A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis
Authors: Nan Song
Hongjie Cai
Rui Xia
Jianfei Yu
Zhen Wu
Xinyu Dai
Venue: EMNLP 2023 Findings (2023)
Keywords: Aspect-based Sentiment Analysis
Document-level
Targeted Sentiment Analysis

Abstract: Most previous studies on aspect-based sentiment analysis (ABSA) were carried out at the sentence level, while the research of document-level ABSA has not received enough attention. In this work, we focus on the document-level targeted sentiment analysis task, which aims to extract the opinion targets consisting of multi-level entities from a review document and predict their sentiments. We propose a Sequence-to-Structure (Seq2Struct) approach to address the task, which is able to explicitly model the hierarchical structure among multiple opinion targets in a document, and capture the long-distance dependencies among affiliated entities across sentences.  In addition to the existing Seq2Seq approach, we further construct four strong baselines with different pretrained models. Experimental results on six domains show that our Seq2Struct approach outperforms all the baselines significantly. Aside from the performance advantage in outputting the multi-level target-sentiment pairs, our approach has another significant advantage - it can explicitly display the hierarchical structure of the opinion targets within a document. Our source code is publicly released at https://github.com/NUSTM/Doc-TSA-Seq2Struct.

A Sequenee-o-Structtre Approach to Document-level Targeted Sentiment Analysis
Nan Song Hongjie Cai Rui Xia " Jianfei Yu! 1, Zhen Wu , Xinyu Dai 2
School of Computer Science and Engineering.
Nanjing University of Science and Technology, China
National Key Laboratory for Novel Software Technology, Nanjing University, China (nsong, hjcai, rxia, jfyujenjust. edu. cn {wuz, daixinyujenju.ed edu. cn
Abstract
Input: Such great restaurant Service Service is quick and friendly even when it 's crowded Staff was really welcoming and recommended the lobster roll, but price was very expensive..
Most previous studies on aspect-based senti- ment analysis (ABSA) were carried out the sentence level, while the research of document- level ABSA has not received enough attention. In this work, focus the document- level targeted sentiment analysis task, which aims to extract the opimion targets consisting of multi- level entities from review document and pre- dict their sentiments. We propose a Sequence- to-Structure (Seq2Struct) approach to address the task, which is able to explicitly model the hierarchical structure among multiple opinion targets in document, and capture the long- distance dependencies among affiliated entities across sentences. addition to the existing Seq2Seq approach, we further construct four strong baselines with different pretrained mod- els. Experimental results on six domains show that our Seq2Struct approach outperforms all the baselines significantly. Aside from the per- formance advantage in outputting the multi- level target-sentiment pairs, our approach has another significant advantage- it can explicitly display the hierarchical structure of the opinion targets within a document. Our source code is publicly released at https://g /gi thub. com/ NUSTM/Doc- DSwDDDo----------- TSA- Seq2Struct.
(a) Sequence-to-Sequence (Previous work)
<b> restaurant <е> Mixed <se> <h> restaurant <i> service <e> Positive <se> <b> restaurant <i> lobster roll <e> Negative <h> restaurant <i> lobster roll price
Negative <b> restaurant <i> staff Positive
(b) Moaluence-to-Stuuctuce (Ours)
restaurant
service
lobster
staff
Mixed
Positive
price
Negative
Positive
Negative
Output: (frestaurant, Mixed); (restaurant-service,
Positive); (restaurant-losste, roll, Negative); (restaurant- lobster lobster_roll-price, rolicee Negative); (restaurant-staff Positive)).
Figure 1: Comparison of two different approaches for the document-level TSA task. Text chunks in blue repre- sent flat entities, and multi-level entities are connected with "_" form opinion target. Text chunks in red indicate the sentiment polarities of opinion targets.
1 Introduction
cations, yet has not received enough attention. Only a limited amount of work attempted to iden- tify the sentiments towards all aspect categories in document (e.g.. the Food Quality category in Restaurant domain), without involving explicit opinion target terms (c.g., entities or aspects) (Titov and McDonald, 2008; Pontiki al., 2016; Li al., 2018: Wang al., 2019; Bu et al., 2021).
Aspect-based sentiment analysis (ABSA) has re- ceived wide attention in NLP for nearly two decades (Hu and Liu, 2004). Most of the previ- studies have focused on sentence-level ABSA. However, review text often consists of multiple sentences, and the opinion targets expressed in these sentences are often interrelated. Conducting sentence-level ABSA on each individual sentence cannot capture the interrelated opinion targets in the entire document. In comparison, document- level ABSA is more suitable for practical appli-
Recently, Luo et al. (2022) introduced new task called deeument-level Targeted Sentiment Anal- ysis (document-level TSA), aiming to discover the opinion target consisting of multi-level enti- ties (or aspects) in a review document, and pre- dict the sentiment polarity label (Positive, Negative
Equal contribution. Corresponding author.

---

Mixed) towards the target. In sentence-level ABSA, an opinion larget is usually single entity or aspect. While in document-level TSA, it often involves multiple entities and aspects throughout the review document, and their relation is affiliated rather than flat. As shown in Figure 1, "restau- rani lobsttrrrool - price" is an opinion target consisting of three-level entities', indicating the price of the lobster roll sold in the restaurant. Luo al. (2022) accordingly proposed a sequence-to- sequence (Seq2Seq) framework to solve the task. By using BART as the backbone, they took the review document as the input, and output a se- quence indicating a set of target and sentiment tuples. For example, the tuple "<h> restaurant <i> lobster_roll <i> price <e> Negative <se>" de- notes that the multi-level opinion target is "restau- rant lobster roll- price" and its corresponding sentiment is Negative.
entities. Thirdly, we employ table-filling method to identify the affiliation relations in flat entities and consequently get the hierarchical opinion tar- get structure. We finally incorporate the sentiments of the flat entities into the hierarchical structure and parse out the turget-sentiment pairs as defined in (Luo et al., 2022).
We evaluate our approach on the document-level 'SA dataset containing six domains. In addition to the existing approach, we further construct four strong baselines with different pretrained models. The experimental results show that our Seq2Struct approach outperforms all the baselines sigmificantly on average Fl of the target-sentiment pairs. Aside from the advantage of performance, our approach can further explicitly display the hierarchical struc- ture of the opinion targets in document. We also make in-depth discussions from the perspectives of document length, the number of levels in opinion target, etc., verifying the effectiveness of our ap- proach in capturing the long-distance dependency among across-sentence entities in document-level reviews.
As shown in Figure 1(b), there is actually a hi- erarchical structure among multiple opinion tar- gets in the document: the first layer is the "restau- rant" entity, the second layer contains three en- tities (or aspects) affiliated to "restauranf" ("ser- vice". "lobster_rone "ool"" and "staff"", and the third layer is the "price" aspect of "lobster_roll" though the Seq2Seq method appears simple and straightforward, it imperfect to model such com- plex hicrarchical structure. On one hand, it outputs the structural information by a sequence of tuples, where the previous tuples affects the generation of subsequent ones. On the other hand, the inherent encoder-decoder architecture is less flexible and effective model the long- -in-tnite dependencies among affiliated entities/aspects across sentences.
2 Task Description
Al-
In document-level TSA task, an opinion target of- ten consists of multi-level entities with affiliated relations (e.g.. "restaurant fobster_roll price"). We call "multi-level opinion target" in contrast to the opinion target at the sentence level. A docu- ment normally contain a set of multi-level opinion targets, which constitute a hierarchical structure shown in Figure 1(b).
Similar as (Luo et al., 2022), we formulate document-level TSA as task to detect set of target-sentiment pairs from a document [11, x2, ...: ÆN| with N tokens:
To address the aforementioned issues, we pro- pose a Sequence-to-Struccuue (Seq2Struct) ap- proach in this work for the document-level TSA task. Our approach still takes a document as the input, but the output is no longer a sequence, but structure as shown in Figure 1(b). It is hierarchi- cal structure with multiple layers of related entities, where each entity is assigned predicted sentiment polarity. Seq2Struct contains four mains steps. We firstly identify flat opinion entities and their senti- ments from the document. Secondly, we propose a multi-grain graphical model based on graph con- volutional network (GCN), to better learn the se- mantic relations between document, sentences and
p (11,5)
where
-m is a multi-level opinion target with m denoting the number of its levels, and E (Positive, Negative, Mixed] is the correspond- ing sentiment.
The document-level TSA task is challenging, the opinion target consisting of multi-level entities, and a predicted opinion target is considered to be correct if and only if entities at all levels match the ground-truth exactly. For instance, "restaurant- lobster-price" is the correct target only if the first, second, and third levels are predicted as "restau- rant", "lobsterr", and "price", respectively.
For the convenience of description, in the following we collectively refer to both "cntity" (e.g. "lobster " and "aspoct" (c.g. "price") as "entity".

