigures) and implicit causes (lower subfigures) with colors the simulated datasets. The gray cluster means padding ut- terances in each dialogue, the blue cluster corresponds the non-emotion utterances, and the red cluster corre- sponds to emotion utterances.
Confounding between Adjacent Nodes: Con- sider two utterances U, and U, being adjacent utter- ances: U; + Uy. If there are no latent confounders. we have P(U,(U:) P(U,Ido(U; = u4)).
4.4 Implicit Causes Study (RQ3)
The latent variable E is intended to represent the mentioned implicit causes. Therefore, the global distribution of the latent variable E should be ap- proximately equal the one of implicit causes. Although human evaluation labels are better for proving reasonable performance, annotate implicit causes due to their unobservabil- ity. We thus trained our model in synthetic dataset given a set of fixed i.i.d. implicit causes to observe how the E' is similar to the ground truth implicit causes distributions. Figure 4 (a-b) shows the pro- jection of E and implicit causes, respectively, using t-SNE (Knyazev al., 2019). We observe that E and implicit causes are both similarly clustered into three parts through the distribution properties. E is consistent with the implicit causes in the samples with or without noise indicating that E successfully learns the implicit causes.
Indeed, implementing intervention operations on conversation data poses a significant challenge. Therefore, in our new work, we have proposed gen- eral intervention writing: do(X) := Pa(X) = 0 where Pa(X) denotes the parent set. Moreover, the most significant obstacle to further research is the lack of high-quality dataset with complete causal relationship labels. Hence, have con- structed simulated dialogue dataset via GPT-4 and plan to make it open soon.
intractable to
Conclusion
The results of testing prevalent approaches on the ARC task have demonstrated that almost all ap- proaches are unable to determine the specific causal relationship that leads to the association of two well-fitted embeddings. In order to enhance the causal discrimination of existing methods, we con- structed SCM with i.i.d. noise terms, and ana- lyzed the independent conditions that identify the causal relationships between two fitted utter- ances. Moreover, we proposed the cogn framework to address the unstructured nature of conversation data, designed an autoencoder implementation to make implicit cause learnable, and created a syn- thetic dataset with noise labels for comprehensive experimental evaluation. While our method still has some limitations, such confounders and the inability to scale to all methods, hope that our theory, design, and model can provide valuable in- sights for the broader exploration of this problem to demonstrate that our work is de facto need for identifying causal relationships.
Moreover, in Appendix G, we first prove the approximate emotion consistency between utter- ance U, and its implicit causes when U, and U; in the emotion-cause pair (U,, U&) do not belong to the same emotion category. Then, we demonstrate through the ERC task that by replacing f/ with E. the emotion consistency provided by implicit causes is preserved.
4.5 Limitations
In our model, our method can distinguish between U, U, and U, Un + Uy. However, our method is unable to distinguish between U, -> U, and U; + + Uy. where L represents a un- observed variable, called common causes or con-

---

References
in Natural Language Processing and the 9th Inter- national Joint Conference on Natural Language Pro- cessing (EMNIP-IJCN(PP) pages 154-164, Hong Kong, China. Association for Computational Lin- guistics.
Raj Agrawal, Chandler Squires, Neha Prasad, and Car- oline Uhler. 2021. The decamfounder: Non-linen causal discovery in the presence of hidden variables. arXiv preprint arXiv:2102.0792..
Taichi Ishiwatari. Yuki Yasuda, Taro Miyazaki, and Jun Goto. 2021. Relation-aware graph attention net- works with relational position encodings for emotion recognition in conversations, In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7360-7370, Online. Association for Computational Linguistics.
Siddhant Arora, Hayato Futami, Emiru Tsunoo, Brian Yan, and Shinji Watanabe. 2023. Joint modelling of spoken language understanding tasks with integrated dialog history. In ICASSP 2023-2023 IEEE Interna- tional Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE
Yinan Bao, Qianwen Ma, Lingwei Wei, Wei Zhou, and Songlin Hu. 2022. Multi-granularity seman- tic aware graph model for reducing position bias emotion-cause pair extraction. arXiv preprint arXiv:2205.02132.
James M Joyce. 2011. Kullback-leibler divergence. In International encyclopedia of statistical science, pages 720-722. Springer.
Enkelejda Kasneci, Kathrin SeBler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, al. 2023. Chatgpt for good? on op- portunities and challenges of large language models for education. Learning and Individual Differences, 103: 102274.
Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jean- nette N Chang. Sungbok Lec, and Shrikanth Narayanan. 2008. lemocap: Interactive emotional dyadic motion capture datahase. Language resources and evaluation, 335453335-359.
D. Kingma and M. Welling. 2014. Auto-encoding variational hayes. arXiv.org.
Hang Chen, Xinyu Yang, and Chenguang Li. 2023. Learning general clause-to-claus, relationships for enhancing emotion-causs pair extraction.
Boris Knyazev, Grabam W Taylor, and Mohamed Amer. 2019. Understanding attention and generalization in graph neural networks. Advances in neural informa- tion processing systems, 32.
Sheng-Yeh Yeh Chen, Chao-Chun Hsu, Chuan-Chus Kuo, Lun-Wei Ku, et al. 2018. Emotionlines: An emotion corpus of multi-party conversations arXiv preprint arXiv:1802.08379.
Meredyth Krych Appelbauu, Julie Banzon Law, Dayna Jones, Allyson Barnacz, Amanda Johnson, and Ju- lian Paul Keenan. 2007. "i think know what you mean": " The role of theory of mind in collaborative communication. Interaction Studies, 8(2):267-28..
Lu Cheng, Ruocheng Guo, Raha Moraffah, Paras Sheth, Kasim Selcuk Candan, and Huan Liu. 2022. Evalua- tion methods and measures for causal learning algo- rithms. IEEE Transactions on Artificial Intellignce.
Diego Colombo, Marloes Maathuis, Markus Kalisch, and Thomas S Richardson. 2012. Learning high- dimensional directed acyclic graphs with latent and selection variables. The Annals of Statistics, pages 294-321.
Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017. DailyDialog: A manually labelled multi mulli-turm turn dialogue dataset. In Proceedings of the Eighth International Joint Conference on Nat- ural Language Processing (Volume I: Long Papers), pages 986-995, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Zixiang Ding, Rui Xia, and Jianfei Yu. 2020. ECPE-2D: Emmotion-cause pair extraction based on joint two- dimensional representation, interaction and predic- tion. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pages 3161-3170, Online. Association for Computational Linguistics.
Zheng Lian, Bin Liu, and Jianhua Tao. 2021. Decn: Dia- logical emotion correction network for conversational emotion recognition. Neurocomputing, 454:483- 495.
Yinhan Liu, Myle Oit, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining ap- proach. arXiv preprint 1907.
Shutong Feng, Nurul Lubis, Christian Geishauser, Hsien-chin Lin, Michael Heck, Carel van Niekerk, and Milica Gašic. 2022. Emowoz: A large-scale cor- pus and labelling scheme for emotion recognition in task-oriented dialogue systems.
Navonil Majumder, Soujanya Poria, Devamanyu Haz- arika, Rada Mihalcea. Alexander Gelbukh, and Erik Cambria. 2019. Dialoguernn: An attentive mn for emotion detection in conversations. In Proceedings of the AAAI conference on artificial intelligence, vol- ume 33, pages 68 1818-66 18-6225.
Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, and Alexander Gelbukh, 2019. Di- alogueGCN: A graph convolutional neural network for emotion recognition in conversation. In Proceed- ings of the 2019 Conference on Empirical Methods

