Generative Emotion Cause Triplet Extraction in Conversations with Commonsense Knowledge
Authors: Fanfan Wang
Jianfei Yu
Rui Xia
Venue: EMNLP 2023 Findings (2023)
Keywords: Emotion Cause Analysis
Commonsense Knowledge
Emotion Recognition in Conversations

Abstract: Emotion Cause Triplet Extraction in Conversations (ECTEC) aims to simultaneously extract emotion utterances, emotion categories, and cause utterances from conversations.
However, existing studies mainly decompose the ECTEC task into multiple subtasks and solve them in a pipeline manner.
Moreover, since conversations tend to contain many informal and implicit expressions, it often requires external knowledge and reasoning-based inference to accurately identify emotional and causal clues implicitly mentioned in the context, which are ignored by previous work. To address these limitations, in this paper, we propose a commonSense knowledge-enHanced generAtive fRameworK named SHARK, which formulates the ECTEC task as an index generation problem and generates the emotion-cause-category triplets in an end-to-end manner with a sequence-to-sequence model.
Furthermore, we propose to incorporate both retrieved and generated commonsense knowledge into the generative model via a dual-view gate mechanism and a graph attention layer.
Experimental results show that our SHARK model consistently outperforms several competitive systems on two benchmark datasets. Our source codes are publicly released at https://github.com/NUSTM/SHARK.

Generative Emotion Cause Triplet Extraction in Conversations with Commonsense Knowledge
Fanfan Wang, Jianfei Yu" and Rui Xia" School of Computer Science and Engincering, Nanjing University of Science and Technology. China (ffwang, jfyu, rxia)@njust. edu. cn
Abstract
Charlie
Ross
Emotion Cause Triplet Extraction in Conversa- tions (ECTEC) aims to simultaneously extract emotion utterances, emotion categories, and cause utterances from conversations. However. existing studies mainly decompose the ECTEC task into multiple subtasks and solve them in a pipeline manner. Moreover, since conversa- tions tend to contain many informal and im- plicit expressions, often requires external knowledge and geasoning-based inference to accurately identify emotional and causal clues implicitly mentioned in the context, which are ignored by previous work. To address these limitations, this paper, we propose commonSense Knowledge-enHannee tive fRameworK named SHARK, which for- mulates the ECTEC task an index genera- tion problem and generates the emotion-cause- category triplets an end-to-end manner with sequence-to-sequemee -o--euue-eeeee model. Furthermore, we propose to incorporate both retrieved and generated commonsense knowledge into generative model via dual-view gate mecha- nism and graph attention layer. Experimental results show that our SHARK model consis- tently outperforms several competitive systems on two benchmark datasets. Our source codes are publicly released at https://gi t NUSTM/SHARK.
As result, Charlie feels and
Uh, well... Joey and broke up.
result, others confused
Oh my God, wh... what happened?
U2
As result, Charlie feels and
Joey great guy, but just... different! mean, during your speech he kept laughing homo erectus!
U3
a result, others feel grooved.
generA-
knew was him!
ECTEC output (Emotion, Cause, Category)
(U1,U1,,adness), (U2, (ad,us, (U4,U3,anger)
Figure example extracting the emotion-cami -aaose- category triplets from the conversation with the help of commonsense knowledge the dashed box.
such as customer support, mental health care, and human-comee compnnc interaction. Therefore, Emotion Cause Analysis in Conversations (ECAC) has been gaining increasing attention from both academia and industry in recent years.
1 Introduction
Most existing studies on ECAC primarily focus on Causal Emotion Entailment (CEE) and Emotion- Cause Pair Extraction in Conversations (ECPEC). The former line of work on CEE assumes that the emotion utterances are given and formulates the ECAC task as utterance classification prob- lem (Poria et al., 2021; Li et al., 2022a; Zhang et al., 2022; Zhao et al., 2023; Gu et al., 2023), which aims to predict whether each utterance in conversation the cause of the given emotion terance. The latter line of work on ECPEC focuses on designing different multi-task learning architec- tures to jointly extract the emotion utterances and
Emotion understanding is key component of human-like artificial intelligence, as emotions are intrinsic to humans and significantly influence our cognition, decision-making, and social interactions. Conversations, a fundamental form of human communication, are replete with diverse emotions.
Beyond mere emotion recognition, delving into the triggers behind these emotions in conversations is more intricate and less explored task. A compre- hensive understanding of both the speaker's emo- tions and their causes facilitates many applications
Corresponding authors.

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their corresponding causes pipeline manner (Li al., 2022b; Jeong and Bak, 2023).
ploys BART to encode the input conversation, fol- lowed by decoding a set of triplets containing the indexes of emotion utterances, cause utterances, and emotion categories. Moreover, to incorporate CSK into the BART-based framework, SHARK feeds each utterance to pre-trained neural knowl- edge model COMET-ATOMICHI (Hwang et al., 2021) for commonsense knowledge generation and widely-used knowledge base ATOMIC (Sap al., 2019) for commonsense knowledge retrieval. Next, SHARK integrates both generated and retrieved knowledge via a dual-view gate mechanism to ob- tain the knowledge representation, and then intro- duces a knowledge-awar graph attention layer to capture the intra-speaker and inter-speaker interac- tions in the conversation. Finally, the knowledge- enhanced utterance representation is used to gener- ale the position index sequence.
Due to the importance of emotion categories in ECAC, Wang et al. (2022) recently explored a task named Emotion Cause Triplet Extraction in Con- versations (ECTEC), which aims to simultaneously extract emotion utterances, emotion categories, and cause utterances from conversations, For example. given the conversation in Figure 1, it expected to identify four cnation-cause-categry triplets. To tackle the task, they further proposed a two-step approach, which first extracts emotion utterances with emotion category and cause utterances sep- arately, followed by pairing them to obtain valid cmotion-cause-caleee category triplets.
However, all the aforementioned studies on ECAC still suffer from two limitations. First, exist- ing works on CEE and ECPEC primarily decom- pose ECTEC into several subtasks and only focus on tackling one or two subtasks. Although Wang et al. (2022) attempted to address the ECTEC task, their pipeline approach still suffers from the error propagation issue. To the best of our knowledge. there is still a lack of an end-to-end approach to generate all the emotion-causs-categon triplets in one shot. Second, since interlocutors usually rely on the dialogue history and commonsense knowl- edge (CSK) to make sense of others' utterances and respond succinctly rather than explicitly (Grice, 1975), it often requires external knowledge and reasoning- based inference accurately identify emotional and causal clues in the conversation. For example, in Figure/ 1, we can find that the CSK reasoned from U3 not only indicates Charlie's s sad- ness in U3 and Ross's anger in U4, but also implics that U3 contains the causes behind their emotions. With the clues inferred from CSK, the two triplets and "(Ug, can be eas- ily inferred. Despite the importance of CSK, it is still under-explored how to utilize CSK to facilitate the ECTEC task.
Our contributions are summarized as follows:
We formulate the ECTEC task an index gener- ation problem by linearizing all emotion-cause- category triplets into a position index sequence, and employ a BART-based encoder framework to generate the index sequence from the input conversation.
