Cross-Document Event Coreference Resolution on Discourse Structure
Authors: Xinyu Chen
Sheng Xu
PEIFENG LI
Qiaoming Zhu
Venue: EMNLP 2023 Main (2023)
Keywords: Event Coreference Resolution
Discourse Structure
Shortest Dependency Path

Abstract: Cross-document event coreference resolution (CD-ECR) is a task of clustering event mentions across multiple documents that refer to the same real-world events. Previous studies usually model the CD-ECR task as a pairwise similarity comparison problem by using different event mention features, and consider the highly similar event mention pairs in the same  cluster as coreferent. In general, most of them only consider the local context of event mentions and ignore their implicit global information, thus failing to capture the interactions of long-distance event mentions. To address the above issue, we regard discourse structure as global information to further improve CD-ECR. First, we use a discourse rhetorical structure constructor to construct tree structures to represent documents. Then, we obtain shortest dependency paths from the tree structures to represent interactions between event mention pairs. Finally, we feed the above information to a multi-layer perceptron to capture the similarities of event mention pairs for resolving coreferent events. Experimental results on the ECB+ dataset show that our proposed model outperforms several baselines and achieves the competitive performance with the start-of-the-art baselines.

Cross-Document Event Coreference Resolution on Discourse Structure
Xinyu Chen, Sheng Xu, Peifeng Li and Qiaoming Zhu
School of Computer Science and Technology
Soochow University, China
(xychennlper, sxuj sust..suda.cdu.om {pfli, qmzhu) @suda.cdu.cn
Abstract
within-document (WD-ECR) and cross-document (CD-ECR) event coreference resolution. This paper focuses on the cross-document task CD-ECR.
Cross-document event coreference resolution (CD-ECR) is task of clustering event men- tions across multiple documents that refer to the same real-world events. Previous studies usually model the CD-ECR task as pairwise similarity comparison problem by using differ- ent event mention features, and consider the highly similar event mention pairs in the same cluster as coreferent. general, most of them only consider the local context of event men- tions and ignore their implicit global informa- tion, thus failing to capture the interactions of long-distance event mentions. To address the above issue, we regard discourse structure as global information to further improve CD-ECR. First, we use discourse rhetorical structure constructor to construct tree structures to rep- resent documents. Then, we obtain shortest dependency paths from the tree structures to represent interactions between event mention pairs. Finally, we feed the above information to a multi-layer perceptron to capture the sim- ilarities of event mention pairs for resolving coreferent events. Experimental results on the ECB+ dataset show that our proposed model oulperforms several baselines and achieves the competitive performance with the sfart-of-the- art baselines.
Events mainly consist of triggers and arguments. Since triggers are the main words that can most clearly express the occurrence of events, each event can be represented its corresponding trigger, Consider the following two event mentions as ex- amples:
SI: The court would hand down a ruling on whether the former president will remain detained for three more months before the current extension expires.
S2: The former president detainment was previ- ously extended for for
three months in July.
The event triggers in the event sentences S7 and S2 are "detained" and "detainment", respectively. Although these two triggers have different forms, they both refer to the same judicial type Arrest. Therefore, "detained" in SI and "detainment" in $2 have coreference relationship and can be aggre- gated to form a coreferent chain.
Previous studies on WD-ECR or CD-ECR typi- cally took a pair of event mentions with their event sentences as input and then predicted whether they are coreferent using a binary classifier (Chen and Ji, 2009; Lu et al., 2016; Lu and Ng, 2017). To this end, an important step in their models was to extract discriminative features of event mentions and then used encoding method to represent event mentions vectors. Simultaneously, almost all previous studies considered the task of event coref- erence resolution as a similarity model and focused on how to calculate the feature similarities between event mention pairs (Liu et al., 2014; 44; Lu and Ng, 2017). Besides, some studies also focused on data augmentation (Nguyen et al., 2016; Choubey and Huang, 2018; Huang al., 2019; Barhom et al., 2019; Fang and Li, 2020), which boost event coref- erence resolution on additional raw data. In the CD-ECR task, there is a limitation that
1 Introduction
In real-world texts, there usually a large number of sentences that deseribe the same event in reality, and an event will be mentioned repeatedly mul- tiple documents. When multiple event mentions (an instance of a specific event in texts) point to the same event ontology, these event mentions are coreferent. Event coreference resolution is useful for many natural language processing (NLP) appli- cations, such information extraction (Liu al., 2017), topic detection (Wayne, 1998), and question answering (Weissenborn et al., 2017). Depend- ing on whether the event mentions are in the same document, this task can be further divided into

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NS Su-mamy
which can provide useful information for associating those long-distance event mention pairs.
NS- Enahlement
EDU1
EDU2
EDU3
We propose strategy for constructing the cross discourse tree. To the best of knowledge, the first to apply the cross-document discourse tree the CD-ECR task.
FDur-- 300 Ruseion police snorth for assallaanss after attard on tornstt Evuz-Over police officers were farningr
wDu stlerked Moscow
the
Nichay Novggou
Figure I: An example of discourse tree. The three underlined words are event mention triggers.
Our model outperforms several baselines and achieves the competitive performance with the start-of-the-art baseline.
two event mentions in a pair may be scattered across one or more documents and have long distance. Previous studies on both CD-ECR and WD-ECR have attempted to extract the context fea- tures, which only represented event mentions from the local perspective (Kenyon-Dean al., 2018; Barhom et al., 2019) and the information from the global perspective was neglected.
Related Work
Research on event coreference resolution mainly draws on the method of entity coreference resolu- tion, which aims to resolve noun phrases/mentions for entities (Raghunathan et al.. 2010; Ng, 2010; Durrett and Klein, 2013; Lee et al., 2017: Joshi et al., 2019). Since event mentions have more com- plex structures than entity mentions, event coref- erence resolution is more challenging task than entity coreference resolution (Yang al., 2015). Early research on event coreference resolution mainly applied machine learning methods (Chen and Ji, 2009; Bejan and Harabagiu, 2010; Liu et al., 2014). Some researchers incorporated vari- ous kinds of regular methods into machine learning methods and improved the performance of event coreference resolution (Nicolae and Nicolae, 2006; Sangeetha and Arock, 2012; Liu et al., 2018), Since these methods relied heavily on manual annotation features, some studies paid more attention to raw text event coreference resolution (Araki and Mi- tamura, 2015; Peng et al., 2016; Lu et al., 2016; Chen and Ng. 2016; Lu and Ng. 2017).
To address the above issue, we introduce dis- course structure to extract global information of event mentions and then apply them to CD-ECR. Discourse structure is tree structure (called "dis- course tree" below). Generally, discourse tree is constructed by discourse rhetorical structure (Mann and Thompson, 1998) (DRS) constructor, which aims to represent input text as tree structure like the example Figure 1, where the leaf nodes are el- ementary discourse units (EDU) and it has smaller fine-grained units than event sentences. The dis- course tree can clearly express the rhetorical rela- tion and nuclcarity relation (c.g., NS (SS-Sumary) of each EDU, which can provide useful information for associating those long-distance event mention pairs.
On the other hand, the DRS constructor gener- ally performs tree construction on single document. In the CD-ECR task, if two event mentions in the same document, we can directly input the docu- ment to DRS constructor to obtain discourse tree, and then extract the global information of the two event mentions. However, if the query is cross- document event mention pair, and the models send their documents to the DRS constructor separately, the global information of this event mention pair cannot be obtained directly. Therefore, we also propose a construction strategy of cross-document discourse tree, which is helpful to obtain the global information of cross-document event mention pairs. We summarize the contributions of our work as fol- lows.