---

Jinjie Ni. 2023. Attention mechanism optimization for sub-symbolic-bant based and souurl-symbolic-beee natu- ral language processing.
Shohei Shimizu and Kenneth Bollen. 2014. Bayesian estimation of causal direction in acyclic structural equation models with individual-specinn confounder variables and non- non-gaussian distributions. Mach. Learn. Res., (5()):662-22622
Ana Rita Nogueira, Andrea Pugnana, Salvatore Rug- gieri, Dino Pedreschi, and João Gama. 2022. Meth- ods and tools for causal discovery and causal infer- ence. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(2)e1149.
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan. 2006. A linear non- gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7(10).
Juan Miguel Ogarrio, Peter Spirtes, and Joe Ramsey. 2016. A hybrid causal search algorithm for latent variable models. In Conference on probabilistic graphical models, pages 368-379, PMLR.
Keisuke Shirai, Hirotaka Kameko, and Shinsuke Mori. 2023. Towards flow graph prediction of open-domain procedural texts. arXiv preprint arXiv:2305. 19499777
Desmond C Ong, Jamil Zaki, and Noah D Goodman. 2015. Affective cognition: Exploring lay theories of emotion. Cognition, 143:141-162.
Francese Sidera, Georgina Perpiña, Jèssica Serrano, and Carles Rostan. 2018. Why is theory of mind impor- tant for referential communication? Current Psychol- ogy, 37:82-97.
Desmond C Ong. Jamil Zaki, and Noah D Goodman. 2019. Computational models of emotion inference in theory of mind: A review and roadmap. Topics in cognitive science, 11(2):38--377.
Pater Spirtes, Clark Glymour, Richard Scheines, Stu- art Kauffman, Valerio Aimale, and Frank Wimberly. 2000. Constructing bayesian network models gene expression networks from microarray data.
Patrícia Pereira, Helena Moniz, and Joao Paulo Car- valho. 2023. Deep emotion recognition in tex- tual conversations: A survey. arXiv preprint arXiv:2211.09172.
Chandler Squires, Annie Yun, Eshaan Nichani, Raj Agrawal, and Caroline Uhler. 2022. Causal struc- ture discovery between clusters of nodes induced by latent factors. In Conference on Causal Learning and Reasoning, pages 669-687. PMLR.
Soujanya Poria, Devamanyu Hazarika, Navonil Ma- jumder, Gautam Naik, Erik Cambria, and Rada Mi- halcea. 2019. MELD: A multimodal multi-party dataset for emotion recognition in conversations. In Proceedings of the 57th Annual Meeting of the As- sociation for Computational Linguistics, pages 527- 536, Florence, Italy. Association for Computational Linguistics.
Palash Thakur, Ronit Shahu, and Vikas Gupta. 2023. Audio and text- emotion recognition system us- ing deep learning. In Advances in Signal Processing, Embedded Systems and loT: Proceedings of Seventh ICMEET-2022, pages 447 4599. Springer.
Veronika Thost and Jie Chen. 2021. Directed acyelic graph neural networks. arXiv:2101.07655.
Soujanya Poria. Navonil Majumder. Devamanyu Haz- arika, Deepanway Ghosal, Rishabh Bhardwaj. Sam- son Yu Bai Jian, Pengfei Hong, Romila Ghosh, Ab- hinaba Roy, Niyati Chhaya, et al. 2021. Recognizing emotion cause in conversations. Cognitive Computa- tion, 1377-1337
arXiv preprint
Hande Aka Uymaz and Senem Kumova Metin. 2022. Vector based sentiment and emotion analysis from text: A survey. Engineering Applications of Artificial Intelligence, 113:104922.
Ruben Sanchez-Romero, Joseph D Ramsey, Kun Zhang, Madelyn RK Glymour, Biwei Huang, and Clark Gly- mour. 2019. Estimating feedforward and feedback effective connections from fmri time series: Assess- ments of statistical methods. Network Neuroxcience, 3(2):274-306.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pictro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint 17Xiv:1710110003.
Penghui Wei, Jiahao Zhao, and Wenji Mao. 2020. Ef- fective inter-clause modeling for end-to-end emotion- cause pair extraction. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, pages 3171-3181. Online. Association for Computational Linguistics.
Weizhou Shen, Junqing Chen, Xiaojun Quan, and Zhix- iang Xie. 2022. Dialogxl: All-in-one xlnet for multi- party conversation emotion recognition. In AAAI.
Weizhou Shen, Siyue Wu. Yunyi Yang, and Xiaojun Quan. 2021. Directed acyclic graph network for conversational emotion recognition. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 1 1th International Joint Conference Natural Language Processing (Volume I: Long Papers), pages 1551-1560, Online. Association for Computational Linguistics.
Rui Xia and Zixiang Ding. 2019, Emotion-cause cause pair extraction: A new task to emotion analysis in texts. In Proceedings of the 57th Annual Meeting of the As- sociation for Computational Linguistics, pages 1003- 1012, Florence, Italy. Association for Computational Linguistics.