We further introduce a dual-view gate mecha- nism to integrate both retrieved and generated knowledge to obtain the knowledge represen- tation, and then incorporate it into the BART- based framework via knowledge-awee graph attention layer.
Experimental results demonstrate that our gen- erative framework consistently performs better than a number of competitive systems on two benchmark datascts. Further in-depth analy- sis shows the importance of the commonsense knowledge for the ECTEC task.
2
Methodology
2.1
Task Formulation and Model Overview
To address these limitations, in this paper, we propose a commonSense knowledge-enHanced generAtive /RameworK named SHARK, which incorporates CSK into pre-trained sequence-to- sequence model BART (Lewis et al., 2020) to gen- erate all the emotiin-ccuuse-categor triplets in an end-to-end manner. Specifically, SHARK formu- lates the ECTEC task as an index generation prob- lem, which linearizes each emotion-causee causttnnc triplet into a position index triplet. It then em-
Given a conversation containing utterances D = Vi....,U,,.. ...... .,. Un). in which each utterance U, corresponds speaker S,, the goal of ECTEC is to extract all the emotion cause triplets:
P (US, Ug UU,,UR.D)
(1)
where Ug is an emotion utterance with certain emo- tion yf, Ug is the corresponding cause utterance. The emotion category yf is one of the six basic

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Graph Attention Layer
<U1> Charlie Uh, woll...ooy and Tadlbrre broke
cuzs Ross Gid, hapoened? great gug. are jos... diffreentl mioo, during sprech lest lerahing homo erectes! knew tim!
<01>
X
Up
Wa
<11>
ATOMIC
BART
BART Decoder
Sonder& togs of of
sadness
15
Emotion Classfier
<Uk
Knoreledge Retrieve
<U3>
+
coRlectlo Charle sad.
05
94
COMET
coReactso Dehens annoyed. eed, coneveed
difierett upast.
06 90
No
anger
Knowlodge
Indlec Conmrnatoo
Indeectoben Comvention
Figure 2: The overview of our proposed commonSense Hnowledge-enHanced generAtive fRamework (SHARK).
emotions defined by Ekman et al. (1999), including Anger, Disgust, Fear, Joy, Sadness and Surprise.
each utterance to obtain the input sequence: X <<><U>><1 U1 <Un>S,: Un</>>, where 
 and 
 refer to the start and end tokens, <Vi> is special token to indicate the start of the i-th utterance, and and U3 denote the speaker's name and the token sequence of the i-th utterance. The input sequence X is then fed to the BART encoder to obtain the hidden representation:
As illusttrated Figure 2, our proposed common- Sense knowledge-enHanced generAtive fRame- worK (SHARK) formulates the ECTEC task as an index generation problem, which linearizes all the emotion amotion-cause-eee cause-category triplets into their corre- sponding position indexes to obtain position index sequence, and employs pre-trained sequence-to- sequence model BART as the backbone generate the position index sequence in end-to-end man- ner. To incorporate CSK into the generative model, SHARK utilizes an external knowledge base and a pre-trained model to both retrieve and generate emotion-oriented CSK for each utterance, as shown in the left of Figure 2. Next, SHARK further intro- duces a dual-view gate mechanism to fuse the CSK representation, and then leverages it to enhance the utterance representation via graph attention layer in the middle of Figure 2.
X Encoder(X),
(2)
where E Lxd L is the length of X. and is the hidden dimension. The representation of the spe- cial token <Ui> in X is regarded the utterance representation of Ug i.e., u4 Based this, we further incorporate CSK obtain the knowledge- enhanced representation X' via a dual-view gate mechanism (Section 2.3) and knowledge-aware graph attention layer (Section 2.4).
Decoder. To map all the emotion cnotion-cause-aategor triplets to position index sequence, we use six indexes (i.e., to 6) to denote six emotion cat- egories, and then use L indexes starting from 7 to denote each word in the input sequence X, in which the index of the special token <Ui> refers to the emotion or cause utterance. Formally, let us use = [cu1, CU1,, 6CI...., ellm, CUl,, cCm] to denote the output index sequence, where eu and cu represent the position indexes of the emotion and cause utterances, respectively, ec denotes the index of the emotion category, and m is the number of triplets. At the (-th time step, the decoder takes the knowledge-enhanced representation X' and the previous decoder outputs Yze as inputs to predict the output probability distribution. Since Y is an index sequence, we first need to convert the indexes
In the next subsections, we will first present the BART-based index generation framework, and then describe the details of the dual-view gate mech- anism and the knowledge-aware graph attention layer.
2.2 Index Generation Framework
Given conversation, we use the pre-trained BART model as the backbone to generate the output index sequence of the emotion-causse-aatggon triplets. Encoder. The encoder is a multi-layer bidi- rectional Transformer, which encodes the input sequence into the hidden representation. Specif- ically, we concatenate all the utterances in a con- versation, and add several special tokens before

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into tokens as follows:
generate novel commonsense knowledge tuples. Taking each utterance and the selected relation type as inputs, the model would automatically generate several tail phrases (we set the beam size to 3) as the CSK for the utterance. For example, given the utterance "Uh, well... Joey and I broke up. and the relation type xReact, the following phrases (sad, regretful, upset) could be generated.
gt = Cy.
y1 < C| Yt
(3)
Xy
|c|
> |
where C' is the token list of the emotion categories, IC 6.
Next, can obtain the last hidden state of the BART decoder and predict the probability distribu- tion as follows:
Knowledge Encoding. We first convert the (4) knowledge phrases for each utterance into a com- plete sentence, which incorporates speaker's ' name, e.g., "[Charlie] feels [sad, different, upser]." for (6) xReact and "Others [annoyed, sad, confused]." for oReact. Next, we concatenate the knowledge sentences of all utterances in the conversation and feed them to the BART encoder. The retrieved knowledge and generated knowledge under the two relation types are encoded separately in the same way as utterance encoding described in Section 2.2. Finally, for each utterance Us, we obtain four knowledge representations: vi ro gi g. Knowledge Fusion. Since the CSK under xRe- act and oReact types would independently influ- ence the representation of utterances from the same speaker and other speakers, we employ a dual- view gate mechanism to perform knowledge fusion from two different views, thereby obtaining view- specific knowledge representations. Specifically, for each relation type, we calculate the knowledge weight through linear layer, and then use the weight integrate retrieved knowledge and gener- ated knowledge. The formula is shown as follows:
ng = Decoder(X (X:: (Yr),
X MLP(X'), ),
(5)
H utt
(E + x2,)/2,
P Y².)= = Softmax((C*. Hunl nG),
(7)
where MLP refers to multi-layer perceptron, Eunt TokenEmbed (um)) and X respectively denote the embeddings of <Ui> and the hidden represen- tations of <Ui>, C TokenEmbed((C C) refers to the embeddings of six emotion categories; [ is the concatenation operation, and "." denotes the dot product; P(y X, Y<)) E RR(C) 1) is the final distribution on all indexes.