---

Multi-grain Graphical Model
Hierarchical Opinion Target Structure identification
Output
[1] Such great restaurant [2] Service is quick ..... crowded. [3] Staff was really ..... the lobster roll. but price was very expensia
(restaurant staff, Positive) (restaurant- lobster rool,
Negative) (restaurant- lobster rrol price, Negative)
(restaurant, Mixed) Mixed) (restaurant service, Positive)
De88B
Sp
Positive
(0
e Mixed
Aoss-Attettion
service
Positive
(e2
staff
P3
Positive
(ey P3)
0, Negative e, -Posiiivee eg Positive
lobster_roll
P4 Negative
(6 Pa)
price
Ps Negative
(05- Pg)
e G-Negttive
Entity Extraction Sentiment Classification
Target-Sentiment Parsing
Figure 2: The overall architecture of our Seq2Struct approach.
3 Approach
Dataset
#Total
#Multi
#Cross
#Cross/#Multi
Books
2470
1005
804
80.00%
As shown in Figure 2. we propose a Sequence- co-Structure (Seq2Struct) approach address the document-level TSA task, which consists of four main modules.
Clothing
1554
389
278
72.46%
Restaurant
4739
2796
2466
88.19%
Hotel
3436
2028
1572
77.51%
Table I: Statistics of opinion targets in four domains of Luo (2022), where #Total means the total number of opinion targets, #Multi means the numher of opinion targets with more than one level, and #Cross means the number of cross-sentence opinion targets.
3.1 Flat Entity Extraction and Sentiment Classification
We adopt DeBERTa (He et al., 2021) as the encoder the input document D = [æ1, 22, ...,,N)::
DeBERTa(2),..., IN),
(2)
where Q = h K V H. Then, h is fed into the softmax layer to predict the sentiment towards e;:
where H (hi, h2. h is context representa- tion D.
We then employ the BIO (Begin, Inside, Outside) tagging scheme to extract flat entities" 2 from D:
p(vile)) = Softmax W
(5)
Mulla: = Softmax(h, We),
(3)
where yf E (Positive, Negative, Mixed] and W. E Rddmodd is the model parameter.
where of E (B,1,O), W. E Roodde x3 is model parameter, and d'oode! is the dimension of the hid- den representation of each token.
We use S = s) to represent the pre- dicted flat entity-sentiment pair set. For the exam- ple shown in Figure 2, S = {("restaurant", Pos- itive), ("price", Negative), ("service", Positive), ("staff"" Positive), ("lobster_roll" Negative)).
Let { represent the extracted en- tity set, where = (Istarts .t, Fend). The rep- resentation of e; is the mean pooling of its to- kens h = conPPoooongghtarr.... ..., hena). Fur- thermore, we perform sentiment prediction on the extracted flat entities. Specifically, for an entity e; in & we utilize an entity-context cross-attention module to capture the context information:
3.2 Multi-grain Graphical Model
The affiliated entities in multi-level opinion target often exist in multiple sentences. In Table 1, we re- port the number and proportion of across-sentence opinion targets in four domains of the dataset (Luo et al., 2022). It can be seen that the proportion of across-sentence opinion targets opinion targets reaches 80.0%, 72.5%, 88.2% and 77.5% in four domains respectively.
MullileeadCrossAAteettooo(
multi-level
Flltt entities in this paper refer to all individual entities in the document, regardless of the hierarchy.

---

lobstor.co Toll
rentourent
service
price
Staff
price
0
Q
restai
rant
lobster
Kobster
rall
Service
staff
Mi
price
lobster
POL
service
0
roll
Service
staff
price
restaurant
1
(b)
lobster_rol roll
0
0
price
Figure 4: The process of Target-Sentiment Pair Parsing- For the left figure, cach connected pair of blue and red nodes represents a flat entity and its corresponding sentiment, respectively. For the right figure, multi-level entities within the dashed hox constitute an opinion target, and the red node indicates the updated sentiment polarities of opinion target.
(a) Affiliated Relation Table
(b) Hierarchical Structure
Figure 3: An illustration of Hierarchical Opinion Target Structure Identification.
To better model the affiliated relations and long- distance dependencies between different entities, we construct multi-grain graphical model, en- hance entity representation learning.
flat entities from the input document, The row e represents the upper-level entity, and the column ej represents the lower-level entity. We concatenate the representations of e₁ and éj as the represen- tation of the cell Tey: hi [h;; ht], and then send a binary classifier predict the affiliated relation:
The graph contains three different types of nodes: 1) Document node, 2) Sentence nodes, and 3) Entity nodes. These nodes are linked with three types of edges: 1) Document-to-Sentennc Edges: The document node is linked to all sentence nodes; 2) Sentence-to-Entire edges: Each sentence node is linked all entity nodes it contains: 3) Entity-to- Entity edges: We maintain a global entity relation map to capture affiliated relations between entities. As long as entity eą and entity 6 were annotated as adjacent upper-level and lower-level entities (i.e., is affiliated with ex) in any document of the training and validation corpus, éş is linked to ex-
= Softmax(h!, W,),
(7)
where W, E R22dmode! x2 is the model parameter. Vig E (1,0) indicates the affiliated relation be- tween e; and ej. Tij means that ej is affiliated with et.
Secondly, based on the predictions on each cell of T, we can finally obtain a hicrarchical opinion target structure (a directed acyclic graph G), as shown Figure 3(b). The cell whose value constitutes an affiliated two-level entity (e.g., "lob- ster_roll price"), and after decoding all cells on the entire table, we get the hierarchical structure.
On this basis, construct the adjacency ma- trix A where Ai node have an edge, otherwise Ag O. We then employ the Graph Convolutional Network (Kipf and Welling, 2017) to update the representation of nodes:
if the i-th node and j-th
Note that when the hierarchical structure con- tains self-loop, we delete the edge with the small- value of p(u 1[(e,, ez)) ensure the output directed acyelic graph.
= bi),
(6)
where l is the index of GCN sub-layers, W. E modell modede and by E Rddddde are model param- eters, (-) is an activation function, i.e., RELU.
3.4 Tanget-Sentiment Pair Parsing
In this section, we introduce set of rules to parse out the target-sentiment pairs based on the pre- dicted sentiments of flat entities and the hierarchi- cal opinion target structure.
3.3 Hierarchical Opinion Target Structure Identification
Till now, we have extracted the flat entities & and learned better entity representations Hº In this subsection, further propose table filling based method to identify the hierarchical structure among multiple opinion targets in a document.
As shown in Figure 4(a), we firstly incorporate the flat entity sentiments obtained in Equation (5) the hierarchical structure G. Considering that the sentiment of lower-level entity should be embodied in the upper-level one, as the lower-level entities are part of the uever-level entities, we then introduce
Firstly, we construct a Affiliated Relation Table T. as shown Figure 3(a), based on the extracted