---

Based the independence conditions of 2- variables SCM, could extend it to the general SCM including more than variables. Given any two variables SCM, we could testify to the Mao Ye. Haitao Wang, and Zheqian Chen. 2023. Msmix: An interpolation-est based text data augmen- tation method manifold swap mixup. arXiv preprint arXiv:2305. 19617.
Furthermore, there may exist set of independence: Σx 1t Y. Σy y X. We would like assume that there a latent variable L, for this situation, constructing two relationships - X and - Y. Then we obtain: Σ,, V. X, Σn M. Y. By utilizing the transitivity of conditional independence, we can establish X j Y, and finally acheive the situation Σx +t Y. Σy 1f X. We likewise assume latent variable L establishing X L and Y T. for the opposite situation where Σx IL Y, Ey TL X, and X, are two isolated variables in SCM. From the above independence conditions, we could obtain: En IL X, Σt TL Y. Due to the graph structure of SCM, we could obtain: Σx TL Y, Σy IL X + X IL Y. Considering the residual terms, we finally obtain: X J.. Σx and X Il Y = Σx IL Y and Y 1K Ey and Y TT X = By X. Hence, we could obtain additional two indepen- dence conditions:
Sayyed Zahiri and Jinho D. Choi. 2018. Emotion Detec- tion TV Show Transcripts with Sequence-based Convolutional Neural Networks. In Proceedings of the AAAI Workshop on Affective Content Analysis, AFFCON' 18, pages 44-51, New Orleans, LA.
Dong Zhang, Liangqing Wu, Changlong Sun, Shoushan Li. Qiaoming Zhu, and Guodong Zhou. 2019. Mod- eling both context-and speaker-sensitive dependence for emotion detection in multi-speaker conversations. In IJCAI, pages 5415-5421.
A Proof of Definition 2
Let X and Y be two variables in SCM, with their respective noise terms denoted as Ex and Ey (where Ex and Ey are mutually independent). Let & and y represent the fitted values of X and w.r.t. each other: X = AY and Ý = t. The residual terms between the fitted values and the true values are denoted as Ex = X - X and Ey Y Y. The true strength of Y - X is k. Hence, if the SCM only contains two variables writing:
IL Y, 1 X X L, +
Y = Ey
(10)
x= KY + Ex
(11) independence condition and finally orientate via the whole SCM.
The residual terms could write:
B Hypotheses and Algorithms for Skeletons
Σx =x=X 1 (x Ex))
(12)
Hypothesis 0. YU, E D, it has the same causal skeleton other ulterances.
= =
(ky + Ex)
(13)
By regarding Hypothesis O as the prior knowl- edge, common causal skeleton containing a tar- get variable and a fixed number of related vari- ables can reason about the relations between the target utterance and other considered utterances. We denote this skeleton of U. by S(U,). There are YU, U, E S(U)) = S(U)).
Then, if the true causal relationship is from Y to X, X k. Σx does not contain the term of Ey while Ny contains the term of Ex. We could obtain the independence of residual terms writting:
Σx AEx 11 Y
(14)
Additionally, there are some other empirical by- potheses from the above approaches. These hy- potheses can be divided into two categories: one is about the "order" of utterances (Hypotheses 1, 2, 3), and the other is about intermingling dynamics among the interlocutors (Hypotheses 4, 5, 6). Hypothesis 1. (Majumder et al., 2019) Under the sequential order, the target utterance receives information only from the previous utterance.
Σy = Rx
(15)
and vice versa. Therefore, could obtain the independence condition:
Ex Y,E, y X
. Exx 11 X