Recently, deep learning work has been applied to both WD-ECR (Nguyen al., 2016; Choubey and Huang, 2018: Fang al., 2018; Huang et al., 2019; Cheng et al., 2019; 9; Lu et al., 2020; Choubey et al., 2020; Lu and Ng. 2021) and CD-ECR (Kenyon-
Dean et al., 2018; Barhom et al., 2019; Zeng et al., 2020; Cattan et al., 2021; Yu et al., 2022). In WD- ECR, Krause et al. (2016) used convolutional neu- ral networks to mine event features for event coref- erence resolution. Huang et al. (2019) incorporated argument compatibility knowledge from large number of the unlabeled corpus. Lu and Ng (2021) investigated span-based models for event coref- erence resolution. In CD-ECR, argument infor- mation was introduced into event representations
We extract global information from discourse

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Inputing documents
Local Information Reprssentation
EDU Embeddings
G c
RoBERTa
Pairwise Score
EDU sequence
4
MLP
probability distribution
DRS Constructor
LCP
R
Event Coreference Prediction
DT DT-SDP
Bi-LSTM
Roy-sne
Global Information Representation
Figure The overall framework our CD-ECR model.
(Barhom et al., 2019; Zeng et al., 2020; Yu et al., 2022), Barhom et al. (2019) jointly learned en- tity and event coreference resolution and leveraged predicate-argument structures. Zeng al. (2020) integrated event-specific paraphrases and argument- aware semantic embeddings for CD-ECR. Yu et al. (2022) augmented pairwise representation with structured argument features to improve CD-ECR performance. Caciularu et al. (2021) pretrained a cross-document language model via sets of re- lated documents for CD-ECR. Held et al. (2021) extracted event mentions features from the local perspective and trained fine-grained classifier to improve CD-ECR performance.
local perspective information of event mentions from EDU embeddings. 2) Global Information Rep- resentation (GIR) extract global information rep- resentation from the discourse tree constructed by the DRS Constructor, 3) Event Coreference Predic- tion (ECP) to receive the global and local repre- sentations event mention pairs and predict the probability that two event mentions are coreferent.
3.1 Local Information Representation
Generally, previous pairwise model on the CD-ECR task took the concatenated vector R(m,, my) = [e(m;), o(m;), v(m;) v(m;)] as the base representation of event pairs, where is element-wise multiplication, the v(m:) and v(m;) are feature vector of the event mention m, and My, respectively. They obtained the feature tor v(-) through encoding the event sentence by pre-trained language model (e.g.. BERT (Xu al., 2019), RoBERTa (Liu et al., 2019)) and then con- sider the word embedding of trigger or trigger con- text tokens as the feature vector v(-).
Compared with previous deep learning work for CD-ECR, we present a novel global information representation for event mentions to enhance the interaction between long-distance event mentions.
3 Model
Formally, given set of documents D {d1, d2, ..., d whose element dj consists of series of sentences {sn, $:2, ..., Sijd, }}, and a set of event mentions .M {m11 m22 .... m]]- these event mentions in M can be distributed in different documents in CD-ECR aims to discover the event mentions in M from different documents in that refer to the same event ontology in the real world and gather them into the same cluster. ECB+ is the most popular corpus for CD-ECR and our work is also performed on this corpus.
Different from previous work, we incorporate EDU representation in R(m,, my) where event mention located. Specifically, we first encode each document using ROBERTALARGE inspired by Cat- tan et al. (2021), which splits long documents into non-overlapping segments of up to 512 word-piece tokens and encodes them independently. Differ- ently, when the document exceeds 512 tokens, in order preserve the integrity of the EDU informa- tion where the trigger located, we just split it into multiple segments of entire EDUs and not just 512 tokens.
Figure shows an overview of our CD-ECR model, which includes three main components: I) Local Information Representation (LIR) to obtain

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doc1
doc2
LCP of mi and
NS-Rr234
pairs
start here
WD pains start here
m
NS-Rga
SN-R14
Sogmenlor
EDU1
EDU;
EDU,
DRS Constructor
DRS Parser
EDU,
EDU;
EDU,
EDU,
Discourse Tree
Mh
my
[LCP, DT-SDP]
DT-SDP: INS- R.s. NS-Rig.4 SN-Raal
(a) Discourse tree construttoon step
(b) A sample LCP DT-SDP discourse tree
Figure 3: Architecture DRS constructor and a sample global information representation.
After encoded by RoBERTa, we extract the trig- ger pair feature ( (m1,, mj) and the EDU pair feature e(m;; m2) from word embeddings for the event mention pair (m,, my), the local information rep- resentation Rioeal (i is obtained as follows.
two ways shown in Figure 3(a). If (m, my) is within-document event mention pair, we feed whole document directly to the DRS constructor to obtain discourse tree. If (m², m2) is cross- document event mention pair, we extract the two event sentences that my and my are located respec- tively, then the other texts are sent to T5 (Raffel et al., 2020) for compression. The compressed texts are concatenated with two event sentences by their origin order to form the input document for the DRS constructor. Finally, the cross-document dis- course tree is obtained. In our work, utilize the state-of-the-an discourse rhetoric structure parser (DRS) (Zhang et al., 2021) to construct discourse trees for the CD-ECR task-specific corpus.
t(m;, mj) = L4, ty],
c(m;, m2) = C o cjl, (i.j) [_(m,,mm))) e(m;, m2)].
(1)
where t4, tú denotes the trigger tokens embeddings of my mj, and ex Ej denotes the EDU tokens embeddings m,, my.
3.2 Global Information Representation
GIR aims to obtain the global information repre- sentation of event mention pair from discourse tree. Figure 3(a) shows the discourse tree construction step for both within-document (WD) and cross- document (CD) event mention pairs.
Shortest Dependency Path (SDP) is widely used in various NLP tasks (Xu et al., 2015; Cheng and Miyao, 2017) to capture crucial interaction infor- mation between sentences, we combine it with discourse tree for CD-ECR task, called DT- SDP. As shown in Figure 3(b), we assume that the event mentions m; and m, are located in EDU, and EDU;, respectively, and the shortest path be- tween them marked red. We express nodes on the red line as the sequence [NS-R1,2, NS-R NS R3,4 to represent DT-SDP. All non-leaf nodes contain the probability distributions information of the rhetoric and nuclearity relation obtained by the DRS parser. Since different event mention pairs can obtain different length of DT-SDP, we apply Bi- LSTM network to encode variable length sequence DT-SDP, and take the output of last hidden layer as DT-SDP representation, denoted as R SDP.
Generally, DRS constructor consists of two main components: Segmentor and DRS parser. The segmentor receives an article with several sentences and splits it into a set of EDU sequences [EDU1, EDU2....EDD,I. where all EDUs are leaf nodes of the discourse tree. Then, the DRS parser predicts a specific rhetoric and nuclearity relation between two adjacent EDUs, and then forms superior DU, which links others EDUs DUs to obtain discourse tree.
Specifically, we first prepare the input data for the DRS constructor. Since the two event mentions in a given input event mention pair (m;, m2) may come from different documents, we process it in

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Additionally, we also extract the lowest common parent node LCP of EDUs that the event men- tions my and my are located from the discourse tree (as shown by dark green node the Figure 3(b)). The probability distributions information of LCP is denoted as RICP. We finally obtain the global information representation Rgiobal(,, j) by concate- nating RICP and RDD- as follows.