---

Dev
Dataset
#Sentence
#1-T
#2-T
#3-T
#D
#D
Books
690
1682
99
287
197
501
5.97
1465
988
17
Clothing
649
1100
92
150
186
304
3.29
1165
385
4
Restaurant
658
3220
94
527
188
992
7.91
1943
2566
221
Hotel
720
2448
103
315
206
673
4.19
1408
1795
231
News
656
2334
93
644
2599
675
54
PhraseBank
835
1415
119
202
434
1.02
1413
589
49
Table 2: Dataset statistics. #D, #P and #Sentence respectively denote the number of documents, target-sentiment pairs and average number of sentences each domain, #1-T, 42-T, #3-T denote the number of single-level targets, two-level targets and three-level targets respectively.
Algorithm to update the sentiments of entities in the hierarchical structure. Specifically, as shown in line 1, we traverse the hierarchical opinion target structure G' to obtain the path set P from the root node entity to the leaf node entity. For each path Pt, if the sentiment of the upper entity is conflict with that of its lower entity, the sentiment of the upper entity will be updated "Mixed", as shown in lines 2 to 10. Finally, we traverse this structure to output a set of (multi-level) target-sentiment pairs, as shown in Figure 4(b).
affiliated relation prediction are:
7 Le
Σ
pl (o (8)
I
181
C.
Σ
Poffle))ogPP(ffe)))
(9)
T
18
C = Σ
Paul (n)) log P(uiy
(10)
Algorithm Multi-level Entity Sentiment Updat- ing
where ý is the golden one-hot distribution, is the predicted distribution, and and & denote the example set and entity set, respectively.
Input: The predicted flat entity-sentiment pair set S and hierarchical opinion target structure G Output: The updated sentiment set for each entity in G. denoted S
The joint training loss is the sum of three parts:
L L. L. fr.
(11)
1: Traverse G to obtain the path P
Experiments
2: for path P: = (Cu...., Gjs ko 4t) P
do
4.1 Experimental Setup
3: for the upper-level entity e, E p; do
4.1.1 Dataset
4: for the lower-level entity ej+b E Pı do
5: if S(c)) is not equal to S(ejik) (k) then
We evaluated our model on the document-level TSA dataset, which includes product reviews, namely Book, Clothing, Restaurant, Hotel, as well as financial and social media reviews, namely News, PhraseBank. Note that we have slightly up- dated the annotations PhraseBank and News, by dealing with the absence of sentiment annotations for upper-level entities. Table presents detailed statistics for all six domains.
6:
$(e,) Mixed 7: end if
8: end for
9: end for
10: end for
11: return
4.1.2 Baselines and Evaluation Metrics
3.5 Model Training
In addition to the Seq2Seq approach proposed by Luo al. (2022), further set up four strong baseline systems by adapting the newly proposed sentence-level ASBA approaches to document- level TSA.
Our approach a multi-task learning framework of three components. We employ cross-entropy of the ground truth and the prediction as our loss function for each component, and learn them jointly.
The loss functions for 1) flat entity extraction, 2) flat entity sentiment classification, and 3) entity
BART-Exttactinn and T5- 15-Extraction are adapted from the Extraction- seq2seq approach

---

Methods
Books
Clothing
Restaurant
Hotel
News
PhraseBank
Average
Seq2Seq (Luo al.. 2022)
34.76
49.40
19.08
34.17
12.91
55.27
34.27
BART-Extraction
33.83
55.42
33.05
58.90
21.80
63.15
44.36
BART-Paraphrase
32.90
55.18
33.21
59.71
21.47
63.08
44.26
TS-Extraction
32.66
52.49
32.85
57.92
22.31
65.48
43.95
TS- P5-aaaphreee
32.64
53.47
33.36
57.95
22,96
64,85
44.21
Seq2Struct
38.41
57.36
36.41
60.10
23.47
63.18
46.49
Table The main experimental results of our approach and five baselines on the domains. Seq2Seq (Luo al., 2022) represents the sate-of-the-an approach in the document-leve' TSA task. We report the results in their paper. addition, we construct the other four strong baselines as described in Subsection 4.1.2.
sentence-level ABSA (Zhang et al., 2021b), using BART and T5 backbones respectively. BART- Paraphrase and T5-Paarphrase are adapted from the Paraphrase-based seq2seq upproach in sentence- level ABSA (Zhang et al., 2021a), using BART and T5 as backbones respectively.
short document
document
document
70
60
50
40
Following (Luo et al., 2022), we evaluate the document-level TSA task based on the output of set of target-sentiment pairs given the input doc- ument. The precision and recall scores are calcu- lated based on exact match of the predicted target- sentiment pairs and the ground The Fl score is taken as the final evaluation metric.
20
10
Book
Clothing
Restaurant
Hotel
News
Figure 5: The performance of approach three types of document length.
and News, respectively, compared with the best results for all baselines. All the improvements are significant based paired t-test.
4.1.3 Implementation Details
We employ DeBERTa (He et al., 2021) as the backbone encoder of our approach, whose hid- den size 768 and maximum length of the input is 512. With commitment to equitable model param- eters, we have chose 355mmll et al., 2020) the backbone of the baselines designed in paper. During training, the learning rate for fine-tuning the pre-trained language model to 3e-5, other learning rates are set to 5e-5, and the dropout rate 0.1. We set batch size to 8 and training epochs to 30. We save the model parame- ters with the highest Fl value on the validation set. During testing, we report Fl score for each domain that are averaged over five different random seeds.
Furthermore, it can be seen that our approach gains more improvement the Books and Restau- rant domains, which have longer document length and more across-sentence targets. This indicates the strength of our approach in identifying complex opinion targets from long documents.
An exception is that our approach on the Phrase- Bank domain is not optimal (slightly lower than T5 75-Extraction and T5- T5-Paraphrase). According to Table 2, the document length of PhraseBank is the shortest in six domains and its average number of sentences is only 1.02. It is reasonable ap- proach does not show significant advantage this case.
4.2 Main Results
4.3 The Impact of Document Length
In Table 3, we report the results of our approach and five baselines on the six domains. It can be observed that our method outperforms all the base- lines on average F1. Furthermore, in comparison with the four strong baselines designed, our approach can still achieve average Fll score improvement larger than 2.1 %.. Specifically, the improvements are 4.58%, 1.94%, 3.05%, 0.39%, 0.51% % on the Books, Clothing, Restaurant, Hotel
We further investigate the performance our ap- proach on test subsets with different document lengths. Based on the number of sentences in the document, we divide the test set of each domain into subsets of short document (1 or 2 sentences), medium document (3 or 4 sentences), and long document (5 more sentences).
As shown in Figure 5, in the Books, Clothing,