2.3 Dual-view Gate Mechanism
For each utterance, we separately obtain its re- trieved and generated knowledge, and then inte- grate both of them through gate mechanism. Knowledge Retrieval. We utilize ATOMIC- 2020' (Hwang et al., 2021), a widely-used com- monsense knowledge graph covering social, phys- ical, and eventive aspects of everyday inferen- tial knowledge, as the external knowledge source. ATOMIC-2020 contains a large number of com- monsense knowledge tuples composed of head phrase, a relation type, and tail phrase, e.g., ("Per- sonX affords a car". xReect, "proud"). In this paper. we focus on two social-interaction relations that are highly correlated with emotion and cause: xReact and oReact, which denote how the subject and the object feel after the input event occurs. Specifically, we use SBERT (Reimers and Gurevych, 2019) to calculate the similarity between each utterance in the dataset and all head phrases in ATOMIC-2020, and obtain the tail phrases under the relations of the top-3 similar head phrases. We then concate- nate these tail phrases as our retrieved knowledge for each utterance.
0f W [u ri T af o (Wlu; r 99: oR
RR
(8)
h R = a; xR p.p RR + (1 - a; 0g RR
(9)
he a; Ro + (1 ai 9
oR oR
of oR
where W E 3d is a trainable weight, denotes the sigmoid function.
2.4 Knowledge-wware Graph Attention Layer
To better model the conversation context and incor- porate CSK effectively, we employ graph atten- tion layer to capture the dynamics and dependen- cies among speakers in the conversation.
Knowledge Generation. A pre-trained neural knowledge model COMET-ATOOMIC"? is used to
As shown in Figure 2, we construct graph G (V,E) for each conversation, where V is the set of nodes and is the set of edges between two nodes.
'https://allenn wwtpo/////////tttoooeee http//alleaacccoeteeeeee cooooommtttmnn segeetttomic--220 2020
Chttps://githhi com/ www.llleenaaaaoaaaa

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Nodes: Each utterance in the conversation is regarded as a node in the graph, and we initialize each node with the utterance representation u; obtained from the encoder.
Number of items
ECF
RECCON
Conversations
1,374
1,106
Utterances
13,619
11,104
Emotion (utverances)
7,690
5,861
Emotion causee (ullerance) poits
9,794
9,915
Edges: The utterance nodes are linked in the temporal order of the conversation, and each edge assigned an attention weight Eij (i > j 2 1) representing the relationship between two utterances. We introduce CSK under two re- lation types to enrich the semantic dependencies among utterances. Specifically, given an utter- ance node, for each its neighbor nodes, if their speakers are the same, the knowledge represen- tation h is used calculate the edge weight between them; otherwise, he is utilized.
Table 1: Statistics of two benchmark datasets.
2.5 Model Training
During the training phase, we use the teacher fore- ing strategy to train our model and the negative log-likelihood loss to optimize the model. The loss function is defined as follows:
C L + Caux
(19)
M
1
L
log P(u Ye),,
(20)
M
For a target node u; the edge weight from neigh- bor nodes új can be computed as follows:
N
1
L
log P(y emoo
(21)
N
2; = w hy:
(10)
h = uj + Whn R
(11) where M and N refer the length of the output index sequence and the number of utterances in a
R3R wj + Whng
(12) conversation, respectively; of and yf denote the
7 = ====Leam Lea [ u; h R 1),
R
(13) ground truth label of index and emotion, respec- tively. Moreover, during the inference, we use the (14) beam search to obtain the target index sequence in an autoregressive manner.
oR = Lea ( u; hj Ro D)
RR S. Sj.
ij
Cij
S + Sj:
(15)
ij
Experiments
where a1, 02 C 2dd and Wh W c Rdxd are
learnable parameters.
3.1 Experimental Settings
Next, we normalize the edge weights across all choices of j using the Softmax function. Finally, the knowledge-enhancee utterance representation h; is obtained by aggregating neighbor nodes ac- cording to the normalized edge weights:
Datasets. We conduct experiments on two bench- mark datasets. ECF (Wang et al., 2022) mul- timodal conversational emotion cause dataset con- taining multi-party conversations from the sitcom Friends, which is closer to real-world scenarios. In this paper, we only consider the textual input. RECCON (Poria et al., 2021) includes dyadic con- (17) versations and is built for the task of emotion cause recognition in conversations. We use the subset RECCON-DD derived from DailyDialog (Li et al., 2017). Both datasets are divided into training, vali- dation and test sets. The basic statistics of the two datasets are shown in Table 1.
Oij = (
(16)
h = Σ Ug
JEAG
where MG the set of neighbor nodes of utterance node u, in the graph.
To facilitate our model to capture emotional dy- namics, we add an auxiliary task of emotion recog- nition in conversations (ERC). As shown in Figure 2, emotion classifier is applied to predict the emotion category of each utterance based the mhowledge-nnhaneee utterance representation.
Implementation Details. We utilize the pre- trained BART-base? model to initial the parameters in the index generation framework. During training, use the Adam optimizer with linear warm up and weight decay of lc-2 for parameter tuning. The batch size and initial learning rate are set to 16 and 2e-5, respectively. We use beam search to
(oy, = Softmax(MLP((::))
(18)
where mo E IR. is the probability distribution over all emotion categories including Neutral.
hhttps://huggingface
http:///hggggaaaaeccoccocoeabbba co/facebook/bart-.....

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Datasel
Method
Anger
Disgust
Fear
Jay
Sadness
Surprise
Avg.
4 Avg-
Pipeline
ECPes2steps (Wang 2022)
24.39
0.00
31.92
ECPE 2steps + CSK
23.86
0.00
3.71
38.41
23:13
40.71
29.50
32.03
ECPE 2D (Ding et al., 2020u)
25.13
0.90
0.00
41.25
21.62
43.24
30,80
33.55
BCF
HCPE-2D CSK
0.00
33.09
UECA Prompt (Zheng et al., 2022)
27.37
12.85
7.91
37.96
22.51
39.53
30.75
32.49
B2E
UECA-Prompt CSK
23.57
7.70
9.36
34.95
22:44
38.69
28.49
30.30
BART
27.15
4.37
2.20
38.66
25.51
37.53
30.35
32.74
SILARK (Ouns)
28.65
10.42
5.33
40.41
25.35
40.45
32.24
34.33
Pipeline
ECPEs2steps (Wang atal. 2022)
17.19
1.33
0.00
45.81
17.54
25.01
34.25
36.08
ECPP 2cPps2 CSK
19.62
0.61
0.95
45.34
20.34
25.80
34.71
36.57
RCPE-2D
2020a)
22.58
0.00
47.72
12.82
36.10
36.58
RECCON
ECPE 2D CSK
21.51
0.35
0.00
47,82
13.78
32.95
36.32
38. 30
B2B
UBCA-Prompt (Zheng et 2022)
24.11
11,38
31.20
47.53
21.40
35.09
38.63
39.63
Prompp
6.98
36,92
38.06
BART
27.68
11.99
37.99
44.04
26.63
33,42
37.73
38.46
SHARK (Ours)
27.00
12,88
42.65
46.88
31,09
32,18
39.82
40.53
Table 2: Performance comparison different methods on the ECTEC task. The best results are in bold.
generate the index sequence and set the beam size to 4. The results on the test set come from the best checkpoint in the validation set. We repeat all the experiments five times with different seeds and report the average results. All experiments are conducted on Nvidia RTX-3090 GPU.