Train
Dev
Test
Topics
25
8
10
Documents
574
196
206
Sentences
1037
346
457
Event mentions
3808
1245
1780
Event Singletons
1116
280
623
Event Clusters
1527
409
805
Table I: ECB+ statistics. We follow the data split by Cybulska and Vossen (2015): train: 1, 3, 4, 6-11, 13- 17,19-20, 22, 24-33; dev: 2, 12, 18. 21, 23, 34, 35; test:36-45, Event Clusters include singletons.
Rylobal (i. | RDP-SDP)
(2)
3.3 Event Coreference Prediction
In this stage, we have obtained the global and lo- cal information representations (i,))ar(t. and (i, of the event mention pair (m4, mj)- We fuse the two features and send them the multi-layer perceptron (MLP) and sigmoid acti- vation function to obtain the coreference score as follows.
annotated different but similar events as subtopics for each ECB topic. We use gold event mentions for both training and evaluation. The detailed statistics are shown in Table 1.
Metrics Following the previous work (Barhom et al., 2019; Cattan et al., 2021; Yu et al., 2022), we use MUC (Vilain et al., 1995), B (Bagga, 1998), and CEAF, (Luo, 2005) evaluate the perfor- mance of our model and also report the CoNLL scores, which is the average of the above three metrics. Among them, MUC is based on event links to evaluate the performance of the model, B² compensates for MUC's neglected evaluation of non-coreferent events by using event nodes the computational target. CEAF. is similar to B² adding entities evaluate the performance of event coreference resolution. The comprehensive use of the above three metries and CoNLL can more ob- jectively measure model performance.
0 = .MP M (ighoaa (1.eet((..)))
(3)
S Sigmoid(0).
(4)
3.4 Training and Inference
During training, apply dropout in Bi-LSTM and MLP networks, the training objective is mini- mize the binary cross-entropy loss as follows.
1 IN -N Lynloggn (1- (1)oy }: (5) L - N
where N is the size of event mention pair samples and y {0, 1} is pair label.
Hyper Parameters We use the pre-trained lan- guage model ROBERTaLARGE to embed event men- tions with 1024 dimensions, the training epoch of our model is set to 10, the learning rate is set to 10-5, Adam optimizer is used to update the param- eters. The minimal and maximum output length of T5RASE generator are set to 20% and 50% of the input text, respectively. Additionally, in order make a fair comparison with the baseline of Caci- ularu et al. (2021), which uses stronger encoder LongformerRASE, we also use the LongformergAse to embed event mentions with 768 dimensions for resolving coreferent events.
During inference, predictor of Barhom al. (2019) to perform doc- ument clustering, and take event mention pairs in the same document cluster as candidate coreferent pairs. We then send these pairs to our CD-ECR model to obtain the coreference score, and consider the pairs whose score > 0.5 as coreferent pairs, oth- erwise non-coreferent. To handle the pairwise event coreference predictions, we perform best- first clustering (Huang al., 2019) on the pairwise scores build the coreferent event clusters.
first reproduce the topic
4 Experimentation
4.1
Experimental Settings
4.2 Experimental Results
Dataset Following previous work (Cybulska and Vossen, 2014), we use the ECB+ dataset to train and test our model, which is the largest and most popular dataset for CD-ECR. ECB+ is extended from ECB (Bejan and Harabagiu, 2010), which
To verify the effectiveness of our model, con- duct the following strong baselines.
1) Barhom et (2019), which jointly learns en- tity and event coreference resolution and leverages predicate-argument structures;

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MUC
B
CEAF
CoNLL
Model
P
R
F1
P
R
F1
P
R
F1
F1
Barhom et al. (2019)
84.5
77.6
80.9
85.1
76.1
80.3
73.8
81.0
77.3
79.5
Zeng et al. (2020)
85.6
89.3
87.5
77.6
89.7
83.2
84.5
80.1
82.3
84.3
Cattan et (2021)
81.9
85.1
83.5
82.7
82.1
82.4
78.9
75.2
77.0
81.0
Caciularu et al. (2021)
89.2
87.1
88.1
87.9
84.9
86.4
81.2
83.3
82.2
85.6
Held et al. (2021)
88.1
87.0
87.5
87.7
85.6
86.6
85.8
80.3
82.9
85.7
Yu et al. (2022)
85.1
88.1
86.6
84.7
86.1
85.4
79.6
83.1
81.3
84.4
Ours(RoBERTa)
85.9
88.6
87.2
85,4
87.8
86.6
83.7
82.8
83.2
85.7
Ours(Longformer)*
87.2
89.4
88.3
86.4
88.3
87.3
84.0
83.2
83.6
86.4
Table Performance comparision of different models on the ECB+ dataset, where "*p indicates that the models use LongFormer their encoders and the other models use BERT/RoBERTa their encoders.
2) Zeng et al. (2020), which integrates event- specific paraphrases and argument-aware semantic embeddings for CD-ECR;
4.8 over Barhom et al. (2019). Yu et al. (2022) augments pairwise representation with structured argument features, improving CoNLL by 4.9 over Barhom et al. (2019) and achieving competitive performance with Zeng al. (2020). This sug- gests that argument features crucial for resolv- ing coreferent events. However, using argument features to calculate event similarity from lo- cal perspective, ignoring the global features events, which leads to the poor performance on long-distance event mentions. Our model uses dis- course trees to capture interactions between event mentions, thus enhancing the representation of event information and improving CD-ECR perfor- mance.
3) Cattan et al. (2021), which develops an end- to-end baselinn for CD-ECR:
4) Caciularu et al. (2021), which pretrains lan- guage model via sets of related documents for CD-ECR. Longformer was used for their encoder. 5) Held et al. (2021), which extracts event men- tions fcatures from the local perspective and trained fine-grained classifier for CD-ECR.
6) Yu et al. (2022), which augments pairwise representation with structured argument features to improve CD-ECR performance.
Table 2 reports the performance of the above six baselines and our model on ECB+ with encoder RoBERTa and Longformer, and the results show that our model (Longformer) significantly (P<0.01) outperforms the best Held et al. (2021), with the improvement of 0.7 in the average score CoNLL, and our model (RoBERTa) achieve the competitive result with them. This result indicates the effective- ness of our proposed model in resolving coreferent event.
Compared with Cattan et al. (2021), who develop an end-to-end baseline for CD-ECR and only use RoBERTa to encode event mentions without using other features, our model improves the CoNLL score by 4.7. It also shows the effectiveness of global information in the discourse tree, since our baseline is also RoBERTa.
Compared with Caciularu et al. (2021), who em- ployed Longformer the encoder. Since Long- former is capable of encoding entire documents, outperforms other encoders in the event corefer- ence resolution task. For fair comparison, we re- place our text encoder RoBERTa with Longformer and the experimental results shows that our model outperforms Caciularu et al. (2021) with average CoNLL score improvements of 0.8.