---

Category
Hypothesis
Original work
Majumder et al. (2019)
Veličković et al. (2017)
Chen et (2023)
Ghosal et al. (2019)
3(k 1), 4,
Lian (2021)
VI
3, 4, 6
Shen et al. (2021)
Algorithm 2: Buliding II cogn skeleton
Table 6: Statistics of cogn skeletons. We detailed the hypotheses each cogn skeleton adopted and the original works from which we designed them.
Input: D, D,p(.). k
Output: H = EV)E) V U1, U2 Uz..... Un
+ 0
Hypothesis (Wei et al., 2020) Under the gruph order, the target utterance receives information from all other utterances.
forall
i 2, N do N
forall j E 2,3, 2,3..... if j! j then ε E else Continue
do
5
Hypothesis 3. (Ghosal et al., 2019) Under the local graph order, target ufterance receives local information from k surround utterances.
6
7
Hypothesis 4. (Zhang al., 2019) The influ- ence between two utterances can be discriminated by whether the two utterances belong to the same speaker identity.
return H. (V,E)
Hypothesis 5. (Lian et al., 2021) Target utter- ance only receives information from the predeces- sor utterances.
Hypothesis 6. (Shen et al., 2021) Between two utterances both related the target utterance, there is also information passing, often dubbed partial order.
A cogn skeleton is denoted by = (V, M). The V U1, U2, U3,.. UN represents a set of utterances in a conversation, and the edge (i,j, ma,)) E denotes the influence from U, to Uj. where mij E .M is the type of the edge de- pending whether U; and U, belong one and the same speaker. Thus M 0, 1, where for that they are the same speaker and for different. Then we denote the speaker type of U, by a function p(U.). At last, we show the process of building cogn skeletons in Algorithms
Algorithm 3: Buliding III cogn skeleton
Input: D, D.p(.), k
Output: H = (V)
V < U1, U2, U3, Un
2 E 0
M + 0,1,1
forall i 2,3..... N do
forall j 2,3. .... do if pUU,) = (U.)) and then
7
E i,,
Algorithm 1: Buliding I cogn skeleton
8
else if p(U))!= p(U.) and i! j
Input: D. D.p(.),
then
Output: H H- (V,E)
E - EU
- U1, U2 U?,,UU
else
2 3 (
0
11
Continue
forall i 2,3. N do [ E U-U (i,
12 return H = (V,E,M)
5 return H. = (V,E)
Finally, Figure 5, we show the adjacency ma- trix of each cogn skeleton by inputting a binary al- ternating conversation case with utterances. But

---

III
IV
V
VI
1
0
1
0
0
1
0
0
0
0
0
0
0
1
0
1
1
1
1
1
0
1
1
1
0
1
0
1
0
1
Figure 5: Adjacency matrices towards 6 cogn skeletons when = 2. (i,5) of None represents that U/ is influenced by Uy.
Algorithm 6: Buliding VI cogn skeleton
Input: D, p.p(),. k
Algorithm 4: Buliding IV cogn skeleton
Output: H E)
Input: D. p.p()).
V U1, U2 U3.... UN
Output: = (V,E, V U1, U2 Us....,U UN
2 E 0
3 M + 0,1
& < 0
4 forall i 2, N do
M + 0,1
5
c+ 0
forall E 2, .., do
7
forall j 2, ..., do p(U)) p(U,) and 0 < i j < then
while 7 0 and < k do
if pUU-) PU,)) then
L
9
8 EU (7,1, et c+1
else if - and
else
0 < i
< k then
& < (7,10,
L 8 (ú,
7 7
10
else Continue
11
L
return H (V,E,M)
return H = (V,E,M)
note that adjacency can not indicate all the dif- ferences among these skeletons, for example, Hy- pothesis 6 takes effect when the model learns the relationship based on the VI skeleton.
Algorithm 5: Buliding V cogn skeleton
C Datasets
Input: D, D.p(-), h
Output: H = EV,E,
DailyDialog (Li et al., 2017): A Hritenwritten dialogs dataset with 7 emotion labels (neutral, hap- piness, surprise, sadness, anger, disgust, and fear). We follow Shen et al. (2021) to regard utterance turns speaker turns.
V U1, U2, Us,-..;U UN
<<-0 0
MM+
forall i 2.3..... do
5
7ti-1
MELD (Poria al., 2019): multimodel ERC dataset with 7 emotion labels as the same as Daily- Dialog.
if p( = p(U)) then
E E (7,
else
EmPrrNLP (Zahiri and Choi, 2018): A TV show scripts dataset with 7 emotion labels (neu- tral, sad, mad, scared, powerful. peaceful, joyful). IEMOCAP (Busso et al., 2008): A multimodel ERC dataset with 9 emotion labels (neutral, happy, sad, angry, frustrated, excited, surprised, disap- pointed, and fear). However, models ERC field
E EU (7, 7
10
return H (V,E,M)

---

are often evaluated on samples with first six emo- tions due the too few samples of latter three
Algorithm 7: Creating Non-noise Syn-
thetic dataset Input: D,S,
emotions. 20 dialogues for validation set follow- ing Shen et al. (2021).
Output: SCMD
RECCON (Poria et al., 2021): The first dataset
forall i E 2......, ....... do
for emotion cause recognition of conversation in- cluding RECCON-DD and RECCON-IE (a sub- set emulating an outto--distribution generalization test). RECCON-DD includes 5380 labeled ECPs and cause spans (no-context, inter-personal, self- contagion, hybrid, and latent).
if E.motion()) (U4) then
L E. ~ || ((,,1)
3
else
E ~ 1|
5
U E,
Synthetic dataset:
We create a synthetic dataset
forall i E 1.2,3..... do
by following the benchmark of the causal discovery field (Agrawal et al., 2021; Squires et al., 2022). To minimize sample bias, we did not randomly draw causal graphs as samples. Inversely, the number samples in the synthetic dataset and the number utterances and labels per sample are restricted to be consistent with RECCON. We use Causal Additive Models (CAMs), Specifically SCM struc- ture for our datasets. As shown in Algorithm 7, first, we assume that each i.i.d. implicit causes E ~ (1,, if is emotion utterance in the original dataset, and E (-,,, if is not. Then, we update each utterance via speaker turns S: if there is an emotion-cause pair (U, U2) E I,, then Uj UyزUū + E, (aja ~ Unifrom( (orroom(0007 [0.7, 1))) and for those pairs without emotion-cause label, aj Uni from(00 0.3]). Finally, we randomly select noise & Uni fnifrom[[- 0.25, 0.25)) for each utterance U; U; +
8
forall j E 1,, do
if (U,, Ug) E then
U 4= Ex Unifrom((0.7..)
else
U- a + Ey(ays 0.3])
SCMD U1, U1, ..... Us return SCMD
to 300, and dropout rate is 0.3.
Meanwhile, because there is only one training dataset for ECPE and ECSR, we evaluated our method times with different data splits by fol- lowing Chen et (2023) and then performed paired sample t-lest on the experimental results.
Finally, we adopted downstream task modules consistent with the SOTA baselines: Wei et al. (2020) in ECPE and ECSR, and Shen et al. (2021) the ERC task.
Implementation Details
In the word embedding, we adopt the affect-based pre-trained features proposed by Shen et al. (2021) for all baselines and models.
For evaluation metrics, we follow Shen et al. (2021) towards ERC, Xia and Ding (2019) to- wards ECPE, and Poria et al. (2021) towards ECSR. Specifically, we adopt the macro Fl score in ECPE and ECSR tasks, micro Fl score for DailyDialog and macro Fl score for the other three datasets in ERC task.
Although there are different pre-trained mod- cls in these skeleton baselines, the SOTA work DAG-ERC and EGAT have investigated their per- formances in consistent pre-trained model. There- fore, for fair and direct comparison, we continue this benchmark using the pre-trained embedding published by DAG-ERC for three tasks.
E Other Experiments in Affective Reasoning
In the hyper-parametery we follow the setting of Shen et al. (2021) in the ERC task. Moreover, in the ECPE and ECSR, the learning rate is set to 3e-5, batch size is set 32, and epoch is set 60. Further in our approach, we set 1, and implicit cause size set to 192, hidden size GNN is set
In Table 7, our approach performs better than the corresponding baseline under all skeletons in four datasets. Hence, using causal auto-encoder to find the implicit causes benefits this task. Besides, our approach improves significantly under skele- tons II, III, and IV. From Figure 2, these three skeletons have more relevant nodes than others, so there are more redundant edges to be corrected
'https: //drive, /etet:::e//diieeegoolleeee