ECF
RECCON
Method
Emo. Fi
Can.
Fj
Cau.
ECPE-2steps
55.46
62.83
64.34
58.92
ECPP-2steps CSK
55.61
65.05
64.96
58.83
BCPE-2D
ECPE 2D + CSK
56.28
66.51
66.24
62.22
UECA-Proopt
URCA-Prompe CSK
53.92
62.08
65.97
62.38
BART
58.63
70.02
67.96
69.01
Evaluation Metrics Similar to (Wang et al., 2022), we separately evaluate the emotion-cause pairs of each emotion category in the triplets with F1 score and further calculate weighted average of F1 scores across different emotion categories. Considering the imbalance of emotion categories in the two datasets, we also report the weighted av- erage F1 score the four main emotion categories except Disgust and Fear.
SHARK (Ours)
60.74
69.13
71.94
67.72
Table Results of the emotion extraction and cause extraction subtasks based on the predicted triplets.
It should be noted that ECPE-2D and UECA- Prompt are designed for ECPE and require modifi- cations to extend them to the ECTEC task. Specifi- cally. we adapt their emotion recognition module from binary classification multi-class classifica- tion. Moreover, we have explored simply incorpo- rating CSK into these methods, which is denoted as + CSK" in the tables of experimental results. For ECPE-2steps and ECPE-2D, we feed the gen- erated knowledge to their encoder obtain the knowledge representation for each utterance, fol- lowed by concatenating the knowledge representa- tion and the original utterance feature the final utterance representation. For (rompp,, we place the commonsense knowledge after each utter- ance, and the whole conversation is then fed into the model together. All the models are initialized with parameters from the pre-trained BERT-base" or BART-base for fair comparison.
3.2 Compared Methods
Since less work has been done on the ECPEC or ECTEC tasks, we compare our framework with several representative methods for ECPE in news articles: (1) ECPE-2steps (Xia and Ding, 2019) is the first pipeline framework proposed for ECPE, which individually extracts the emotion set and cause set, followed by emotion-cause pairing and filtering. Wang et al. (2022) has adapted it to the ECTEC task. (2) ECPE-2D (Ding et al., 2020a) is a joint end-to-end (F22)) framework using the cross- road 2D transformers to model the interactions of different emotion- Canotion-ccuse pairs. (3) UECA-Prompt (Zheng et al., 2022) is universal prompt method that decomposes ECPE into multiple objectives and converts them into sub-prompts. (4) BART, which only utilizes the index generation framework to generate the triplets, without two knowledge- enhanced modules and the auxiliary task our SHARK model.
3.3 Main Results
In Table 2, we report the results of different meth- ods the ECTEC task. To better compare these methods, we also report the F1 scores of two sub-
httpp:: thub. hemm/oggle-reeerch/aae https://huggingfer co/bert t- base- cased
or

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ECF
RECCON
Melhod
Ang.
Fear
Sod.
Sur.
Avg.
Avg.
Ang.
DNs.
Sad.
Sur.
6Avg.
Avg
SHARK
"olo GAL
27.77
7.65
5.37
38.97
25.50
39.35
31.25
33.42
25.51
16.23
-00.76
46.52
30.17
35.12
39.54
40.19
DGM
10.01
40.00
34.05
35.66
47.01
30.72
34.64
wio retrieved CSK
28.85
6.26
4.65
40.66
24.08
38.16
31.40
33.70
26.50
15.59
37.84
46.83
30.53
32.67
39.67
40.39
woo generated CSK
27.45
7.3]
8.88
39.3
19.34
26.09
37.48
31.05
33.13
24.89
15.94
40.54
46.63
31.33
31.42
39.33
39,97
CSK
10.90
5.52
26.71
39.84
13.58
48.75
36.82
wol auxilirr tack
28.74
7.66
4.00
38.77
26.65
35.33
30.75
32.92
28.03
12.51
33.97
45.34
27.22
32.68
38.52
39.35
Table 4: Ablation study on two datasets. "GAL" and "DGM" denotes the knowledge-aware graph attention layer and the dual-view gate mechanism, respectively.
tasks including emotion extraction and cause ex- traction in Table 3. Note that the weighted F for
ECF RECCON
RECCON - ECF
Method
6 Avg.
4 Avg.
6 Avg.
4 Avg.
emotion extraction is evaluated based the emo-
ECPE 2steps
25.95
27.35
16.03
17.46
ECPE-2-teps + CSK
27.12
28.54
17.19
18.68
tion categories in the predicted triplets, rather than
ECPE-20
28.82
30.38
16.36
17.82
the predictions from the auxiliary task.
BCPE-2D
28.71
UBCA-Prompt
28.87
29.77
17.60
18.74
Results on ECTEC. First, it is clear that the B2E methods significantly outperform ECPE-2steps, which shows the superiority of the joint E2E frame- work in addressing the ECTEC task compared to the two-step pipeline framework. Second, BART achieves comparable performance other base- line methods, indicating the feasibility of the in- dex generation framework to solve ECTEC. Third, the introduction of CSK on the baseline methods does not lead to significant improvements, and ECPE-2D + CSK, UECA-Promiy + CSK even per- form worse the two datasets. This suggests that simply concatenating knowledge may bring much noise, and it necessary explore more effec- tive approaches for knowledge fusion. Finally, we can clearly observe that our proposed SHARK ob- tains the best results and performs much better than BART, which demonstrates the effectiveness of our framework and reveals the benefits of incorporating CSK into the ECTEC task.
UBCA-Prompi
27.92
BART
35.91
37.12
18.04
19.26
SHARK (Ours)
36.56
37.68
18.70
19.87
Table 5: Cross-dataset evaluation for ECTEC. "A B" refers training on dataset and testing on dataset
experimental results are shown in Table 4. We can see that removing different modules from SHARK leads to varying degrees of perfor- mance degradation. Specifically, discarding the auxiliary task has the largest impact on perfor- mance, indicating that ERC is helpful for triplet extraction. Moreover, removing the generated CSK has greater negative effect compared to removing the retrieved CSK. This suggests the ability of the pre-trained knowledge model to generate relevant and informative commonsense knowledge, which is beneficial to emotion-cause understanding. Fur- thermore, the significant performance drop when both sources of external knowledge are disregarded highlights the importance of incorporating knowl- edge into the ECTEC task.
Results on two subtasks. Similar to the conclu- sions drawn on ECTEC, incorporating knowledge into the baseline methods through simple concate- nation may lead to a performance decline. How- ever, SHARK greatly outperforms other methods on both subtasks, further validating the effectiveness of our framework and the necessity of knowledge fusion. Furthermore, given that the relation types of CSK selected (xReact and oReact) directly related emotions, SHARK shows significant im- provement over BART primarily on the emotion extraction subtask.
3.5 In-Depth Analysis
Cross-dataset Analysis. To compare the general- ization of each method, we conduct cross-dataset testing between ECF and RECCON, and report the results in Table 5.