Formally, the common part of Barhom et al. (2019), Zeng al. (2020) and Yu al. (2022) is that their input feature of event mention pairs can be represented as R(m;, my) [u(m),, u(m)), u(m;) u(m))) f(m4, my) is additional pairwise feature. Barhom et al. (2019) trains entity and event coreference to- gether and takes argument features fm, m,). which outperforms several early CD-ECR models (Cybulska and Vossen, 2015; Kenyon-Dean al., 2018). Zeng al. (2020) not only uses the ar- gument features but also integrates event-specific paraphrases, significantly improving CoNLL by
my)], where
Compared with Held et al. (2021), both our model (RoBERTa) and Held et al. (2021) achieve the same CoNLL score 85.7. Held al. (2021) focuses on extracting event mentions features from the local perspective and trains a fine-grained clas- sifier. Compared with them, our model pays more

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MUC
B
CEAF
CoNLL
Model
P
R
F1
P
R
F1
P
R
F1
F1
singletons+
85.9
88.6
87.2
85.4
87.8
86.6
83.7
82.8
83.2
85.7
Corpus
singletons-
85.9
88.6
87.2
74.5
76.1
75.3
57.4
76.9
65.7
76.4
Ours
singletons+
82.0
84.6
83.3
75.4
71.8
73.6
82.1
81.8
81.9
79.6
Topic
singletons-
82.0
84.6
83.3
57.8
58.0
57.9
48.7
65.4
55.8
65.7
singletons+
81.9
85.1
83.5
82.7
82.1
82.4
78.9
75.2
77.0
81.0
Corpus
singletons-
81.9
85.1
83.5
70.2
70.8
70.5
52.3
68.2
59.2
71.1
Cattan
singletons+
76.3
80.1
78.1
71.7
77.4
74.5
77.8
73.1
75.4
76.0
Topic
singletons-
76.3
80.1
78.1
54.1
63.4
58.4
44.2
56.3
49.5
62.0
Table 3: Performance comparison corpus/topic level and witheiinggoooss///iittootiinnnnn singllll singletons.
attention to extracting event mention features from the global perspective, which uses discourse tree to capture the features of long-distance event mention pairs. In word, the performance improvement of our model and Held al. (2021) can be owned to the intensive study of global and local features, respectively.
pairs to baseline 2). However, Rglobal ( my)
only contains RICP;
4) +SDP, which adds the feature RDr--SPP baseline 3).
The results are shown in Table 4. Since the pro- cess of constructing cross-document discourse trees is more complicated than that of dochmnnd discourse (mainly in the data preparation stage), we compare the performance of adding the cross-document LCP information separately due to the other influencing factors such as data noise.
Comapred with our model Ours(RoBERIa)) us- ing RoBERTa as encoder, Ours(Longformer) using Longformer improves CoNLL by 0.7. This indo- cates that LongFomer is a stronger encoder than RoBERTa the CD-ECR task.
Compared with baseline(RoBERTT,, +WD-LCP improves CoNLL by 1.2 the ECB+ dataset ing the within-document LCP. This preliminarily shows the effectiveness of the LCP information in discourse tree. Compare with the baseline +WD- LCP and +CD-LCP, the introduction of the cross- document LCP leads to an improvement 1.6 on CoNLL. This indicates that LCP can alleviate the long-distanee problem which is poorly handled by local information representation. The comparison +CD-LCP and +DT-SDP shows the significant improvement of 1.8 CoNLL and indicates that DT-SDP crucial capture the interaction be- tween long-distance event mentions.
In the above baselines, Cattan et al. (2021) eval- uate their model not only at the corpus level with singletons, but also at the topic level without single- tons. We also report the result on these experiment settings. The performance comparison with Cattan et al. (2021) at the corpus/topic level with (single- //nsh)/without (singletons-) singletons are shown in Table 3. The results show that model sig- nificantly outperforms Cattan al. (2021) on both corpus level and topic level with/without singletons on all metrics.
5 Analysis
5.1 Impact of Global Information
5.2 Analysis Construction Strategy for Cross Document Discourse Structure
We conduct ablation experiments to further eval- uate the contribution of global information from discourse tree and design the following simplified models.
In order deeply analyse the impact of different construction strategies of the cross-document DRS tree, we design two other cross-document DRS con- struction strategies for comparison with the strategy in our model as follows.
!) Baseline(RoBERTa)) which resolves corefer- ent events using local feature Rioeal (m;; my) only; 2) +WD-LCP, which adds the global feature Rylobal (m4, my) of within-document event men- tion pairs to baseline However, Rylobal (14, my) only contains RICP;
1) ES-comp, which takes event sentence and other sentences compressed by T5 model as structor input;
3) +CD-LCP, which adds the global feature Reglobal (m,, my) of cross-do event mention
2) ES-only, which takes only event sentence as constructor input without any other text;

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MUC
B²
CEAF
CoNLL
Model
P
R
F1
P
R
F1
P
R
F1
F1
BaselineRRoBERTa)
82
85.2
83.6
82.9
82.2
82.5
75.5
79.2
77.3
81.1
+WD-LCCP
83.1
85.2
84.1
83.2
82.4
82.8
79.2
80.6
79.9
82.3
+CD-LCP
84.8
87.8
86.3
84.9
83.3
84.1
82
80.7
81.3
83.9
+DT-SDP
85.9
88.6
87.2
85.4
87.8
86.6
83.7
82.8
83.2
85.7
Table 4: An ablation study for discourse structure.
MUC
B
CEAF
CoNLL
Strategy
P
R
F1
P
F1
R
F1
w/ DT-SDP
85.9
88.6
87.2
85.4
87.8
86.6
83.7
82.8
83.2
85.7
ES-comp
w/o DT-SDP
84.8
87.8
86.3
84.9
83.3
84.1
82.0
80.7
81.3
83.9
w/ DT-SDP
84.8
86.6
85.7
84.0
82.7
83.3
78.3
81.3
79.8
82.9
ES-only
w/o DT-SDP
84.5
86.3
85.4
83.9
82.5
83.2
78.0
81.1
79.5
82.7
w/ DT-SDP
85.2
86.7
85.99
84.2
86.1
85.1
81.1
82.2
81.6
84.2
Full-doc
w/o DT-SDP
83.7
85.6
84.6
80.5
84.2
82.3
77.5
80.2
78.8
81.9
Table 5: Results using different construction strategies of cross-document DRS tree.
3) Full-doc, which concatenates two documents that contain the event mention pairs and then sends them to the constructor.
comp. It shows that ES-comp is compromise strategy, because taking event sentences and other compressed irrelevant texts as constructor input can increase the positive impact so as to achieve the best performance.
The results are shown in Table Regardless of whether DT-SDP information is introduced, the construction strategy ES- comp outperforms the two other strategies. If we exclude DT-SDP, the Full- doc strategy achieves the worst performance due to the data noise introduced by irrelevant texts. The ES-only strategy achieves the second worst perfor- mance, which is slightly better than Full-doc. How- ever, it is fails to outperform ES-comp because has no other contextual information to guide it.
5.3 Case Study
In this subsection, we give the examples to anal- yse the effectiveness of global information and the strategy ES-comp in model. Considering the following two documents A and as examples, where the bolded texts represent event sentences (S7 and S4, respectively), and the underlined words indicate event mentions, the goal of our model to predict whether the event mentions "winning" and "chosen" are coreferent.