---

Skt
Model
DaiyDDaaog
MELD
EmeryNEP
TEMOCAP
SkI
DailyDialog
MELD
EmoryNLP
JEMOCAP
DialggEL.
$4.93
11
C4.97 (5.1 (1803)
(2677
Ours
89.51
63,62
39.16
66.47
11I
54,37 (1533)
58.19
(,4.26 (6.55
EOATT
59.23
58.77
06.76
1V
55.62 (14.03)
$7.22 (1647)
36,91 (22.11
62.34 (L5.31)
Ours
39.68
63.71
39.62
68.18
54.62 (14.00)
5X. 19 (15722
35 (U462)
65.131,4448))
ROAI
54.31
60.91
34.42
65.22
53.27 (,0.200
58.39 (3444)
34.98
34.98
(J4.66)
68.18 (L3.999)
67.68
DECNI
59.08
63.78
39.44
67.41
67.61
Table 8: Overall performance of implicit causes E in ERC task.
DAG-ERC
59.33
63.65
39:02
68,03
VI
Ours
59,53
63.81
39.54
69.17
Table 7: Overall performance in ERC task. denotes the results implemented in this paper. The better scores in the same skeleton are bold, and the best of all skeletons is in red.
following:
7-utterances cases: 110, 170, 224, 372, 500.
8-utterances cases: 62, 74, 104, 177, 584.
To obtain the non-negative value, we adopted the sigmoid(() 0.05 process the original tensors (1 A²) outputted from the encoder. We follow common practice: set the threshold as 0.05 to delete some unimportant edges. And to highlight which implicit cause contributes the each utterance best, we adopted the sof mof())))) to process columns afterward and labeled the block with value 0.
74
73
F1 score
71
70
2
4
#layers
Figure 6: Further layers L and related node number K with VI skeleton model in ECPE task.
It is excepted that: (i) when skeletons construct overage edges, our model is able to degrade the influences of some negligible utterances by delet- ing the corresponding edges from their implicit causes. (ii) when skeletons construct insufficient edges, our model can add some edges to obtain more information.
by our approach, which is demonstrated again in Appendix F. In contrast, V and VT achieve the best results in MELD, EmoryNLP, and IEMOCAP datasets, which indicates that Hypothesis 5 is more probably strong inductive bias that conversation enjoys.
G Proof of emotion consistency of implicit causes and utterances
Then, we investigate how the number of layers and the variants of causal skeletons would affect the performance of our approach. So we further conducted several contrasts with up to 5 and up to 6, as shown in Figure 6. One observation is that the best performance occurs at either & = 1, 2, or 3, which indicates that & > 1 offers no advantage and even leads to confounding. Moreover, = 1 achieves the best performance under all & values. other words, one layer is sufficient yield the most effective implicit causes.
We would like to explain why implicit causes and utterances are consistent in emotion from both theory and euqation, in the condition where emo- tional utterance and cause utterance possess differ- ent emotion types.
We define the implicit causes the unobserv- able emotional desire and the utterances as the ob- servable emotional expression. This definition is proposed in Ong al. (2019, 2015), which also argues that emotional expression is affected by de- sires and event outcomes. Moreover, for emotion utterances that are not influenced by explicit cause factors, the source of their emotions should origi- nate from implicit causes. The desire and the ex- pression generally belong to the same emotion be- cause the outcomes often have little effect on emo- tional expression. Our paper can also deduce this conclusion from the SCM (Equation 1). Consider- ing there a lincar map f(-) from representation space to emotion space. Then we can obtain the
F Visualization of Causal Graph
the Figure to 11, we showed the Visualization of the adjacency matrix (I - - When the auxiliary loss LOSSkL achieves the lower bound, (I (T) represents the relationship matrix be- tween utterances and implicit causes.
In the ECPE task, we extracted 10 samples from test sets in different folds. To facilitate compari- son and contrasting, we selected five 7-utterances cases and five 8- 8-utterances cases. The IDs are as

---

following:
S((T AJU) = S(B)
(16)
(1 - F(E)
(17)
( ((E)
(18)
Note that W = AI and A,, So in W, the value of the elements on the diagonal is constant at 1 and is a constant maximum of each column. Naturally, fEE is an approximate estimate of fU) especially U, and Uj in the ECP (Uz, U,) do not belong to the same emotion category, which is why we think implicit causes reasonable when the Fl score of Table 6 high.
Therefore, we test the Fl scores ERC task by replacing H with E from a consensus that implicit causes should be aligned with utterances in the emotion types.
In Table 8, we reported the overall results of E in ERC task. Note that only examine the sam- ple of ECP with different emotion types. Among five skeletons and four datasets, almost all results achieve 90% scores of corresponding performances of H. which indicates that E is practically aligned with H in the affective dimension.