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BARI-Extraction
Seq2Struct
Within-Sentence
Across-Sentence Seq2Str. GCN Diff.
Dataset
Dataset
Table 4: The performance on different levels of targets, Seq2Str. w/o GCN
2-T
3-T
1-T
2-T
3-T
Diff.
Books
47.92
24.68
54.61
35.23
Books
46.60
41.50
5.10,
28.18
19.32
8.86
Clothing
58.02
48.06
61.10
57.70
Clothing
61.12
59.92
1.201
54.70
46.51
8.194
Restaurant
42.24
34.78
19.58
50.98
39.07
24.23
Restaurant
41.76
40.64
1.12.
35.90
32.50
3.40
Hotel
68.54
61.48
42.20
78.57
64.45
45.32
Hotel
69.10
66.14
2.96.
51.28
47.07
4.214
Average
54.18
42.25
30.89
61.31
49.11
34.77
Average
54.64
$2.05
2.594
42.51
36.35
6.16]
Table 6: The performance of our approach with and without the Multi-grain Graphical Model (on opinion target with multi-level entities).
where "-" means that the corresponding domain does not have corresponding targets.
Dataset
Seq2Struct
w/o GCN
Diff.
4.5
The Effect of the Multi-grain Graphical Model
Books
38.41
36,63
1.78
Clothing
57.36
56.74
0.62
Restaurant
36.41
35.84
0.58
In this part, we conduct ablation study on GCN to examine the effect of the multi-grain graphical model.
Hotel
60.10
59.32
0.781
News
23.47
22.39
1.08]
PhraseBank
63.18
62.98
0.201
Average 46.49
45.65
0.841
Firstly, in Table 5 we report the performance of our approach with and without the Multi Multi-grain Graphical Model on the entire test set. shows that removing the multi-grain graphical model from our approach causes average of 0.84% decrease across six domains. The decrease is significant according to paired t-test.
Table 5:
The performance of our approach with and without the Multi-grain Graphical Model (on the entire test set).
Restaurant, and Hotel domains, the corresponding Fl scores consistently decrease when the document length increases. This illustrates the challenge of the Document-level TSA task, the longer the doc- ument length, the more difficult is to accurately extract the multi-level opinion target and sentiment from it.
Secondly, to analyze the effect of GCN on opin- ion target with multi-level entities, we divide multi- level opinion targets in the test set into a Within- Sentence subset and Across-Sentence subset, where Within-Sentence denotes that multi-level en- tities are within sentence, and Across-Sentence denotes that across multiple sentences. The results in Table 6 shows that removing GCN causes a 2.59% and 16% drop in Within-Sentence and Prross-Sentence respectively. It confirms the ef- fectiveness of the advantage of multi-grain graph- ical model in capturing cies among affiliated entities, especially the across- sentence ones.
4.4 The Impact of Levels Opinion Target
Table 4 reports the performance of our approach in extracting opinion targets with different levels. The same as Table 2, 1-T, 2-T, and 3-T represent the number of levels in an opinion target. We report the results on test subsets divided into 1-T, 2-T, and 3-T, respectively. We have not reported the results on News and PhraseBank they contain relatively fewer multi-level opinion targets.
dependen-
Finally, in Figure we display the performance of our approach with and without GCN as the num-
can be seen that, when the number of levels increases, the Fl score decreases significantly. indicates the challenge of the Document-level TSA task from another aspect. The more levels of en- tities the opinion target has, the more difficult the task will be.
60
Our Approach
w/o GCN
50
F1(%)
30
In comparison with the BART-Exttaction seq2seq method, our approach achieves consistent and stable improvements at different levels. The av- erage improvements are 7. 13%, 6.86% and 3.88% on 1-T, 2-T, and 3-T, respectively.
Number of sentences in the document
Figure 6: The average performance of six domains different document lengths.

---

Dataset
Soq2Struet
Seq2Struct
Diff.
Furthermore, Seq2Struct can accurately predict distant hicrarchical entities, For example, in doc- ument 5, Seq2Struct predicts the pair ("Alexander Cipher-character", Positive), where "Alexander Ci- pher" and "character" are separated by four long sentences.
Books
38.41
37.08
1.33
Clothing
57.36
55,97
1.39
Restaurant
36.41
35.34
1.07_
Hotel
60.10
59.58
0.52.
News
23.47
22.85
0.62.
PhraseBank
63.18
63.91
0.731
Average
46.49
45.79
0.70
In addition, Seq2Struct can recall more entities. For example, in document 4, the predicted entities of Seq2Struct are "Heel color", "Front toes area", and "appearance".
Table 7:
The performance comparison between Seq2Struct and Seq2Structuarten: in document-level TSA task.
ber of sentences in the document increases. It can observed that the performance of our approach with GCN is insistently higher than that without GCN. As the number of sentences increases, the improve- ment becomes larger in general. Both suggest the effectiveness of the multi-grain graphical model of approach in modeling long documents.
Related Work
ABSA is a broad research area which includes var- ious tasks. Schouten and Frasincar (2016); Zhang et al. (2022a) provided comprehensive survey to these subtasks. In this paper, due to space limita- tion, only review the related tasks.
End-to-end ABSA, the task of joint aspect ex- traction and aspect-basee sentiment classification (also called targeted sentiment analysis in some references), has received wide attention (Mitchell et al., 2013; Zhang et al., 2015; Poria et al., 2016; Hu et al., 2019; Li et al., 2019a,b; Jiang et al., 2019; Chen and Qian, 2020; Yu et al., 2021b; Hamborg and Donnay, 2021). However, all these studies were performed at the sentence level. this paper. we focused on targeted sentiment analysis at the document level. Unlike the opinion target at the sentence level, which is normally entity or as- pect, the opinion target studied in this work often contains multi-level entities.
4.6 Discussion on the Place of Sentiment Classification
In our approach, we perform sentiment classifica- tion at the stage of flat entity extraction. A corre- sponding question is then raised: Which place is the most suitable for sentiment classification?
To answer this question, we design a variant of our approach Seq2Structvariantt which pre- diets the sentiment after obtaining the hierarchi- cal opinion target structure, and report its perfor- mance in Table It can be seen that the Fl score of Seq2Structyariant has an average decrease of 0.7%. We speculate that the possible reason that the opinion expression often appears near the entity, and has little relation with the structure of opinion targets. It may hence be more effective to perform sentiment classification towards the flat entities.
Among massive ABSA studies, only a few fo- cused on the document level. Titov and McDon- ald (2008) proposed a statistical model to extract textual evidence for aspect category and predict sentiment rating for different categories in review document. Lei et al. (2016) proposed an encoder- generator framework extract rationales for aspect category and predict aspect category sentiment rat- ing. Yin al. (2017) modelled aspect category sen- timent rating as a machine comprehension problem. Li et al. (2018) designed a hierarchical network for aspect category sentiment considering both user preference and overall rating. Wang et al. (2019) proposed a hierarchical reinforcement learning ap- proach interpretably predict aspect category sen- timent rating. However, all these studies focused on identifying the sentiments of aspect categories in a document. By contrast, we extract the explicit opinion entity terms in a document and organize them in a hierarchical structure.
4.7
Case Study
In Figure 7, conduct the case study by display- ing the outputs of our approach (Seq2Struct) and Seq2Seq. In comparison, Seq2Struct can more ex- plicitly display the hierarchical structure opinion target in a document and more accurately predict the corresponding sentiments, across different doc- ument length. For example, in short document 1, Seq2Struct can predict the upper entity "Dan- skin" of "quality". In document 2, which is slightly longer, Seq2Struct can predict what Seq2Seq can- not predict ("read- gread-personalities Positive). In the longer document 3, Seq2Seq predicts the wrong pair ("rights-size size B", Negative), while Seq2Struct predicts them all correctly.