Comparing each strategy with (w/) or without (w/o) DT-SDP, we can see that the ES-only strategy has the least performance improvement when DT- SDP is included because the input text contains only event sentences, which is too short, and the depth and breadth of the constructed discourse are too small, leading to the short length of the shortest path between two EDUs. Using the node sequence to represent the path may only include common parent nodes, which tends to exclude DT- SDP. In contrast, the performance of the Full-doc strategy achieves the most improvement. This is because the long input text increases the depth and breadth of the discourse tree, which also increases the length of the shortest path and enriches the representation of DT-SDP. However, the existence large amount of noise also limits the increase in performance so that it does not outperform ES-
A: (SI) Smith, 26, who played young political researcher in the show, will become the biggest star of all after winning the role of the 11th Doc- tor. (S2) Speaking The Guardian, Buchan said his old co-star would make an excellent Doctor Who. "It s sublime bit of custing. He's got that huge hair, a twinkle in his eye -Mat' 's the king of geek chic. He is possibly going to be one of the best Doctors we 've ever had."
B: (S3) 26-year-old Matt Smith has been cast as the next incarnation of the Doctor. Users on the Facebook Doctor Who forum that I frequent mostly had the same reaction: "Who" Matt Smith?" (S4) The guy is relatively unknown and the skepties wondered if the right person was chosen. (S5) After all, everyone speculated that Paterson Joseph,

---

who had appeared on couple episodes of the show, was going to be the next Doctor and here we get some no-name.
"NS-Elaborate" in DT-SDP associate EDU4 with J. EDU13 can introduce the agent "Smith" of the event mention "winning" EDU4. "Smith" can also be passed to the event mention "chosen" in EDU10 using DT-SDP. This makes "Smith" ap- parent in the tokens of "winning" and "chosen" and facilitates accurate prediction.
We utilize the ES-comp approach to generate a discourse tree. Initially, we compress the three non-bolded sections $2, S3, and S5, resulting in the compressed texts CI, C2, and C3, respectively, as follows.
In this case, if we use the strategy ES-only, the node "NS- WIS-Elaborate" will be excluded in the ob- tained DT-SDT and the common argument "Smith" cannot be perceived. Finally, if we use the Full-doc strategy, the pair still gets low coreference score, because the tree structure becomes complicated and the redundant information interferes with the model's 's perception of argument information.
Cl: It's sublime bit of casting.
C2: Matt Smith has been cast as the next incar- nation of the Doctor. "Who 's Matt Smith?"
C3: we get Paterson Joseph.
Then, the compressed documents A and B are concatenated as [SI, CI, C2, S4, C3] and then are fed to the DRS Constructor. The resulting EDU sequence is displayed as follows.
6 Conclusion
F:U;; Smith, 26,
EDU2: who played young political researcher in the show,
In this paper, we propose a cross cross-document event coreference resolution model. The novelty of our model twofold. First, we introduce the dis- course structure that represents global informa- tion for pairs of event mentions to provide support for event coreference resolution. Second, we pro- pose strategy for constructing cross crcumen dis- course trees, which allows cross-document coref- erent event mentions be easily identified by the model. Experimental results on the ECB+ dataset show that our proposed model outperforms several baselines. In the future, we plan to extend our task to cross-modal event coreference resolution.
3DU3: will become the biggest star of all
EDU4: after winning the role of the 11th Doctor
5DU5: It's a sublime bit of casting.
EDUs: "Matt Smith has been cast as the next
incarnation of the Doctor.
EDU7: Who's s Maul Smith ?
EDUs: The guy is relatively unknown
EDUg: and the skeptics wondered
ED if the right person was chosen
EDUn:: get Paterson Joseph.
Figure 4 illustrates the simplified discourse tree produced by the ES-comp strategy. Notably, since we solely examine the coreferent relation between "winning" and "chosen", Figure excludes irrel- evant nodes like EDUII Furthermore, EDU,, EDU2, and EDU; are merged into EDU1~3 since these three nodes not part of DT-SDP. The DT- SDP in this case is INS-Elaborate, NS-Eaabor, NS-Cause, SN-Span, SN-Cause].
Limitations
Our method still suffers from several shortcomings. First, we focus only on the event coreference res- olution step using annotated event mentions. The upstream task event detection is also critical for event coreference resolution. Second, although the introduction of the discourse tree and global information has great performance boost, the in- ference time of the DRS constructor is long. Last but not least, there is still room for optimization in the strategy of cross-document DRS construction.
Directly measuring the similarity between EDU4 and EDU10 to predict coreference relation would result in misidentification due to their differing contexts. Since the rhetorical relation nodes
Acknowledgements
DS-Cause
The authors would like to thank the three anony- mous reviewers for their comments on this paper. This research was supported by the National Natu- ral Science Foundation of China (Nos. 62276177. 61 11836007, and 62376181), and Project Funded by the Priority Academic Program Development Jiangsu Higher Education Institutions (PAPD).
Elaboraae
SN-Span
NS Elaborate
SN-Cause
EDU
EDU4
EDUst
EDU
EDU,
EDU
Figure 4: The cross-document discourse tree con- structed by the strategy ES-com comp.

---

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Reviews:
[{"review_id": "iva5uoa7J1", "reviewer": "Reviewer_fuqf", "summary": "", "questions": "1. Results in Table 4 are interesting. Compressing the rest of the document seems to be quite effective. I wonder what information this compressed document includes, and why it's better than full documents. The author (s) attribute this to noise in line 515, but some additional analysis here can be useful.\n2. In the ECB+ dataset, only few sentences from each document were annotated with coreference relations. To construct the discourse structure, did the author(s) use the full documents or only these annotated sentences?\n3. Following up on the above point, how would the discourse structure based method deal with longer documents. The related work CDLM (cited above) works on longer context inputs, so how do they compare against each other in long document settings.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your valuable comments.\n\n**Q1: The paper completely misses out a comparing against important prior work. Cross-document document modeling has previously been studied for this task (Caciularu et al., Findings 2021 (https://aclanthology.org/2021.findings-emnlp.225/)) and the paper doesn't provide any mention of this work.**\n\n**A1**: We will add Caciularu et al. (Findings of EMNLP 2021) to the \"Related Work\" section and add it to our baselines in the revised paper.\n\nThis work was not used as baselines in the original paper because it employed Longformer as the encoder, whereas our model and baselines used RoBERTa as the encoder. Since Longformer is capable of encoding entire documents, it outperforms other encoders in the event coreference resolution task. However, utilizing the document-level event encoder LongFormer requires significant GPU memory (approximately 44GB), necessitating a powerful graphics card such as the A100-80G for efficient operation. This requirement presents a challenge to researchers and institutions with limited computing resources.\n\nIt would be unfair to directly compare our RoBERTa-based model to those using the stronger pre-training language model Longformer. For fair comparison, we replace our text encoder RoBERTa with Longformer and the experimental results are showed in the following table. The table shows that our model outperforms Caciularu with average CoNLL score improvements of 0.8. We will include these results in our revised paper.