---

Sample Id:1 10
Sample Id:1 170
Sample Id:224
Sample Id:372
Sample 10:500
10
DialogX L
Ul: utterance
LC
02
CAE (II)
Ul: utterance
Ei: implicit cause
th
Semple Id:62
Sample Id:74
Sample Id:1 04
Sample [d:177
Sample Id:584
10
D
Ui: utterance
U6
US
CAE (II) Ui: utterance El: implicit cause
Figure 7: Causal Graph cases of DialogXL and Ours (CAE II).
Sample Id:1
Sample Id:1 77
Sample Id:224
Sample Id:372
Sample Id:500
ut
U2
EGAT Ul: utberance
CAE (III)
10
Ui: utberance
El implicit cause
62
Sample Id:62
Sample Id:74
Sample 1d:104
Sample 1d:177
Sample Id:584
EGAT
Ui: utterance
0t.
US
103
10.
in
CAE (III)
us
US
Un
Ui: utberance
El: implicit cause
Us
Figure 8: Causal Graph cases of EGAT and Ours (CAE III).

---

Sample Id:110
Sample Id:170
Sample Id:224
Sample Id:372
Sample Id:500
RGAT
Ui: utterance
US
10
U6
17
UL
12
13
US
01.
155
114
13
22
122
144
US
US
UL
U3
UA
US
12
CAE (IV)
Ui: utterance
Ei: implicit cause
Sample Id:62
Sample Id:74
Sample Id:104
Sample Id:177
Sample Id:584
U1
RGAT
140
Ui: utterance
V2
CAE (IV)
Ui: utterance El: implicit cause
1M
(M
Figure 9: Causal Graph cases of RGAT and Ours (CAE IV).
Sample Id:110
Sample Id: 170
Sample Id:224
Sample Id:372
Sample Id:500
DECN
Ui: utterance
UG
UG
1M
IN
In
12
CAE (V) Ui: utterance Fi: implicit cause
U5
US
U4
UA
un
A:l
62 Sample 1d.74
64
82 Sample Id:104
s4 Sample Id:584
Sample Id:62
Sample ld:177
12
02
DECN Ui: utterance
U7
12
12
DE
DE
12
03
12X
CAF (V) Ui: utterance
Up
U4
Ei: implicit cause
UC
U9
Figure 10: Causal Graph cases DECN and Ours (CAE V).

---

Sample Id:110
Sample [d:170
Sample Id:224
Sample Id:372
Sample Id:500
LG
DAG-ERC utterance
CAE
L23
Ui: utterance
Fi: implicit cause
UT
12
Sample Id:62
Sample Id:74
Sample Id:104
Sample Id:177
Sample 1d:584
DAG-ERC
Ul: utterance
US
US
U7
UT
18
US
CAE (VI)
173
023
Ul: utterence
Ei: implicit cause
Figure II: Causal Graph cases of DAG-ERC Ours (CAE VT).