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Example Danskin always a great choice. Good quality.
Example Extremely uncomfortable. and heel to large liner support all Heel color and appearance awful Not complimentary the shoe
narrow
Seq2Seq [K): (quality, Positive)
Seq2Struct (<]:
Danskin
Positive
Seq2Seq (*); (shoe-heel area, Negative) (shoe, Negative) (shoe- color, Negative)
quality
Positive
Seq2Struct (V|:
Example 2: Great read on how humans have understood and treated cancer from ancient Egypt to 2010 or so. Interesting stories and personalities.
shoe
Negative
Seq2Seq (x): (read-stories, Positive) (read, Positive)
Heel color
appearance
heel area
Front toes area
Seq2Struct (+):
read
Positive
Negative
Negative
Negative
Negative
stories
personalities
Example 5: Adams creates new thriller character destined to a long-running series best sellers. Daniel Knox knock- around archacologist Egypt with a history of interesting relationships with his brethren and sponsors The supervisor Alexander Cipher adventure The beautiful. This is excellent, well-crafted story. It cries out for a sequel in the adventures of Daniel Knox.
Positive
Positive
my tights EXTREMELY SMALL could pass my thighs II am 5' and 128lbs, read the reviews and the sizing chart and thought that size B would be ideal but does not fit The color was a perfect match but can not wear these tights anywhere because the incorrect sizing guidelines.
Example 3:1 am lamaica and excited today. , HOWEVER,
Seq2Seq (xe): (Alexander Cipher-story, Positive) (Alexander Cipher, Pasitive) (adventure, Positive) (Alexander Cipher-story-Alexndee Cipher, Positive)
Seq2Seq (x): (lights-color, Positive) (tights, Mixed] (tights-size B, Negative)
Seq2Struct (V))
Seq2Struct (+):
tights
Mixed
Alexander Cipher
Mixed
color
sizing guidelines
story
character
Positive
Nogative
Positive
Negative
Figure 7: The case study by comparing the Seq2Seq approach and our Seq2Struct approach.
From the perspective of methodology, graph Convolutional network (GCN) has been widely used in ABSA (Zhang et al., 2019; Sun et al., 2019;
extract the opinion targets consisting of multi- level entities from review document and pre-
diet the corresponding sentiments. Different from the existing Seq2Seq mythology, we propose a Sequence-to-Strreuure (Seq2Struct) approach to address this task, to model the hierarchical struc- ture among multiple opinion targets and capture the long-distance dependencies among affiliated entities. Experiments have verified the advantages of our Seq2Struct approach in more accurately ex- tracting multi-level opinion targets and predicting their sentiments, and more explicitly displaying the hierarchical structure of the opinion targets in a document.
Cai et al., 2020; Liang et al., 2021; Hou et al., 2021;
Tian et al., ;0 Zhang et al., 2022b; Chen et al., 2022). However, most of these studies employed GCN to model the relations between different enti- ties within a single sentence. Different from that, in this work multi-grain graphical model is pro- posed learn the affiliated relations among entities across multiple sentences in a document.
Table filling, the method to predict the relation between any two targets by filling a table, has re- ceived much attention on entity and relation ex- traction task (Miwa and Sasaki, 2014; Gupta et al., 2016; Wang and Lu, 2020) and open information extraction task (Yu et al., 2021a). In the ABSA task, Wu al. (2020); Jing al. (2021) proposed to use table filling to tag aspect terms, opinion terms and the relations between them. In contrast, in this work we use table filling to model the affiliated relation between two entities.
Limitations
This paper focuses on addressing the task of document-level TSA, which, along with its dataset, has been recently introduced. Our approach is pri- marily designed to tackle the challenge of extract- ing the affiliated relations among entities over in-domain setting. Nevertheless, this task remains challenges, particularly in aspects such as long doc- ument encoding, coreference problem, and open- domain setting. We welcome more researchers to explore this task.
6 Conclusion
this work, we focus on a document-level ABSA task, called document-level TSA, which aims to