\n\n| **Model**        |  | **$MUC$** |  |  | **$B^3$** | |  | **$CEAF_e$** |  | **$CoNLL$** |\n|:----------------:|:----:|:-------:|:----:|:----:|:------:|:----:|:----:|:---------:|:----:|:---------:|\n|                  | P    | R       | F1   | P    | R      | F1   | P    | R         | F1   | F1        |\n| Caciularu(CDLM)  | 89.2 | 87.1    | 88.1 | 87.9 | 84.9   | 86.4 | 81.2 | 83.3      | 82.2 | 85.6      |\n| Ours             | 87.2 | 89.4    | **88.3** | 86.4 | 88.3   | **87.3** | 83.2 | 84.0      | **83.6** | **86.4**      |\n\n**Q2: The qualitative analysis presented in section 5.3 is hard to follow. The intuition behind the use of discourse structure can be described better in the paper.**\n\n**A2**: We will revise this section as follows:\n\nConsidering the following two documents A and B as examples, where the bolded texts represent event sentences (S1 and S4, respectively), and the underlined words indicate event mentions,  the goal of our model is to predict whether the event mentions “winning” and “chosen” are coreferent.\n\nA: (S1)**Smith, 26, who played a young political researcher in the show, will become the biggest star of all after winning the role of the 11th Doctor.** (S2) Speaking to The Guardian, Buchan said his old co-star would make an excellent Doctor Who. ``It s a sublime bit of casting. He's got that huge hair, a twinkle in his eye - Matt's the king of geek chic. He is possibly going to be one of the best Doctors we've ever had.'' \n\nB: (S3) 26-year-old Matt Smith has been cast as the next incarnation of the Doctor. Users on the Facebook Doctor Who forum that I frequent mostly had the same reaction: `` Who 's Matt Smith?''  (S4) **The guy is relatively unknown and the skeptics wondered if the right person was chosen.** (S5) After all, everyone speculated that Paterson Joseph, who had appeared on a couple episodes of the show, was going to be the next Doctor and here we get some no-name.\n\nWe utilize the ES-comp approach to generate a discourse tree. Initially, we compress the three non-bolded sections S2, S3, and S5, resulting in the compressed texts C1, C2, and C3, respectively, as follows:\n\nC1: It's a sublime bit of casting.\n\nC2: Matt Smith has been cast as the next incarnation of the Doctor. ''Who 's Matt Smith?''\n\nC3: we get Paterson Joseph.\n\nThen, the compressed documents A and B are concatenated as [S1, C1, C2, S4, C3] and then are fed to a DRS Constructor. The resulting EDU sequence is displayed as follows: \n\nEDU1: Smith, 26, \n\nEDU2: who played a young political researcher in the show,\n\nEDU3: will become the biggest star of all\n\nEDU4: after winning the role of the 11th Doctor\n\nEDU5: It's a sublime bit of casting. \n\nEDU6: \"Matt Smith has been cast as the next incarnation of the Doctor.\n\nEDU7: '' Who's Matt Smith ? ''\n\nEDU8: The guy is relatively unknown\n\nEDU9: and the skeptics wondered\n\nEDU10: if the right person was chosen\n\nEDU11: we get Paterson Joseph .\n\nFigure 4 (Page 8) illustrates the simplified discourse tree produced by the ES-comp strategy. Notably, we solely examine the coreferent relationship between \"winning\" and \"chosen\", hence Figure 4 excludes irrelevant nodes like EDU11. Furthermore, EDU1, EDU2, and EDU3 are merged into EDU1-3 since these three nodes are not part of DT-SDP. The DT-SDP in this case is [NS-Elaborate, NS-Elaborate, NS-Cause, SN-Span, SN-Cause].\n\nDirectly measuring the similarity between EDU4 and EDU10 to predict coreference would result in misidentification due to their differing contexts. Since the rhetorical relation nodes \"NS-Elaborate\" in DT-SDP associate EDU4 with EDU1-3, EDU1-3 can introduce the agent \"Smith\" of the event mention \"winning\" to EDU4. \"Smith\" can also be passed to the event mention \"chosen\" in EDU10 using DT-SDP. This makes \"Smith\" apparent in the tokens of \"winning\" and \"chosen\" and facilitates accurate prediction.\n\n**Q3: I wonder what information this compressed document includes, and why it's better than full documents. The author (s) attribute this to noise in line 515, but some additional analysis here can be useful.**\n\n**A3**: The compressed file will preserve participant details of a particular event, aiding the enhancement of argument information. Examples can be seen in A2 above (namely C1, C2, and C3). This information is particularly valuable when making coreference predictions on two event mentions that share participants but differ in context as it boosts their similarity. The full documents contain extraneous event triggers and arguments. If constructing a discourse tree using the entire document as input, the model cannot distinguish the rhetorical relations between two specific event mentions. This can introduce additional errors that undermine the model's performance.\n\nConsidering the examples in A2, the compressed text C1 omits information about other entities such as \"The Guardian\" and \"Buchan\", as well as redundant action words like \"said\" and \"going\". Removing these details allows the model to better focus on the rhetorical connection between the two specific event mentions \"winning\" and \"chosen\", which is helpful in predicting their relation.\n\n**Q4: In the ECB+ dataset, only few sentences from each document were annotated with coreference relations. To construct the discourse structure, did the author(s) use the full documents or only these annotated sentences?**\n\n**A4**: To create the discourse structure of event mention pairs within a document, we utilize the entire document as input. For event mention pairs across documents, we implement the ES-comp technique and also try feeding either the complete document or only annotated sentences to the DRS constructor. The experiment results are showed in table 4, which shows that ES-comp is the best strategy.\n\n**Q5: Following up on the above point, how would the discourse structure based method deal with longer documents. The related work CDLM (cited above) works on longer context inputs, so how do they compare against each other in long document settings.**\n\n**A5**: To deal with longer documents, we use a compression method to reduce their size while maintaining crucial data and removing irrelevant information. The experimental results on long documents show that our model using RoBERTa can achieve similar performance, in comparison with Longformer-based CDLM. This result indicates the effectiveness of the compression mechanism in filtering redundant information. If the RoBERTa encoder in our model is replaced by Longformer, our model will outperform CDLM, showed in the table of Q1.", "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 studies an important research question about integrating global discourse information into the pairwise coreference resolution task. This is well-motivated for the task of cross-document coreference resolution. The methods presented are simple and innovative and provide strong results over the listed baselines.\n2. The ablations studies are quite thorough. The paper showcases the effectiveness of each module, within-doc LCP, cross-doc LCP, and the DT-SDP. The use of T5-based compression for cross-document context is simple and effective.\n3. The paper is mostly well-written and provides enough details about the methods described in the work.", "reasons_to_reject": "1. The paper completely misses out a comparing against important prior work. Cross-document document modeling has previously been studied for this task (Caciularu et al., Findings 2021 (https://aclanthology.org/2021.findings-emnlp.225/)) and the paper doesn't provide any mention of this work. The results presented in this work are comparable to those in Caciularu et al., Findings 2021.\n2. The qualitative analysis presented in section 5.3 is hard to follow. The intuition behind the use of discourse structure can be described better in the paper.", "questions_to_authors": "1. Results in Table 4 are interesting. Compressing the rest of the document seems to be quite effective. I wonder what information this compressed document includes, and why it's better than full documents. The author (s) attribute this to noise in line 515, but some additional analysis here can be useful.\n2. In the ECB+ dataset, only few sentences from each document were annotated with coreference relations. To construct the discourse structure, did the author(s) use the full documents or only these annotated sentences?