An obvious observation that the performance of all the methods significantly deteriorates when tested on a different dataset. This indicates that training a model on one dataset does not ensure good adaptation to other datasets. However, in comparison with other methods, BART and SHARK show great advantages across different datasets. es- pecially SHARK outperforms others by at least 7% on 4 avg. F1 under the ECF-RECCON setting, which demonstrates the strong generalization abil-
3.4 Ablation Study
To investigate the impact of individual modules on the overall performance, we conduct an abla- tion study of SHARK for the ECTEC task, and the

---

Melhod
utt 10
10
BCF
RECCON
Avg
14
Weighted Fl (%)
Weightd (16)
ECPE-2D
29.41
ECPE-2D CSK
32.84
36.61
29. 10
31.37
33
BART
34.50
29.91
32.05
SHARK (Ouns
33,68
36.16
31.49
33.46
32
Table Performance comparison of different methods for conversations with varying numbers of utterances from the test set ECF.
aVg. avg-
31-
38
Beamn Size
Beam Sio0
Ang.
Joy
Avg. 4.vg
Figure 3: The F1 change curve with different beam size.
ChaGGPT (O-shot)
13.19 62
28.57
24.14
14.29
16.95
18.32
17.89
ChaGGPT (S-shot)
14.81
20.00
0.00
33.33
26.92
15.69
22.18
22.76
(Ouns) 18.72
27.19
due the complexity of extracting three elements (i.e., emotion, cause, and category) in the ECTEC task, the performance of ChatGPT is not satisfac- tory.
Table 7: Performance comparison of ChatGPT and our framework on 50 conversations from the test set ECF.
ity and high robustness of our framework. More- over, we find that models trained on ECF can adapt RECCON well, while models trained REC- CON perform poorly on ECF. We conjecture the reason that the conversations in ECF come from TV series that are close to the real world, which involve informal text and complex scenes; while RECCON consists of human-generated conversa- tions that are simpler and more formal, thus weak- ening the generalization of the model.
Sensitivity Analysis of Beam Size. In order to investigate the impact of beam size on our SHARK model, we conduct experiments with dif- ferent beam sizes, and the results are shown in Figure The F1 curves exhibit slightly different trends on the two datasets, but a beam size of 4 is the optimal choice for both datasets.
3.6 Case Study
To show the advantage of our SHARK model, we compare its predictions with the output of three baseline systems
The Impact of Conversation Length. We con- duct further analysis to explore the impact of differ- ent conversation lengths, i.e., separately evaluating the predictions for conversations with varying num- bers of utterances from the test set of ECF. As shown in Table 6, we can observe that our gener- ative models perform much better than encoder- based methods conversations with more than 10 utterances. which account for about 42.65% in the ECF dataset. In these long conversations, encoder- based methods often fail to fully consider the con- textual information and ignore a number of triplets, while our generative models tend to comprehen- sively capture the context, effectively capturing broader range of triplets.
test sample. As shown in Figure 4, ECPE-2D and ECPE-2D + CSK only cor- rectly identify two caottion-cuse--aateoryy triplets, i.e., "(U2,U1 Moreover, our base model BART can further iden- tify another triplet "(U1,U1 suurpissrrrsse"", but still ig- nores "surprise) due to the long-term de- pendency between U4 and UI. In contrast, with the help of the CSK, SHARK correctly extracts all the four triplets. These observations show the ef- fectiveness of the proposed generative model and CSK for the ECTEC task.
and "(U2.U22,urpriise
Related Work
Comparison with ChatGPT. Considering the remarkable performance of large language models in various NLP tasks, we further apply ChatGPT to the ECTEC task under zero-shot and few-shot set- tings. Specifically, we randomly selected 50 conver- sations from the test set of ECF, and then fed each test conversation and task- insk-instruction prompt into ChatGPT obtain the predicted emotion-cause triplets. Table 7 presents the 0-shot and 5-shot results of ChatGPT. It is obvious that. SHARK per- forms significantly better than ChatGPT in both zero-shot and shot settings. We conjecture that
4.1 Emotion Cause Analysis
Emotion Cause Analysis (ECA) has attracted in- creasing attention in recent years. It contains two representative subtasks: emotion cause extraction (ECE) and emotion-causs pair extraction (ECPE). ECE aims to extract the potential causes given the emotions (Lee et al., 2010a.b: Gui et al., 20,6,, 2017: Fan et al., 2019); ECPE was proposed jointly extract the emotions and the correspond- ing causes in pairs, thereby solving the problem ECE's emotion annotation dependency (Xia and

---

fective computing (Majumder et al., 2019; Ghosal et al., 2020; Zhu al., 2021; Song al., 2022). Recent years have witnessed a shift from ERC to ECAC. Poria et al. (2021) introduced an interesting task of recognizing emotion cause in conversations, aiming to find the causes behind the given emo- tions in the conversation, and constructed new dataset RECCON, Several works for the CEE sub- task have subsequently emerged (Li et al., 2022a; Zhang al., 2022: Zhao al., 2023; Gu et al., 2023). Furthermore, Li et al. (2022b) and Jeong and Bak (2023) attempted to extract emotion and causes in conversations simultaneously, and Wang et al. (2022) introduced the ECTEC task. How- ever, the aforementioned encoder-only models for ECPEC mainly solve the task in a pipeline man- ner (independently predicting emotions and causes before matching, or first predicting emotions and then using the emotion predictions to infer causes), which suffer from error propagation. In contrast, our proposed generative framework enables end- to-end triplet generation, which can extract all the emotion-cause-catege triplets from conversa- tion in one shot.
JJoe feels carious, worried, confused. [Joyy foels happy, recognized, looked
Jory: Ohh... wonder it is that dude.
purtouat surprised (Others feel confured.
OOthere
Monica: There is a dude?
U2
U3
Joey: Yeah.
Phoebe feels curious, confused! Phoebe feels proud, proud, excrted
Generoted sReectt Betriced sRoort
Phoebe: Who? Who is It?
U4
Retriceed pilech
(Ur,,i,,urpriise, (U2, surrppis),, (U2,U2,surprise), (Urrpi,)
Ground Truth
(U1,U1,sadness), (U2,U1,surpriaee (U2,U2,,urprise)
ECPE-2D
ECPE-2D + CSK
(ur,ut,surprise), surrpii)r
(U1,U1,surprise), surrprii,,,i
BART
(U2,U2,surprise), (U3juz.surprise)
(Ur,U1,,urprise), (U2, U1.supiil,,, (U2,u2,surprise), (surpii
SHARK
Figure 4: Comparison of predictions test sample.
Ding, 2019). Much research has been conducted on the ECPE task. Xia and Ding (2019) first pro- posed two-step framework ECPE--steps, which first extracts individual emotion set and cause set, and then pairs the corresponding emotions and causes. Following their work, many end-to-end approaches have been proposed to address the lim- itations of the pipeline architecture. One line of work focuses on multi-task learning using joint modeling framework (Ding et al., 2020a,b; Wei et al., 2020; Fan et al., 2020; Chen et al., 2022). An- other line of work transforms ECPE into unified sequence labeling problem and designs novel tag- ging schemes (Yuan al., 2020; Chen al., 2020; Cheng al., 2021; Fan et al., 2021). More recently, several studies attempted to address the ECPE task with prompt-based based methods (Zheng et al., 2022) and machine reading comprehension-base meth- ods (Zhou et al., 2022; Cheng et al., 2023).