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Ethics Statement
Technical Papers, December 11-16, 2016, Osaka, Japan, pages 2537-2547. ACL.
We conduct experiments on the publicly avail- able document-level TSA dataset, which includes Books, Clothing, Restaurant, Hotel, News and PhraseBank. The dataset do not share personal information and do not contain sensitive content that can be harmful to any individual or community.
Felix Hamborg and Karsten Donnay, 2021. Newsmtsc: A dataset for paaali--ttare--depeddeee sentiment clas- sification in political news articles. In Proceedings of the 16th Conference of the European Chapter of the Association for Computationall Linguistics; Main Vol- ume, EACL 2021, Online, April 19 23, 2021, pages 1663-1675. Association for Computational Linguis- tics.
Acknowledgments
Pengeheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021. Deberta: decoding-enhanced bert with disentangled attention. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenRe- view.net.
The authors would like to 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).
Xiaochen Hou, Peng Qi, Guangtao Wang. Rex Ying. Jing Huang, Xiaodong He, and Bowen Zhou. 2021. Graph ensemble learning over multiple dependency trees for aspect-level sentiment classification. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computa- fional Linguisties: Humun Language Technologies, NAACI-IILT 2021, Online, June 6-11, 1, 2021, pages 2884 2894. Association for Computational Linguis- tics.
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Reviews:
[{"review_id": "Ck8tus5bW5", "reviewer": "Reviewer_F76R", "summary": "", "questions": "Apart from the weaknesses mentioned earlier, there are additional questions:\n\nA) In the abstract, when referring to \"long-distance dependencies\", does it exclusively pertain to the relationships between lower-level and upper-level entities, without considering relationships among entities at the same level?\n\nB) Why does Table 1 only present statistics for opinion targets in four domains?\n\nC) In the Introduction and Related Work sections, you mentioned that sentence-level Aspect-Based Sentiment Analysis (ABSA) usually involves only a single entity. Do you think your understanding of relevant literature might be limited? Have you considered that both sentence-level and document-level ABSA could involve flat or hierarchical targets? Would your proposed method still be effective in these scenarios?\n\nD) If an overall review of the upper entity is not present, the hierarchical structure proposed in the paper cannot be established. If such a review does exist, it inherently carries polarity. Integrating the sentiments of lower entities results in a more ambiguous \"mixed\" sentiment. I believe that directly analyzing the context of the upper entity would best reflect the primary sentiment expressed by the reviewer. How do the authors address this concern?\n\nE) Is the methodology introduced in this paper aimed solely at improving sentiment classification outcomes rather than the extraction process itself? If this is the case, should the paper present results for pure sentiment classification to validate the effectiveness of the approach?\n\nF) Are the sentiment classification results presented in Table 7 for a standalone classification task, or are they part of a joint task involving both extraction and sentiment classification? If it is a joint Task-Based Sentiment Analysis (TSA) task, please correct the terminology accordingly.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the time and effort you dedicated to reviewing our paper. Your insightful comments and feedback have been invaluable in enhancing the quality of our work. We have carefully considered your comments and suggestions, and we are grateful for the opportunity to address them in this response.\n\n**Response to Reason To Reject #1:**\n\nThe phenomenon you mentioned above does indeed exist, but our approach supports not only the modeling of affiliated relation between entities but also the modeling of flat entities. In some reviews that only contain flat entities, our approach does not construct any entity as a parent node of another entity. For instance, the example: \"service is quick and friendly, ... , and food is delicious.\" Our model outputs a graph structure, but the nodes \"service\" and \"food\" will not be connected, meaning there is no parent-child relationship between them. This adaptability underscores the effectiveness of our method in handling diverse document types, even those with flat entity relations.\n\n**Response to Reason To Reject #2:**\n\nThanks for your careful review! In Aspect-Based Sentiment Analysis (ABSA), it's common to refer to datasets from different domains as multiple datasets. For instance, in SemEval, datasets encompassing two domains like Laptop and Restaurant within a single dataset are often regarded as two distinct datasets. Your suggestion is indeed more precise, and we will incorporate your advice in the revised version of our paper. We have already reached out to the author Luo[1] and are working to provide a publicly accessible data access link as soon as possible.\n\n[1] Luo, Yun, et al. \"Challenges for open-domain targeted sentiment analysis.\"\n\n**Response to Reason To Reject #3:**\n\nOur paper addresses an emerging task with relatively limited existing methods. In our paper, we have designed four strong baselines based on the sequence-to-sequence architectures to tackle this task. We agree with your comment, regarding the diversity issue in comparing systems. As a response, we conducted additional experiments using BERT and RoBERTa as backbones and present the results below.\n\n|      Domain       | Books | Clothing | Restaurant | Hotel | News  | PhraseBank | Average |\n| :---------------: | :---: | :------: | :--------: | :---: | :---: | :--------: | :-----: |\n|  Ours_BERT (F1)   | 34.73 |  49.97   |   32.91    | 52.40 | 18.74 |   58.01    |  41.32  |\n| Ours_RoBERTa (F1) | 33.84 |  53.16   |   33.32    | 54.57 | 21.24 |   59.89    |  42.67  |\n|     Ours (F1)     | 38.41 |  57.36   |   36.41    | 60.10 | 23.47 |   63.18    |  46.49  |\n\n**Response to Reason To Reject #4:**\n\nWhile our paper lacks an explicit \"Ablation Study\" section, we have effectively performed ablation-like investigations in Sections 4.5 and 4.6. Moreover, pertinent discussions are presented throughout the paper. For instance, Section 4.3 delves into the impact of document length on model performance, while Section 4.4 juxtaposes baseline and our approach performances across different opinion target levels, affirming our method's efficacy in predicting affiliated entity relations. To address this, the revised paper will comprehensively and lucidly detail the ablation study. For concerns regarding the prior point (c), please refer to Response to Reason To Reject #3.\n\n**Response to Question #1:**\n\nYes, \"long-distance dependencies\" specifically refer to the dependencies between entities with affiliated relations that are distant from each other within the document. Thank you for pointing out this issue, and we will include more specific descriptions in the revised version of the paper.\n\n**Response to Question #2:**\n\nWe will include the statistical results for these two datasets in the revised version of the paper.\n\n**Response to Question #3:**\n\nSentence-level ABSA indeed also involves such affiliated relations, and our method is equally applicable. However, currently, there is a lack of affiliated relations between entities annotation at the sentence level. We will consider conducting such work at the sentence level in our future research endeavors.\n\n**Response to Question #4:**\n\nOur method not only identifies the sentiments of opinion targets with affiliated relations but also recognizes the sentiments of targets without affiliated relations. When a target lacks affiliated relations, its sentiment is related to that of a flat entity. However, if affiliated relations exist, it indicates that the opinion target encompasses multiple parts or entities. Consequently, integrating the sentiment of each lower-level entity becomes crucial. This contributes to a more comprehensive reflection of the sentiment of opinion targets that encompass multiple entities/aspects.\n\n**Response to Question #5:**\n\nThe primary focus of our paper is to extract those complex opinion targets with affiliated relations, while sentiment classification serves as a secondary focus built upon this foundation.\n\n**Response to Question #6:**\n\nThank you very much for pointing out this issue. The results presented in Table 7 showcase the outcomes of the joint extraction of opinion targets and sentiment labels. We will make the necessary correction to the legend of Table 7 in the final version.\n\n**Response to Missing References:**\n\nThank you for your suggestion. We appreciate your feedback regarding the missing references you mentioned, and we will ensure to include them in the final version.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "A) This work has taken into account the interplay among various aspects discussed in the paper, which indeed holds the potential to benefit the TSA task.\n\nB) This research has provided valuable insights into utilizing graph networks for modeling documents in the context of TSA tasks.\n\nC) The attention given by this work to the document-level TSA task is noteworthy.", "reasons_to_reject": "A) The author's understanding of the research problem appears to be unclear, which raises concerns about the feasibility of the proposed solutions. This paper is based on the assumption that there are affiliated relations among targets mentioned in lengthy comments (documents). However, in reality, there is a possibility that no such affiliations exist among the targets. This lack of affiliation would hinder the construction of the hierarchical structure proposed in this paper. For instance, in review documents that directly comment on aspects like \"service\" or \"food\" of a restaurant or compare two restaurants, the relationships among targets might remain flat, making it impractical to establish a single target as a parent node.\n\nB) The paper exhibits two potential issues related to the experimental dataset. Firstly, the paper claims to have conducted experiments on six distinct datasets, whereas they actually stems from a single dataset across different domains (Luo et al., 2022). Relying solely on a single dataset for experimentation might inadequately validate the effectiveness of the proposed methodology. Furthermore, the URL for this dataset is currently inaccessible, potentially posing challenges for reproducing the results presented in this paper.\n\nC) The baselines in this paper lack representativeness and diversity. The paper considers baseline Seq2Seq as the state-of-the-art (SOTA) method for this task, whereas it actually serves as a benchmark model for the dataset. The other four baselines comprise variations of two methods using different pre-trained models as backbones, which differ from the backbone used in this paper. As a result, the potential influence of the backbone on experimental results cannot be ruled out, suggesting potential shortcomings in the experimental design.\n\nD) The experimental analyses of this work lack a comprehensive ablation study. The analyses in sections 4.3 and 4.4 provide little assistance in demonstrating the effectiveness of the proposed method. Considering the earlier point (c), the experimental design of this paper may be somewhat inadequate.", "questions_to_authors": "Apart from the weaknesses mentioned earlier, there are additional questions:\n\nA) In the abstract, when referring to \"long-distance dependencies\", does it exclusively pertain to the relationships between lower-level and upper-level entities, without considering relationships among entities at the same level?