Reviews:
[{"review_id": "gUgIvYrh7E", "reviewer": "Reviewer_5DgJ", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate Reviewer 5Dgj's attention to the details in our paper. We fully agree with your perspective and apologize for our oversight regarding abbreviations. Particularly for ECPE, ECSR, and ECP, it can be challenging for many readers to distinguish between them during their initial reading. \n\nTo address your concerns, we will make the following changes in the final version: whenever an abbreviation appears for the first time in each section or page, we will provide its full form. In this way, we hope readers can easily find the full forms whenever they feel uncertain about the abbreviations. Alternatively, we may use more distinctive abbreviations that better represent each term, such as 'ECP' = 'EC pairs', 'ECPE' = 'EC pairs extraction', and 'ECSR' = 'EC span recognition'. \n\nLastly, thank you for investing your time and effort in reviewing our paper.", "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 study reveals that when it comes to more specific causal relationships within semantically similar sentences (such as reasoning tasks), both unsupervised and supervised methods may not exhibit the same level of “intelligence” and output some “pseudo-correlation”.\n2. The discussion of the methods proposed in this work is thorough and detailed.\n3. The authors propose new methods to address the problem of causal discrimination in affective reasoning. Their approaches significantly outperform other state-of-the-art methods, including GPT-3.5 and GPT-4.", "reasons_to_reject": "There are too many abbreviations throughout the paper (e.g., ECP, ERC, ECPE, ECSR), which can be a bit confusing sometimes. Otherwise, I see no reasons to reject this paper.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This study concentrates on the challenge of causal discrimination, specifically on the task of affective reasoning in *conversations*. The paper reveals that almost all existing models excel at capturing semantic relatedness within utterance embeddings but fall short in determining the specific causal relationship underlying the association between two utterances on the task of causal discrimination. To overcome this limitation, the authors constructed a SCM with i.i.d. noise terms, and proposed the *cogn* framework to address the unstructured nature of conversation data. Experimental results show that the proposed approaches significantly outperform existing methods on two affective reasoning tasks including Emotion-Cause Pair Extraction (ECPE) and Emotion-Cause Span Recognition (ECSR) and one emotion recognition task (ERC), demonstrating its effectiveness in affective reasoning.\n\nThe main contributions are as follows: 1) The authors incorporated i.i.d. noise terms, and formulated the dialogue process as a structural causal model (SCM); 2) The authors devised the *cogn* skeleton to address the problems of variable-length and unstructured dialogue samples; 3) The authors adopted an autoencoder architecture to overcome the unobservability of implicit causes and make it learnable; and 4) The authors constructed a synthetic dataset with implicit causes and conducted extensive evaluations of the proposed method.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "There are too many abbreviations throughout the paper (e.g., ECP, ERC, ECPE, ECSR), which can be a bit confusing sometimes."}, "extra_text": {}}, {"review_id": "bflEv1doWI", "reviewer": "Reviewer_2DCP", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review. However, after going through your comments, we couldn't help but wonder if you may have missed or skipped many sections of the paper (especially section 3.2 and Appendix B). We understand this, considering that our paper, with its appendix, exceeds the average length for a conference paper, so it's possible that you had to overlook some parts that you deemed less critical or content that falls into the appendix, given the review deadline. Regardless, we will respond to each of your points and indicate the corresponding sections where we have provided sufficient clarification:\n\nPoint 1: More convincing benchmarks with simple patterns.\n\nWe have explicitly introduced these 'simple patterns' in our paper. Specifically, in Section 3.2 and Appendix B, we formulate Hypotheses 1-6 and Skeletons 1-6 to describe these simple patterns (called prior knowledge in our papers). Moreover, in the experiments, we indeed treat the SOTA works on these simple patterns as benchmarks. Further, there are some inaccuracies and incomplete observations in the simple patterns you mentioned. For instance, in $U_{1}$: ''Let's name a red object'', $U_{2}$: ''Red Apple'', $U_{3}$: ''Red Sun'', a 100% causal connection between $U_{2}$ and $U_{3}$, as you mentioned, is not present. The relationship between ''Red Apple'' and ''Red Sun\" is of similarity rather than causality, the common reason being $U_{1}$. That is, the causal relationship is $U_{3} \\leftarrow U_{1} \\rightarrow U_{2}$. For other simple patterns you may not have considered, we invite you to refer in detail to Appendix B. \n\nPoint 2: You believe that the strong capabilities of LLM undermine our contribution.\n\nOur Tables 1, 3, and 5 emphasize that although LLMs have strong abilities, they lack causal inference capabilities. Simultaneously, many recent studies have shown that LLMs lack causal discriminability [1][2]. The weak causal inference capacity results in those without extensive complex prompt instructions, performing worse than supervised specialized models. Therefore, for these tasks, we believe that the contribution deriving from improvements to supervised specialized models far outweighs mere research on LLM prompts and fine-tuning. Moreover, the need to construct unique skeletons for each specific domain is the reason why our paper only takes the dialog emotion inference task as an example. However, our contribution lies in: for most text inference tasks, it can introduce i.i.d. noise, follow our skeleton construction process, and then apply the model we propose to gain causal insights.  \n\nPoint 3: You are unclear as to why implicit causes can correspond to independent noise. \n\nWe have explained this issue in the papers, and you could check lines 245-255 in Section 3.1 and lines 324-333 in Section 3.3. To explain simply: The insights in this part involve cognitive science[3] and conversation theory[4]. For each utterance, while it superficially appears to be directly influenced by the preceding utterances, the actual causing factor is mental states within the speaker, such as desires, goals, and beliefs. These mental states are also referred to as common ground in the field of cognitive science. Therefore, the preceding utterances, which we refer to as explicit causes, are like the parents  set of X in the SCM; they are endogenous and observable. Meanwhile, the mental states, which we term as implicit causes, are like the noise terms in the SCM; they are exogenous and unobservable. Hence, they can naturally correspond to each other. \n\nPoint 4: Unclear writing.\n\nThank you for your feedback. Although the other two reviewers found our manuscript to be clear and detailed, we value your perspective and will replace some of the longer sentences with more short ones in the final draft. Indeed, our paper covers a range of topics, which may make reading difficult for readers with less background in these areas.\n\nPoint 5: Formatting errors.\n\nWe appreciate your observations. Multiple updates to the paper have unfortunately led to some inconsistencies and errors. We will thoroughly correct these in upcoming versions. However, DD and IE are mentioned in Appendix C (line 967). \n\n\n[1]. Jin, Zhijing, et al. \"Can Large Language Models Infer Causation from Correlation?.\" arXiv preprint arXiv:2306.05836 (2023).\n[2]. Long, Stephanie, et al. \"Can large language models build causal graphs?.\" arXiv preprint arXiv:2303.05279 (2023).\n[3]. McKinley, Geoffrey L., Sarah Brown-Schmidt, and Aaron S. Benjamin. \"Memory for conversation and the development of common ground.\" Memory & cognition 45 (2017): 1281-1294.\n[4]. Chen, Hang, et al. \"Learning a Structural Causal Model for Intuition Reasoning in Conversation.