Conclusion
In this paper, we proposed commonSense knowledge-enHaree
generAtive fRameworK named SHARK for the Emotion Cause Triplct Ex- traction in Conversations (ECTEC) task. Specifi- cally, we formulated the ECTEC task as an index generation problem and employed a BART-based model to generate all the emotioo-ccause-category triplets in one shot. Moreover, we designed a dual- view gate mechanism and graph attention layer to incorporate both the retrieved and generated commonsense knowledge. Experimental results on two benchmark datasets show the superiority of SHARK over number of comparison systems and the usefulness commonsense knowledge.
Limitations
Although the proposed SHARK model has ob- tained state-of-the-art performance on two bench- mark datasets for the ECTEC task, our work still suffers from the following limitations.
4.2 Emotion Analysis in Conversations
Emotion recognition in conversations (ERC) is hot-spot task in sentiment analysis, which aims to assign emotion labels all the utterances in con- versation. Due to the increasing amount of public conversational data (Busso et al., 2008; Li al., 2017; Poria et al., 2019; Firdaus et al., 2020), ERC has received continuous attention in the field of af-
First, only consider the commonsense knowl- edge under two relation types, i.c., xReact and oRe- act, and thus design a dual-view gate mechanism to better capture the intra- atra-speaker and inter-speaker interactions. However, there are other causal rela-

---

tions that may help cause inference, such as xWant and oWant. We plan to incorporate the other related causal relations into our generative framework in the future. Second, it might be interesting to ex- plore the potential of incorporating commonsense knowledge for different modalities to boost the per- formance of the Multimodal ECTEC task.
ACM Transactions on Information Systems, 41(4):1- 27.
Zixiang Ding, Rui Xia, and Jianfei Yu. 2020a. ECPE- 2D: Emotion-cause pair extraction based on joint two-dimensional representation. interaction and pre- diction. In Association for Computational Linguis- tics (ACI), pages 3161-3170.
Ethics Statement
Zixiang Ding, Rui Xia, and Jianfei Yu. 2020b. End-to- end emotion-cause pair extraction based on sliding window multi-label learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3574-3583.
This paper does not involve any data collection and release, and thus there experiments are conducted on two publicly avail- able datasets, namely ECF (Wang al., 2022) and
privacy issues. All the
Paul Ekman et al. 1999. Basic emotions. Handbook of cognition and emotion, 98(45-60):16.
RECCON (Poria et al., 2021), which do not include personal information or contain any objectionable content that could potentially harm individuals or communities.
Chuang Fan, Hongyu Yan, Jiachen Du Lin Gui, Li- dong Bing, Min Yang. Ruifeng Xu, and Ruibin Mao. 2019. A knowledge regularized hierarchical ap- proach emotion cause analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natu- ral Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-JCCNL", pages 5614-5624.
Acknowledgements
The authors would like thank the anonymous reviewers for their insightful comments. This work was supported by the Natural Science Foundation of China (62076133 and 620061 17), and the Nat- ural Science Foundation of Jiangsu Province for Young Scholars (BK20200463) and Distinguished Young Scholars (BK20200018).
Chuang Fan, Chaofa Yuan, Jiachen Du, Lin Gui, Min Yang. and Ruifeng Xu. 2020. Transition based di- rected graph construction for emotion-cause cause pair ex- traction. In Association for Computational Linguis- rics (ACL), pages 3707-3717.
Chuang Fan, Chaofa Yuan, Lin Gui, Yue Zhang, and Ruifeng Xu. 2021. Multi-task sequence tagging for emotion-cause pair extraction via tag distribution re- finement. IEEF/ACM Transactions on Audio, Speech, and Language Processing, 29:2339-2350.
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Reviews:
[{"review_id": "lcPVqbBDhr", "reviewer": "Reviewer_63gB", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your careful review and valuable suggestions. We will explain your concerns point by point below.\n\n**Q1: About the motivation for solving the ECTE task with the generative model.**\n\n**A1:** We clarify our motivation as follows: \n\nTo address the ECTEC task, an intuitive idea is to first identify which utterance expresses the emotion, and then find the cause utterance that triggers it, followed by determining the emotion category based on the emotion-cause utterance pair. Therefore, emotion is the core element of the ECTEC task, and the other two elements (i.e., cause and category) are highly dependent on the emotion utterance. We believe the decoder in the generative model can well capture the conditional dependency relationship among the three elements, because it generates the emotion utterance, cause utterance, and emotion category in an autoregressive manner to form the triplet.\n\nThanks for your constructive comments. We will add this argument in our revision to strengthen the motivation behind the proposed generative model.\n\n**Q2: The fine-grained analysis regarding the function of the specific module is missing.**\n\n**A2:** We report the fine-grained experimental results on the ECF dataset in the table below. In general, compared with previous encoder-based methods (ECPE-2D), the generative module (BART) significantly boosts the *Recall* metric, i.e., 6.25% and 6.61% in two weighted average scores, respectively. In addition, comparing the results of BART and SHARK, it is easy to find that the integration of CSK (SHARK) brings a significant increase in the *Precision* metric, i.e., 2.66% and 2.69% in two weighted average scores, respectively.\n\n| Method         | 6 Avg. P  | 6 Avg. R  | 6 Avg. F1 | 4 Avg. P  | 4 Avg. R  | 4 Avg. F1 |\n| -------------- | --------- | --------- | --------- | --------- | --------- | --------- |\n| ECPE-2D        | 35.68     | 28.94     | 30.80     | 38.87     | 31.53     | 33.55     |\n| ECPE-2D +  CSK | **36.94** | 26.66     | 30.37     | **40.25** | 29.05     | 33.09     |\n| BART           | 27.70     | **35.19** | 30.35     | 29.23     | **38.14** | 32.74     |\n| SHARK (Ours)   | 30.36     | 35.02     | **32.25** | 31.92     | 37.41     | **34.23** |\n\nMoreover, we conduct further analysis to explore the impact of different conversation lengths, i.e., separately evaluating the predictions for conversations with varying numbers of utterances in the test set. As shown in the following table, we can observe that our generative models perform much better than encoder-based methods on conversations with more than 10 utterances. In these long conversations, encoder-based methods often fail to fully consider the contextual information and ignore a number of triplets, while our generative models tend to comprehensively capture the context, effectively capturing a broader range of triplets.\n\n| num_utt ≤ 10   |              |              |               |              |              |               |\n| -------------- | ------------ | ------------ | ------------- | ------------ | ------------ | ------------- |\n| **Method**     | **6 Avg. P** | **6 Avg. R** | **6 Avg. F1** | **4 Avg. P** | **4 Avg. R** | **4 Avg. F1** |\n| ECPE-2D        | 35.57        | 33.22        | 33.56         | 39.65        | 37.04        | 37.41         |\n| ECPE-2D +  CSK | 35.94        | 30.73        | 32.84         | 40.07        | 34.26        | 36.61         |\n| BART           | 28.69        | 36.54        | 31.45         | 30.56        | 40.37        | 34.50         |\n| SHARK (Ours)   | 31.27        | 37.18        | 33.68         | 33.46        | 40.00        | 36.16         |\n\n| num_utt > 10   |              |              |               |              |              |               |\n| -------------- | ------------ | ------------ | ------------- | ------------ | ------------ | ------------- |\n| **Method**     | **6 Avg. P** | **6 Avg. R** | **6 Avg. F1** | **4 Avg. P** | **4 Avg. R** | **4 Avg. F1** |\n| ECPE-2D        | 36.00        | 26.91        | 29.41         | 38.81        | 29.01        | 31.71         |\n| ECPE-2D +  CSK | 38.02        | 24.73        | 29.10         | 40.99        | 26.66        | 31.37         |\n| BART           | 27.60        | 34.56        | 29.91         | 28.89        | 37.12        | 32.03         |\n| SHARK (Ours)   | 29.77        | 34.26        | 31.49         | 31.50        | 36.47        | 33.46         |\n\n\n**Q3: The writing in the first paragraph lacks smoothness. I'm unsure why the author opted to begin with a discussion on 'social media'.