\n\nB) Why does Table 1 only present statistics for opinion targets in four domains?\n\nC) In the Introduction and Related Work sections, you mentioned that sentence-level Aspect-Based Sentiment Analysis (ABSA) usually involves only a single entity. Do you think your understanding of relevant literature might be limited? Have you considered that both sentence-level and document-level ABSA could involve flat or hierarchical targets? Would your proposed method still be effective in these scenarios?\n\nD) If an overall review of the upper entity is not present, the hierarchical structure proposed in the paper cannot be established. If such a review does exist, it inherently carries polarity. Integrating the sentiments of lower entities results in a more ambiguous \"mixed\" sentiment. I believe that directly analyzing the context of the upper entity would best reflect the primary sentiment expressed by the reviewer. How do the authors address this concern?\n\nE) Is the methodology introduced in this paper aimed solely at improving sentiment classification outcomes rather than the extraction process itself? If this is the case, should the paper present results for pure sentiment classification to validate the effectiveness of the approach?\n\nF) Are the sentiment classification results presented in Table 7 for a standalone classification task, or are they part of a joint task involving both extraction and sentiment classification? If it is a joint Task-Based Sentiment Analysis (TSA) task, please correct the terminology accordingly.", "paper_topic_and_main_contributions": "This work attempts to address the task of Target Sentiment Analysis (TSA) for long texts. Compared to sentence-level TSA, there is limited research on this task. The paper argues that there are affiliated relationships among targets (also referred to as entities or aspects) in documents such as comments, and capturing such affiliations would be beneficial for solving the TSA task. However, existing sequence-to-sequence frameworks are incapable of modeling these dependencies. Therefore, the authors propose a Sequence-to-Structure approach. Firstly, they employ DeBERTa to extract entity-sentiment pairs. Subsequently, they construct a graphical model and a hierarchical structure of targets based on affiliated relations between entities. Lastly, sentiments of entities derived in the first step are integrated based on the hierarchical structure.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "MAMS for multi-aspect sentence-level TSA;\nASAP for document-level TSA in review domain;\nNewsMTSC for document-level TSA in news domain;"}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "a) A comma should be added to formula 4.\nb) In line 87, the adversative relation between \"simple and straightforward\" and \"imperfect\" is weak. After all, in most cases, simple methods are often flawed.\nc) \"If\" in line 287 should be lowercase.\nd) In line 349, \"the other\" means the other between two parts, where \"the\" should be dropped.\ne) A conjunction should be added before \"our approach\" in the conclusion such as \"and\"."}}, {"review_id": "0Ve8xdqfoA", "reviewer": "Reviewer_moT5", "summary": "", "questions": "1. The motivation is unclear, why the star rating labels can be assumed to be a “coarse-grained synthesis” of aspect ratings in the review?  Especially for the Sequence-to-Structure representation.\n2. What are the advantage of your method compared to the sequence-to-sequence methods? In my opinion, your method also directly identifies the entities and aspects in the document, and then predicts their sentimental polarity. The part that truly reflects the hierarchical structure is more like a post-processing of the predicted results instead of incorporating hierarchical structure to help predict.\n3. In section4.2, why your method does not show a significant advantage in shorter document length?\n4. Lack of detailed examples to analyze long-distance dependencies, and the advantages of traditional sequence-to-sequence framework.\n5. The comparison experiment lacks credibility without classic benchmark model, but only selects the conventional Seq2Seq, Bart, T5. \n6. The selection of data sets does not have diversity, that is, the lack of convincing conclusions for hierarchical modeling, is it a case of the reviewer data set, or can it be generalized to other scenarios.\n7. The usage of the expression“predict their sentiments” is inaccurate in the abstract", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Response to Question #1:**\n\nThank you for your comment. We are glad to clarify our motivation. Our motivation stems from two aspects: 1) addressing the limitation of sequence-to-sequence methods in explicitly modeling the hierarchical structure among multiple opinion targets in a document; 2) aiming to tackle the shortcomings of the previous method in modeling long-distance dependencies among entities across sentences.\n\n**Response to Question #2:**\n\nWe apologize for any confusion, but we need to clarify that the hierarchical structure is not a post-processing step. During the training phase, it actively participates in the model training process, as outlined in Section 3.3 of the paper. In the testing phase, the model leverages the trained parameters to explicitly output the hierarchical structure, which in turn aids in updating the sentiments of parent nodes.\n\n**Response to Question #3:**\n\nOur approach focuses on better modeling affiliated relations and long-distance dependencies among entities. In the PhraseBank domain, where the average sentence length is 1.02, the occurrence of entities with affiliated relations and long-distance dependencies is limited. As a consequence, our method may not exhibit a notable advantage within the PhraseBank domain. We will provide a clearer explanation of this issue in the revised version of the paper.\n\n**Response to Question #4:**\n\nWe have showcased the advantages of our approach in handling long-distance dependencies in Appendix A.1 Case Study. Specifically, in Document 3, for the entities \"tights\" and \"sizing guidelines\" located in the first and fourth sentences respectively, our method demonstrates superior accuracy in predicting their hierarchical relationship compared to the baseline.\n\n**Response to Question #5:**\n\nOur paper addresses an emerging task with relatively limited existing methods. In our paper, we have designed four strong baselines based on the sequence-to-sequence architectures, further providing additional validation for our motivation.\n\n**Response to Question #6:**\n\nThank you for your comment. It is indeed highly valuable. This concern isn't only specific to our work but also reflects a challenge faced by the entire ABSA field. Compared to traditional ABSA research that predominantly focuses on just the laptop and restaurant domains within SemEval, our work encompasses six distinct domains, significantly broadening the scope of investigation. In our future works, we plan to further increase the inclusion of diverse domains to enhance dataset diversity.\n\n**Response to Question #7:**\n\nWe will make the expression clearer in the revised version of the 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": "The text is fluently written and easy to understand.", "reasons_to_reject": "The method proposed in this paper still has many incomprehensible places. Please see the following questions.", "questions_to_authors": "1. The motivation is unclear, why the star rating labels can be assumed to be a “coarse-grained synthesis” of aspect ratings in the review?  Especially for the Sequence-to-Structure representation.\n2. What are the advantage of your method compared to the sequence-to-sequence methods? In my opinion, your method also directly identifies the entities and aspects in the document, and then predicts their sentimental polarity. The part that truly reflects the hierarchical structure is more like a post-processing of the predicted results instead of incorporating hierarchical structure to help predict.\n3. In section4.2, why your method does not show a significant advantage in shorter document length?\n4. Lack of detailed examples to analyze long-distance dependencies, and the advantages of traditional sequence-to-sequence framework.\n5. The comparison experiment lacks credibility without classic benchmark model, but only selects the conventional Seq2Seq, Bart, T5. \n6. The selection of data sets does not have diversity, that is, the lack of convincing conclusions for hierarchical modeling, is it a case of the reviewer data set, or can it be generalized to other scenarios.\n7. The usage of the expression“predict their sentiments” is inaccurate in the abstract", "paper_topic_and_main_contributions": "This paper focuses on document-level targeted sentiment analysis, aiming to extract the opinion targets consisting of multi-level entities from a review document and predict their sentiments. To explicitly model the hierarchical structure and long-distance dependencies among multiple opinion, this paper proposes a Sequence-to-Structure approach. Experimental results demonstrate its effectiveness.", "excitement": 2, "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": "250 a Affiliated Relation Table\n287 In short, If the"}, "extra_text": {}}, {"review_id": "cCaf96wlNS", "reviewer": "Reviewer_GrSt", "summary": "", "questions": "In your Hierarchical Opinion Target Structure, the identification texts can contain nested or overlapping opinion targets, where one target might encompass another. how do you solve/reduce this?\n", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Response to Reason To Reject #1:**\n\nThank you for your question. This issue is not limited to our tasks, but is even a common challenge in the entire ABSA field. For the out of distribution data, the generalization performance of the model generally decreases. Certainly, exploring research concerning cross-domain sentiment analysis has value and significance. In the future, we will consider conducting research on the cross-domain document level TSA task.\n\n**Response to Question #1:**\n\nThank you for your insightful comment. We evaluated the nested entity frequency across four domain datasets, with results presented in the table below. The results indicate that instances of nested or overlapping targets are indeed infrequent. In the ABSA tasks, where the primary goal is to identify sentiments associated with distinct entities or aspects within the text, and these entities or aspects typically exist independently. On the other hand, we could also explore methodologies that support nested entity extraction, such as the approach mentioned in Chen[1].\n\n| Domain | Books | Clothing | Restaurant | Hotel |\n| :----: | :---: | :------: | :--------: | :---: |\n| Ratio  | 0.05  |   0.02   |    0.08    | 0.04  |\n\n[1] Chen, Shaowei, et al. \"Bidirectional machine reading comprehension for aspect sentiment triplet extraction.\"", "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": " Document-level TSA involves analyzing sentiment across multiple sentences or segments in a document. A Sequence-to-Structure approach can capture the hierarchical structure of a document, understanding how sentences relate to each other and contribute to the overall sentiment such as shown by Affiliated Relation Table in Figure 3 in the paper.", "reasons_to_reject": "The approach might struggle to generalize to diverse document types, lengths, and domains that were not well-represented in the training data. This could result in reduced performance on out-of-distribution data.", "questions_to_authors": "In your Hierarchical Opinion Target Structure, the identification texts can contain nested or overlapping opinion targets, where one target might encompass another. how do you solve/reduce this?