\" arXiv preprint arXiv:2305.17727 (2023).", "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 addresses causal reasoning, an important and open research challenge in today's AI literature and proposes an approach for structural causal reasoning, demonstrating stronger performance over baselines", "reasons_to_reject": "- I wish the paper provided a better description of the task itself. Based just on Figure 1 example, I couldn't help but wonder if the correct answer has a trivial pattern --- that there's a causal relationship between the most recent utterance of between two speakers (100% cases?), and also that there's a causal relationship between two most recent utterances of each speaker (the vast majority of the cases?), which makes me wonder whether simple majority baselines of some sorts could've worked surprisingly well. Detecting when the causality misses between two adjacent utterances (which can happen, though less frequently) seems relatively easy due to the abrupt change of the topic. Fundamentally, I came away with the suspicion --- whether the classification formulation experimented in this paper justifies the relatively complex method proposed in this work, and I wish there was a more convincing benchmark explored in this paper. \n\n- Concurrently, I'm not convinced if the relatively simplistic classification of causal relationship explored in this paper helps advancing the field in terms of affective understanding in conversation, especially in the context of the currently powerful LLMs that can carry about an amazing level of conversation. While causal reasoning is generally an interesting and open research question, I wonder if the particular task formulation explored in this paper is rather too narrow to be relevant in the current AI SOTA capabilities.\n\n- I wish the authors provided more intuition about why \"implicit causes\" correspond to \"i.i.d. noise\". While I understand to some degree that implicit causes may seem like \"noise\", various statements around this don't seem well justified or even precisely stated. \n\n- The lack of clarity of writing is among the weakest points of this paper. At least for me, of the couple dozen papers across NLP and ML venues that I have reviewed in recent years, this one by far was the most opaque to unpack. As one of the many such examples, I found the following sentence hard to parse and hard to fully understand [line 073 - line 079]: \"In order to discriminate different causal relationships between two similar embeddings, we construct the dialogue process as a Structural Causal Model (SCM) stemming from many endeavors supporting that i.i.d. noise of SCM could facilitate the discrimination of causal relationships when finning two variables.\" In fact, I found almost half of the remaining sentences of the introduction section somewhat troublesome, sometimes because the sentences make claims that are not yet defined or justified.\n\n- In the same vein, the technical writing seems a bit sloppy at times. For example in Table 3, DD and IE were never defined, and the caption talks about ECPE-MLL, while it doesn't even appear in the rows of the methods listed in that Table. Moreover, the boldfaced numbers seem inconsistent. For example, GPT-4's IE score is higher than that of ECPE-2D, but not boldfaced.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes an approach to \"affective reasoning in conversation\" formulated as a \"causal discrimination\" task, which essentially aims to classify whether a pair of conversation turns has a causal relationship (and if so, in what directionality).\n\nThe underlying premise of this work (which is assumed but not formally or empirically demonstrated) is that this particular task formulation of emotional causal relation classification in a pair of conversation turns is important.\n\nThe key thesis of this paper is that handling of i.i.d. noise is important in reasoning about such structural causal relationships, and the paper thus proposes an autoencoder architecture that can incorporate such i.i.d. noise, which presumably corresponds to the \"implicit causes\".", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Y1KYBcpFCw", "reviewer": "Reviewer_28nr", "summary": "", "questions": "", "limitations": "", "rating": 5, "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 for the time and effort you have invested in reviewing our paper. From your comments, it is evident that you have carefully scrutinized our work, and many of your concerns align closely with our latest research. We would like to address each of your points and clarifications in turn:\n\nPoint 1: The limitations of confounding bias and backdoor paths.\n\nWe have been attempting to integrate previous methods of causal research into dialogue and other deep-learning inference tasks. To overcome these limitations, in our new work, we have proposed interventions suitable for deep representations. Essentially, traditional interventions $do(X):= P(X = x_{1}) = 1$, but our proposed interventions for deep representations are defined as $do(X):= Pa(X) = \\emptyset $, where $Pa(X) $ denotes the parent set of $X$. We hope you will soon see the details of this insight in our soonly papers.\n\nPoint 2: Address the challenge more fundamentally. \n\nThe most significant obstacle to further research is the lack of a high-quality dataset with complete causal relationship labels. Therefore, we have constructed a simulated dialogue dataset and plan to make it open soon. The new dataset, generated via the GPT-4 API, controls the preamble utterances that can be known. Simultaneously, to resolve this issue, we are considering many new aspects, such as determining the causal consistency of causal structures and representations. This guiding thought leads us to consider the causal abstraction in dialogue causal relationships, which aligns with the novel intervention insights mentioned in point 1. In subsequent manuscript versions, we will add these insights in the limitations section. \n\nPoint 3: Suggestions on writing.\n\nWe appreciate your diligent review. In future versions, we plan to provide more comprehensive explanations for the details in figures and tables.", "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. This paper is well-organized, clearly describing the task and problem.\n\n2. The proposed method is based on a high level of understanding of existing research and includes the definition of a valid hypothesis with technical descriptions.\n\n3. It clearly describes the limitations and provides appropriate analysis through experiments.", "reasons_to_reject": "1. The authors have partly addressed this in the limitations section, but considering confounding-bias and the backdoor problem is relatively weak compared to previous studies on causality.\n\n2. Isn't the proposed method also not a fundamental solution to the challenge of affective reasoning? If there are labels and structures that can represent causality in a given conversation situation, I think it is natural to see the effect of investing more layers and resources.", "questions_to_authors": "", "paper_topic_and_main_contributions": "1. The authors demonstrate the effectiveness of their approach in affective reasoning\n2. They curate a synthetic dataset with implicit causes to visualize the latent variable in our implementation.\n3. They exhibit a significant reduction in false predictions for negative samples across three causal discrimination scenarios.\n4. They formulate the dialogue process and analyze the cogn skeleton with encoder-decoder structure, GNN, and graph attention.", "excitement": 5, "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "Figure 3. The description of the colors and shading is missing and hard to see, which is confusing."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper presents a narrow and focused contribution to affective reasoning in conversations through the lens of causal discrimination, predicting the presence of causal relationship and direction in pairs of utterances. The paper then presents structural causal model with the  i.i.d. noise inclusion into conversation. To incorporate such i.i.d. noise, the paper presents an Autoencoder framework.  The study of causal relationship (in particular for LLMs or not-so-large LMs) is very important but much less explored compared to the rest of the field. As such, this work provides contributions, although very narrow in its task scope, which are interesting and insightful. The presentation of the paper could be polished further (following the comments of 5DgJ, 2DCP).  The abstract and presentation of results could be decompressed further.
Decision: Accept-Main