**\n\n**A3:** Thanks for pointing out this. We will adopt your nice suggestion to rewrite the first paragraph in our revision.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper's writing is solid, with the methods section being particularly well-structured and easy to follow.  \n2. The proposed generative-based model is the first unified model for the emotion-cause-triplet extraction task.  \n3. The authors conducted extensive experiments, including re-implementing other baselines, and the achieved performance scores are impressive, demonstrating the model's effectiveness.", "reasons_to_reject": "1. The generative-based model proposed in the paper may not be considered novel, as similar approaches exist in general information extraction or sentiment analysis areas. The issue of error propagation is also common in these tasks, and it is not specified for emotion-cause-triplet extraction. To strengthen the motivation behind the proposed model, the authors could provide a more persuasive argument that highlights the unique challenges of emotion-cause-triplet extraction and how the generative model is well-suited to handle them. The core of this task revolves around understanding how one utterance causes an emotion in another utterance, and further clarification on this aspect would be beneficial.  \n2. In the experiment part, the fine-grained analysis regarding the function of the specific module is missing, which results in the necessity of the proposed module not being fully verified. The overall score and ablation study show the results from a high-level view. However, there should be more fine-grained, numerical analysis regarding the generative-based model and CKS. While a case study can offer an intuitive understanding of the module’s performance, it is essential to ensure that the results are not biased by carefully selected cases. Therefore, it would be beneficial to examine what kinds of cases or problems the module, e.g., generative module, is oriented to solve and to what degree the module improves the performance for these specific cases.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper proposes an end-to-end model equipped with commonsense reasoning for the task of emotion-cause triplet extraction. The model utilizes BART to generate triplets, unifying the typically separate subtasks involved in this overall task. This unified approach helps address issues with error propagation that can occur when splitting the task into multiple stages. Additionally, the authors introduce a novel dual-view gate mechanism to incorporate external knowledge into the model, addressing the lack of external knowledge utilization in previous work. Experiments demonstrate the effectiveness of the proposed model on this challenging task.", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "1. The writing in the first paragraph lacks smoothness. The author should consider a more coherent transition from the broader context to the specific task at hand. I'm unsure why the author opted to begin with a discussion on 'social media' when none of the datasets used in this paper originate from social media. Additionally, the applications of emotion-cause pair extraction such as customer support, mental health care, and human-computer interaction, seem to have a minimal connection with social media."}}, {"review_id": "tD1KGje0Nd", "reviewer": "Reviewer_xHNc", "summary": "", "questions": "1. Is there any comparison between the proposed method and the ChatGPT (zero-shot)?\n2. Considering the knowledge extraction and fusion modules, does the proposed method  cost more time for training and inference than baselines?\n3. Will you make the source code open-sourced? This can benefit the future research that follow your work and improve it.\n", "limitations": "", "rating": 3, "confidence": 5, "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": "Thanks for your careful review and valuable suggestions. We will explain your concerns point by point below.\n\n**Q1: Is there any comparison between the proposed method and the ChatGPT (zero-shot)?**\n\n**A1:** We have evaluated the performance of ChatGPT on the ECTEC task. Specifically, we randomly selected 50 conversations from the test set of ECF, and then fed each test conversation and a task-instruction prompt into ChatGPT to obtain the predicted emotion-cause triplets. The table below presents the zero-shot and few-shot results of ChatGPT. It is obvious that SHARK performs significantly better than ChatGPT in both zero-shot and few-shot settings. We conjecture that due to the complexity of extracting three elements (i.e., emotion, cause, and category) in the ECTEC task, the performance of ChatGPT is not satisfactory.\n\n| Method         | Anger | Disgust | Fear  | Joy   | Sadness | Surprise | 6 Avg. F1 | 4 Avg. F1 |\n| -------------- | ----- | ------- | ----- | ----- | ------- | -------- | --------- | --------- |\n| ChatGPT 0-shot | 13.19 | 21.62   | 28.57 | 24.14 | 14.29   | 16.95    | 18.32     | 17.89     |\n| ChatGPT 5-shot | 14.81 | 20.00   | 0.00  | 33.33 | 26.92   | 15.69    | 22.18     | 22.76     |\n| SHARK (Ours)   | 18.72 | 5.06    | 0.00  | 39.03 | 12.89   | 37.36    | 27.19     | 29.23     |\n\nWe will add these results in our revision.\n\n**Q2: There is no error analysis.**\n\n**A2:** Thanks for your nice suggestion! We will add more error analysis in our revision. Based on our observation, the proposed generative framework generally predicts more triplets, and thus significantly improves the *Recall* metric. However, it sacrifices the *Precision* metric to some extent (as shown in the table below). Hence, we plan to analyze several representative error cases regarding imprecise predictions in our revision.\n\n| Method         | 6 Avg. P  | 6 Avg. R  | 6 Avg. F1 | 4 Avg. P  | 4 Avg. R  | 4 Avg. F1 |\n| -------------- | --------- | --------- | --------- | --------- | --------- | --------- |\n| ECPE-2D        | 35.68     | 28.94     | 30.80     | 38.87     | 31.53     | 33.55     |\n| ECPE-2D +  CSK | **36.94** | 26.66     | 30.37     | **40.25** | 29.05     | 33.09     |\n| BART           | 27.70     | **35.19** | 30.35     | 29.23     | **38.14** | 32.74     |\n| SHARK (Ours)   | 30.36     | 35.02     | **32.25** | 31.92     | 37.41     | **34.23** |\n\n\n**Q3: Does the proposed method cost more time for training and inference than baselines?**\n\n**A3:** We have conducted experiments on an Nvidia RTX-3090 GPU to compare the computational efficiency of different methods and report the training and inference time as well as the parameter size.  As shown in the table below, we make the following observations: \n\n- SHARK incurs slightly higher computational cost than the baseline model BART, due to introducing more parameters for the knowledge extraction and fusion modules.\n- Although there are more parameters, the training time and inference time of SHARK are comparable to those of UECA-Prompt.\n\nBased on these observations, we believe that the computational cost of SHARK is generally acceptable considering the performance improvement over other baselines.\n\n| Method             | Number of Parameters (M) | Training Time (s) | Inference Time (s) |\n| ------------------ | ------------------------ | ----------------- | ------------------ |\n| UECA-Prompt        | 108.34                   | 69.19             | 139.52             |\n| UECA-Prompt  + CSK | 108.34                   | 70.31             | 140.67             |\n| BART               | 140.69                   | 55.40             | 118.06             |\n| SHARK (Ours)       | 152.14                   | 77.67             | 144.53             |\n\n*Note*：`Training Time` refers to the duration for the model to run through the training set and the verification set in a single training epoch, and `Inference Time` refers to the time it takes to make predictions on the entire test set. The training/inference time of UECA-Prompt is measured based on its publicly available source codes.\n\n**Q4: Will you make the source code open-sourced?**\n\n**A4:** We will open-source our source codes via GitHub, and add the link in our revision.", "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 proposed method is novel and interesting for the community.