\n", "paper_topic_and_main_contributions": "The authors proposed a  Sequence-to-Structure approach to address the task of document-level TSA task. The authors also construct four s baselines adapted from the  proposed seq2seq methods with different pre trained models and achieve an improvement.", "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": "QWQDoa5U0p", "reviewer": "Reviewer_vt9J", "summary": "", "questions": "The example document is not very long compared with other traditional Targeted Sentiment Analysis(TSA) or Aspect-based Sentiment Analysis(ABSA). Can models in the ABSA or TSA perform equally well on document level TSA? Should some of these models be added as baselines?\nWhat are the underlying causes for the performance difference between Book/Clothing/Restaurant and so on? Field difference might be the superficial reason.", "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": "**Response to Reason To Reject #1 & Question #1:**\n\nThanks for your professional review and recognition of our work! \n\na) In order to enhance the clarity of the motivational narrative without occupying excessive space, we opted for a relatively concise example. We greatly appreciate your suggestion, and in the revised version of the paper, we will provide more suitable and more extensive examples.\n\nb) Directly applying these baselines is unable to capture the affiliated relations between entities. Thank you for your suggestion, and we will consider adapting these baseline models to better align with our task and utilize them as the benchmark models in our paper.\n\nc) Due to space limitations, the Case Study demonstrating the advantages of our approach over baselines has been included in Appendix A.1. This section highlights benefits in terms of document length, long-distance entity dependencies, and entity recall rates. \n\nd) The variations in performance across domains can be attributed to factors such as diverse document lengths, complexities of affiliated relations among entities, varying sentiment distributions, and entity diversity. We will conduct more in-depth analysis and discussion on these aspects in the revised version of the paper. Thank you for your excellent suggestion.", "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 proposes a Sequence-to-Structure approach for document-level targeted sentiment analysis, which explicitly models the hierarchical structure among multiple opinion targets in a document.\n2. The paper introduces a multi-grain graphical model to learn entity representations, capturing long-distance dependencies among entities across sentences.\n3.  The paper evaluates the proposed approach on six datasets and provides detailed performance analysis.", "reasons_to_reject": "The example document is not very long compared with other traditional Targeted Sentiment Analysis(TSA) or Aspect-based Sentiment Analysis(ABSA). Can models in the ABSA or TSA perform equally well on document level TSA? Should some of these models be added as baselines?\nThere is no case study to help the readers better understand the advantages and disadvantages of the model.", "questions_to_authors": "The example document is not very long compared with other traditional Targeted Sentiment Analysis(TSA) or Aspect-based Sentiment Analysis(ABSA). Can models in the ABSA or TSA perform equally well on document level TSA? Should some of these models be added as baselines?\nWhat are the underlying causes for the performance difference between Book/Clothing/Restaurant and so on? Field difference might be the superficial reason.", "paper_topic_and_main_contributions": "The paper proposes a Sequence-to-Structure approach for document-level targeted sentiment analysis (TSA). Previous studies have focused on sentence-level ABSA, while document-level ABSA has not received enough attention. The proposed approach aims to extract opinion targets consisting of multi-level entities from a review document and predict their sentiments. The approach includes four main steps: flat entity extraction and sentiment classification, multi-grain graphical model, hierarchical opinion target structure identification, and target-sentiment pair parsing. Experimental results show that the proposed approach outperforms the state-of-the-art methods.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "b6lR5uKc20", "reviewer": "Reviewer_7rjQ", "summary": "", "questions": "1. The granularity of extracting all entity terms seems very detailed. Is this practical for real applications? \n2. How does performance compare on informal texts like social media data versus the more structured review dataset used? Does this approach generalize as well?\n3. Is the inference process very slow given the iterative graph computations? How might this be optimized for practical use cases?", "limitations": "", "rating": 2, "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": "Thank you very much for reviewing our paper. We have taken your comments and feedback into careful consideration and addressed them in this response.\n\n**Response to Question #1 & Reason To Reject #3 :**\n\nCiting the definition of opinion targets provided by Liu[1], it encompasses entities with nested relations and/or their different aspects. From an academic perspective, this not only offers a more precise definition for opinion targets within text reviews but also holds significant practical value. For instance, a customer expressing negative sentiment towards the price of sushi at one restaurant while conveying positive sentiment towards the variety of pasta dishes at another restaurant. This finer granularity and enhanced accuracy in identification contribute to a business's ability to refine specific attributes of their products. This achievement surpasses the capabilities of merely extracting flat entities. Overall, businesses can gain a more comprehensive understanding of their product strengths and areas for improvement, enabling more targeted product enhancement, selection, and recommendation strategies.\n\n[1] Liu, Bing. Sentiment analysis and opinion mining.\n\n**Response to Question #2:**\n\nIn the paper, the data for the Books, Clothing, and Restaurant domains are sourced from Amazon and Yelp social media platforms. These datasets are manually annotated informal texts. Our model has been thoroughly tested on datasets from six diverse domains, demonstrating its strong adaptability.\n\n**Response to Question #3:**\n\nWe evaluated the time consumption during inference for both our model (encoder) and the traditional sequence-to-sequence model (encoder-decoder). For the Restaurant domain, the inference time of the sequence-to-sequence model is 122.5s, while our model is only 5.64s. Intuitively, our method requires less time compared to the sequence-to-sequence model. Furthermore, in practical applications, our approach can leverage parallel computation to accelerate processing speed, offering an advantage over autoregressive methods.", "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. It tackles an important but understudied problem in NLP - document-level targeted sentiment analysis. Most prior work has focused on sentence-level, so this addresses a clear gap.\n2. Technically, the multi-grain graphical model using GCNs is useful for capturing long-range dependencies between entities.\n3. The results demonstrate very strong improvements over all baseline methods across multiple datasets. This shows the real-world impact of the solutions proposed.", "reasons_to_reject": "1. The core technique of using graph convolutional networks has been applied before in NLP, so the multi-grain GCN itself is more of an incremental adaptation rather than a significant innovation. Meanwhile, table filling approaches have also been explored for relation extraction tasks, so its application here for hierarchical structure identification is not entirely new either.\n2. While performance gains are shown, it's unclear if these will sustain on noisy, informal textual data or when scaling up to massive documents. The sophiscated pipeline nature of the approach may limit generalizability.\n3. From the application side, no user studies or extrinsic evaluations are done to measure how useful the fine-grained output would be for any practical system or real users.", "questions_to_authors": "1. The granularity of extracting all entity terms seems very detailed. Is this practical for real applications? \n2. How does performance compare on informal texts like social media data versus the more structured review dataset used? Does this approach generalize as well?\n3. Is the inference process very slow given the iterative graph computations? How might this be optimized for practical use cases?", "paper_topic_and_main_contributions": "1. The paper proposes a new Sequence-to-Structure approach to model the hierarchical structure of opinion targets in a document, in contrast to previous sequence-to-sequence methods.\n2. It introduces a multi-grain graphical model using graph convolutional networks to better learn semantic relations between entities across sentences. It employs a table filling approach to identify the hierarchical affiliation relations among entities.\n3. Experiments on 6 SOTA benchmark datasets highlight the effectiveness of the proposed approach. Other experiments also provide extensive analysis and discussions on the impact of document length and levels of opinion targets.", "excitement": 2, "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: This paper introduces a Sequence-to-Structure approach for document-level targeted sentiment analysis (TSA), addressing the challenge of multi-level entity extraction and sentiment prediction in review documents. They employ DeBERTa to extract entity-sentiment pairs, construct a graphical model capturing semantic relations between entities, and identify hierarchical affiliations among entities. Experimental results demonstrate the approach's effectiveness, outperforming existing methods on multiple benchmark datasets.

Reasons to Accept:
- Addresses the underexplored area of document-level targeted sentiment analysis.
- Introduces a technically valuable multi-grain graphical model using GCNs to capture entity dependencies.
- Demonstrates substantial performance improvements across multiple datasets, showing real-world impact.
-Proposes a Sequence-to-Structure approach for modeling hierarchical structures among opinion targets.
- Utilizes a multi-grain graphical model to capture long-distance dependencies among entities.
- Evaluation of the approach on six datasets with detailed analysis.
- Effectively addresses document-level TSA, considering the interplay of sentences and document structure.
- Well-written and comprehensible text.

Reasons to Reject:
- Limited representation of longer documents in experiments, with unclear performance on typical document-level TSA scenarios.
- Lack of groundbreaking innovation in the core techniques used.
- Uncertainty about performance on noisy or massive documents and concerns about limited generalizability.
- Absence of user studies to assess practical usefulness.
- Potential difficulties in generalization to diverse document types and domains.
- Issues with dataset selection and potential challenges in data accessibility.
- Baseline models lacking diversity and potential issues with the experimental design.
Decision: Accept-Findings