\n3. Following up on the above point, how would the discourse structure based method deal with longer documents. The related work CDLM (cited above) works on longer context inputs, so how do they compare against each other in long document settings.", "paper_topic_and_main_contributions": "This paper studies the task of cross-document event coreference. They propose methods to incorporate the discourse structure of documents for both within-document and cross-document mention pairs. They use a combination of two discourse related signals, lowest common parent (LCP) and shortest dependency path in the discourse tree (DT-SDP). For the cross-document mention pairs, they compress the two documents except for the two focus sentences using a T5-based system and create a meta document.\n\nThe experiments on the standard ECB+ dataset show the effectiveness of the proposed method, especially the DT-SDP module. Interestingly, the T5-based compression of cross-document context is quite effective and outperforms the use of full document texts.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew Peters, Arie Cattan, and Ido Dagan. 2021. CDLM: Cross-Document Language Modeling. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2648–2662, Punta Cana, Dominican Republic. Association for Computational Linguistics."}}, {"review_id": "iocHMKONSb", "reviewer": "Reviewer_T5Xb", "summary": "", "questions": "Q1. L208 In the local information, it is very common to have $v(m_i) - v(m_j)$ as another representation. Is there any reason why the paper did not use it?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your valuable comments.\n\n**Q1: The paper missed some stronger baselines that have similar/better performance on some metrics.**\n\n**A1**: We will add Caciularu et al. (Findings of EMNLP 2021) and Held et al. (EMNLP 2021) to the \"Related Work\" section and add both papers to our baselines in the revised paper.\n\nCaciularu et al. (2021) was not used as baselines in the original paper because they employed stronger pre-trained model Longformer as the encoder, whereas our model and the baselines used RoBERTa as the encoder. However, utilizing LongFormer requires significant GPU memory (approximately 44GB), necessitating a powerful graphics card such as the A100-80G for efficient operation. This requirement presents a challenge to researchers and institutions with limited computing resources.\n\nIt would be unfair to directly compare our RoBERTa-based model to those using Longformer. For fair comparison with Caciularu et al. (2021), we replace our text encoder RoBERTa with Longformer and the experimental results are showed in the following table. The table shows that our model outperforms Caciularu with average CoNLL score improvements of 0.8. \n\nWe also add Held et al. (2021) as a baseline, which used fine-tuning RoBERTa. Both our model and Held et al. (2021) achieve the same CoNLL score 85.7. Held et al. (2021) focused on extracting event mentions features from the local perspective and trained a fine-grained classifier. Compared with them, our model pays more attention to extracting event mention features from the global perspective, which uses DT-SDP and LCP to capture the features of long-distance event mention pairs. In a word, the performance improvement of our model and Held et al. (2021) can be owned to the intensive study of global and local features, respectively. \n\nWe will include these results in our revised paper.\n\n| **Model**        |  | **$MUC$** |  | | **$B^3$** |  |  | **$CEAF_e$** | | **$CoNLL$** |\n|:----------------:|:----:|:-------:|:----:|:----:|:------:|:----:|:----:|:---------:|:----:|:---------:|\n|                  | P    | R       | F1   | P    | R      | F1   | P    | R         | F1   | F1        |\n| Caciularu(CDLM)  | 89.2 | 87.1    | 88.1 | 87.9 | 84.9   | 86.4 | 81.2 | 83.3      | 82.2 | 85.6      |\n| Held             | 88.1 | 87.0    | 87.5 | 87.7 | 85.6   | 86.6 | 85.8 | 80.3      | 82.9 | 85.7      |\n|Ours(RoBERTa-based)|85.9|88.6|87.2|85.4|87.8|86.6|83.7|82.8|83.2|85.7|\n| Ours(Longformer-based)             | 87.2 | 89.4    | **88.3** | 86.4 | 88.3   | **87.3** | 83.2 | 84.0      | **83.6** | **86.4**      |\n\n\n**Q2: The method in this paper depends heavily on the discourse parser, the paper has not reported the effect of the performance of the discourse parser accuracy on the final performance.**\n\n**A2**: Since there is no discourse relation annotated in the ECB+ dataset, we did not report the performance of discourse parsing in our paper. At present, the performance of discourse parsing remains low. In our paper, we utilize Zhang's system to create discourse trees, achieving F1-scores of 71.8, 59.5, and 47.0 for Span, Nuclearity, and Relation tasks on the RST dataset. \n\n \n**Q3: L208 In the local information, it is very common to have $v_{m_i}-v_{m_j}$ as another representation. Is there any reason why the paper did not use it?**\n\n**A3**: $v_{m_i}-v_{m_j}$ typically represents the Euclidean distance between two event mentions, while $v_{m_i} \\circ v_{m_j}$ typically represents cosine similarity. Cosine similarity places greater emphasis on the difference in direction between the vectors of event mentions. Maintaining consistency in the direction of event mention vectors is important in determining whether two event mentions are coreferent, which Euclidean distance fails to capture.", "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 proposed to use global information to provide more information to the prediction which is new for this event coreference problem. \n2. The paper is well-written and easy to follow. \n3. The paper provided an analysis of the parsing strategy to obtain the tree for the global information and an ablation study.", "reasons_to_reject": "1. The paper missed some stronger baselines that have similar/better performance on some metrics. This may justify the SOTA achievement of this paper. E.g. Held et at 2021 (https://aclanthology.org/2021.emnlp-main.106.pdf) got $B^3=86.6$ which is equal to the $B^3$ performance of this paper. Caciularu et al. 2021 (https://aclanthology.org/2021.findings-emnlp.225.pdf) have $MUC=88.1 $ which is greater than 87.2 of this paper and CONLL=85.6 which is equivalent to 85.7 of this paper. \n2. The method in this paper depends heavily on the discourse parser, the paper has not reported the effect of the performance of the discourse parser accuracy on the final performance.", "questions_to_authors": "Q1. L208 In the local information, it is very common to have $v(m_i) - v(m_j)$ as another representation. Is there any reason why the paper did not use it?", "paper_topic_and_main_contributions": "The paper focuses on the problem \"Cross-Document Event Coreference Resolution\". The paper addresses the lack of global information embedded in the final representation for the prediction. So the paper proposed a discourse parser to construct a discourse tree to represent each document. Then its shortest path between two mentions, and the lowest common parent node are extracted from the discourse tree to encode global information.  The paper shows the performance improvement for some metrics, The paper also provided the ablation study and analysis of the construction strategy.", "excitement": 4, "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": {"Missing_References": "Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference \nhttps://aclanthology.org/2021.emnlp-main.106.pdf \n\nContrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities\nhttps://aclanthology.org/2022.naacl-main.267.pdf \n\nCDLM: Cross-Document Language Modeling\nhttps://aclanthology.org/2021.findings-emnlp.225.pdf", "Typos_Grammar_Style_and_Presentation_Improvements": "Equation 2: The equations should be aligned using the \"=\". using the latex environment \"aligned\" with \"&\" before the \"=\" will make them aligned.\nL214. \"e.g.\" should have been \"e.g.,\" (with the comma)\nL284: a space between the word \"tasks\" and the parenthesis is needed.\nTable 1,2,3,4: The model names should be aligned on the left.\nFigure 2b: The example is not very good as it does not show the lowest common parent node clearly.\nFigure 4: The bottom borders of the EDU's boxes are missing."