\n2. The proposed model can achieve new state-of-the-art performance.\n3. The proposed knowledge integrating modules can fuse the external knowledge adaptively.\n", "reasons_to_reject": "1. As ChatGPT is widely adopted for NLP application tasks in nowadays, no results from ChatGPT are provided. If the zero-shot results of ChatGPT can outperform the proposed model, it is hard to highlight the meaning of this work.\n2. It is good to include some cases to show the superiority of this work, but there is no error analysis.\n3. There are no computation or time cost comparisons with baselines. As mentioned in Introduction, this task is widely applied in real-world scenarios, so computational efficiency should be an important factor.\n4. The dataset and source code of this work is not provided. It is hard to reproduce the results reported in this work.", "questions_to_authors": "1. Is there any comparison between the proposed method and the ChatGPT (zero-shot)?\n2. Considering the knowledge extraction and fusion modules, does the proposed method  cost more time for training and inference than baselines?\n3. Will you make the source code open-sourced? This can benefit the future research that follow your work and improve it.\n", "paper_topic_and_main_contributions": "This model tackles the task of Emotion-Cause Triplet Extraction in Conversations (ECTEC).\nThis paper proposes a commonsense knowledge-enhanced sequence-to-sequence model.\nExperiment results show that the proposed model can achieve new state-of-the-art performance.", "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": "ME5QyXPOM7", "reviewer": "Reviewer_yf8h", "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": "Thanks for your careful review and valuable suggestions. We will explain your concerns point by point below.\n\n**Q1: What are the advantages of the generation paradigm?** \n\n**A1:** We first clarify our motivation behind the proposed generative model for the ECTEC task.\nTo address the ECTEC task, an intuitive idea is to first identify which utterance expresses the emotion, and then find the cause utterance that triggers it, followed by determining the emotion category based on the emotion-cause utterance pair. Therefore, emotion is the core element of the ECTEC task, and the other two elements (i.e., cause and category) are highly dependent on the emotion utterance. We believe the decoder in the generative model can well capture the conditional dependency relationship among the three elements, because it generates the emotion utterance, cause utterance, and emotion category in an autoregressive manner to form the triplet.\n\nFurthermore, previous encoder-only models for ECPEC mainly solve the task in a pipeline manner (independently predicting emotions and causes before matching, or first predicting emotions and then using the emotion predictions to infer causes), which suffer from error propagation. Although a number of joint models have been proposed to boost the performance of the ECPE task, they are primarily based on multi-task learning, which still suffers from error propagation. In contrast, our proposed generative framework enables end-to-end triplet generation, which can extract all the emotion-cause-category triplets from a conversation in one shot.\n\nMoreover, as shown in the Cross-dataset Evaluation in Table 5, the generative framework demonstrates stronger generalization, suggesting extensive potential for real applications.\n\n\n**Q2: Can we remove the index2token step in Figure 2?** \n\n**A2:** No, it is necessary to keep the index2token step in our current framework. This is because if we remove the index2token step, the input of the decoder would be a sequence of position indexes rather than the tokens from the source text, in which the semantic meanings of the numerical indexes are ambiguous, preventing effective attention interaction with the output representations of the encoder.\n\n\n**Q3: More details on the implementation of baselines and justify that they are properly tuned and fairly compared.**\n\n**A3:** Thanks for your suggestion. We will add more details about the implementation of baselines. Specifically, all models are initialized with parameters from a pre-trained model of 'base' size (BERT-base/BART-base). For a fair comparison, we use the same batch size for all the models and conduct all the experiments on an Nvidia RTX-3090 GPU. The average results of multiple experiments are reported for each model. Since ECPE-2D and UECA-Prompt are designed for ECPE, we adapt their emotion recognition module from binary classification to multi-class classification to perform our task.\n\n\n**Q4: It would be better to compare number of parameters (model size) and inference speed.**\n\n**A4:** We have conducted experiments on an Nvidia RTX-3090 GPU to compare the computational efficiency of different methods and report the training and inference time as well as the parameter size.  As shown in the table below, we make the following observations: \n\n- SHARK incurs slightly higher computational cost than the baseline model BART, due to introducing more parameters for the knowledge extraction and fusion modules.\n- Although there are more parameters, the training time and inference time of SHARK are comparable to those of UECA-Prompt.\n\nBased on these observations, we believe that the computational cost of SHARK is generally acceptable considering the performance improvement over other baselines.\n\n| Method             | Number of Parameters (M) | Training Time (s) | Inference Time (s) |\n| ------------------ | ------------------------ | ----------------- | ------------------ |\n| UECA-Prompt        | 108.34                   | 69.19             | 139.52             |\n| UECA-Prompt  + CSK | 108.34                   | 70.31             | 140.67             |\n| BART               | 140.69                   | 55.40             | 118.06             |\n| SHARK (Ours)       | 152.14                   | 77.67             | 144.53             |\n\n*Note*：`Training Time` refers to the duration for the model to run through the training set and the verification set in a single training epoch, and `Inference Time` refers to the time it takes to make predictions on the entire test set. The training/inference time of UECA-Prompt is measured based on its publicly available source codes.", "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 is in general well-written and easy to follow (though some notations are dense)\n- the empirical results are good.", "reasons_to_reject": "- the rationale behind generating rather than discriminating triple entries needs more discussions and empirical evaluations: roughly, encoder-only models (with some additional classification layers) could also be applied to perform the extraction. What are the advantages of the generation paradigm? \n- can we remove the index2token step in Figure 2 (with supervised decoding, it is possible for BART to adapt to the new input sequence). \n- it should be helpful to elaborate more details on the implementation of baselines and justify that they are properly tuned and fairly compared.\n- it would be better to compare number of parameters (model size) and inference speed. \n", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper studies the problem of extracting triples of {emotion cause, emotion utterance, emotion category} from conversations. The main technical contribution is an end-to-end generation-based model with commonsense knowledge.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes an end-to-end model for emotion-cause triplet extraction which is augmented with commonsense knowledge. The unification proposed in this paper aims to better handle error propagation. The generated and retrieved commonsense knowledge are integrated into the model with a novel gating mechanism. The results exhibit the effectiveness of the proposed model. The paper was missing some key details which the rebuttal helped addressing. The additional inference/training time footprint argument and the argument about its closeness to the existing approaches (i.e., UECA-Prompt) is rather unconvincing when we consider those numbers at large scale. The writing, in particular the introduction, needs further improvement to better position the problem.
Decision: Accept-Findings