}}, {"review_id": "ReZQrL1BTg", "reviewer": "Reviewer_48CY", "summary": "", "questions": "The sentence in lines 225-231 lacks clarity, the EDU sequence is concatenated to what? Do you mean that you concatenated the EDU and encode all EDUs using RoBERTa in addition to encoding each document with RoBERTa? Also, I don’t really understand the constraints in page 3 equation 1. How the sum of all EDUs is less than 512 but the sum of the length of all EDUs except the last is greater than 512?", "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": "Thank you very much for your valuable comments.\n\n**Q1: The paper doesn’t compare the proposed model to actual state-of-the-art models (that outperform the proposed model by ~1 F1 point): CDLM: Cross-Document Language Modeling (Caciularu et al., 2021) Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference (Held et al., 2021).**\n\n**A1**: We will add Caciularu et al. (Findings of EMNLP 2021) and Held et al. (EMNLP 2021) to the \"Related Work\" section and add both papers to our baselines in the revised paper.\n\nCaciularu et al. (2021) was not used as baselines in the original paper because they employed stronger pre-trained model Longformer as the encoder, whereas our model and the baselines used RoBERTa as the encoder. However, utilizing LongFormer requires significant GPU memory (approximately 44GB), necessitating a powerful graphics card such as the A100-80G for efficient operation. This requirement presents a challenge to researchers and institutions with limited computing resources.\n\nIt would be unfair to directly compare our RoBERTa-based model to those using Longformer. For fair comparison with Caciularu et al. (2021), we replace our text encoder RoBERTa with Longformer and the experimental results are showed in the following table. The table shows that our model outperforms Caciularu with average CoNLL score improvements of 0.8. \n\nWe also add Held et al. (2021) as a baseline, which used fine-tuning RoBERTa. Both our model and Held et al. (2021) achieve the same CoNLL score 85.7. Held et al. (2021) focused on extracting event mentions features from the local perspective and trained a fine-grained classifier. Compared with them, our model pays more attention to extracting event mention features from the global perspective, which uses DT-SDP and LCP to capture the features of long-distance event mention pairs. In a word, the performance improvement of our model and Held et al. (2021) can be owned to the intensive study of global and local features, respectively. \n\nWe will include these results in our revised paper.\n\n| **Model**        |  | **$MUC$** |  | | **$B^3$** |  |  | **$CEAF_e$** | | **$CoNLL$** |\n|:----------------:|:----:|:-------:|:----:|:----:|:------:|:----:|:----:|:---------:|:----:|:---------:|\n|                  | P    | R       | F1   | P    | R      | F1   | P    | R         | F1   | F1        |\n| Caciularu(CDLM)  | 89.2 | 87.1    | 88.1 | 87.9 | 84.9   | 86.4 | 81.2 | 83.3      | 82.2 | 85.6      |\n| Held             | 88.1 | 87.0    | 87.5 | 87.7 | 85.6   | 86.6 | 85.8 | 80.3      | 82.9 | 85.7      |\n|Ours(RoBERTa-based)|85.9|88.6|87.2|85.4|87.8|86.6|83.7|82.8|83.2|85.7|\n| Ours(Longformer-based)             | 87.2 | 89.4    | **88.3** | 86.4 | 88.3   | **87.3** | 83.2 | 84.0      | **83.6** | **86.4**      |\n\n\n**Q2: The model is evaluated only at the corpus level with singletons.**\n\n**A2**: The performance comparison with Cattan et al at the topic level with (singletons+) and without (singletons-) singletons are shown in following table. The results in this table show that our model outperforms Cattan et al. (2021) on both corpus level and topic level with/without singletons.\n\n|    | **Model** |         |  | **$MUC$** |  |  | **$B^3$** |  |  | **$CEAF_e$** |  | **$CoNLL$** |\n|:------:|:---------:|:-----------:|:----:|:-------:|:--------:|:--------:|:------:|:--------:|:--------:|:---------:|:--------:|:----------:|\n|        |           |             | P    | R       | F1       | P        | R      | F1       | P        | R         | F1       | F1         |\n| Ours   | Corpus    | singletons+ | 85.9 | 88.6    | 87.2     | 85.4     | 87.8   | 86.6     | 83.7     | 82.8      | 83.2     | 85.7       |\n|        |           | singletons- | 85.9 | 88.6    | 87.2     | 74.5     | 76.1   | 75.3     | 57.4     | 76.9      | 65.7     | 76.4       |\n|        | Topic     | singletons+ | 82.0 | 84.6    | 83.3     | 75.4     | 71.8   | 73.6     | 82.1     | 81.8      | 81.9     | 79.6       |\n|        |           | singletons- | 82.0 | 84.6    | 83.3     | 57.8     | 58.0   | 57.9     | 48.7     | 65.4      | 55.8     | 65.7       |\n| Cattan | Corpus    | singletons+ | 81.9 | 85.1    | 83.5     | 82.7     | 82.1   | 82.4     | 78.9     | 75.2      | 77.0     | 81.0       |\n|        |           | singletons- | 81.9 | 85.1    | 83.5     | 70.2     | 70.8   | 70.5     | 52.3     | 68.2      | 59.2     | 71.1       |\n|        | Topic     | singletons+ | 76.3 | 80.1    | 78.1     | 71.7     | 77.4   | 74.5     | 77.8     | 73.1      | 75.4     | 76.0       |\n|        |           | singletons- | 76.3 | 80.1    | 78.1     | 54.1     | 63.4   | 58.4     | 44.2     | 56.3      | 49.5     | 62.0       |\t\t\t\t\t\t\t\t\t\n\n**Q3: The model is evaluated only on a single dataset.**\n\n**A3**: Yes, it is better to evaluate our model on multiple datasets. However, ECB+ is the most popular dataset for cross-document event coreference resolution, most previous work only evaluated their performance on ECB+. Following previous work for comparison, we also evaluate our model on ECB+.\n\n**Q4: The sentence in lines 225-231 lacks clarity, the EDU sequence is concatenated to what? Do you mean that you concatenated the EDU and encode all EDUs using RoBERTa in addition to encoding each document with RoBERTa? I don’t really understand the constraints in page 3 equation 1. How the sum of all EDUs is less than 512 but the sum of the length of all EDUs except the last is greater than 512?**\n\n**A4**: These two constraints are used to truncate a document with the limitation of 512 tokens. \n\nWe will rewrite this parts as follows:\n\nHowever, to maintain the EDU information's integrity where the trigger is located, we combined the EDU sequence obtained from the segmentor in the DRS Constructor (outlined in Section 3.2) to create a DU that does not exceed a length of 512. \n\n(1)\tIf N (the token number in a document) is less than or equal to 512, we encode the full document by RoBERTa directly.\n\n(2)\tIf N is greater than 512, we first identify the EDU containing t512 (assuming it is the (q+1)-th EDU), and then encode the [EDU1, EDU2,...,EDUq] using RoBERTa.\n\n(3) We update the value of N to the number of tokens of the remaining documents and renumber them.\n\n(4) We repeat (1)-(3) until all tokens in a document are encoded.", "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 proposes a nice approach to integrate discourse signals into a cross-document event coreference model, providing substantial gain over the baseline (+4.6 F1). \n\n\n- The ablations were thoroughly conducted, showing the effect of (almost) each component, both in the pairwise scorer and the strategy for running a discourse parser on two documents.", "reasons_to_reject": "- The paper doesn’t compare the proposed model to actual state-of-the-art models (that outperform the proposed model by ~1 F1 point):\nCDLM: Cross-Document Language Modeling (Caciularu et al., 2021)\nFocus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference (Held et al., 2021).  \nWhile it may not be necessary for the model to surpass the current SOTA, the paper should still discuss the advantages of the proposed model over the SOTA, (e.g., robustness, runtime, etc). Unfortunately, the paper fails to cite these relevant papers. \n\n\n- The model is evaluated only at the corpus level with singletons. It would be nice to also evaluate the model at the topic level without singletons, as proposed in “Realistic Evaluation Principles for Cross-document Coreference Resolution (Cattan et al., 2021)”.  A few recent works have adopted this evaluation methodology, so it’ll be great to also compare your results to these models. \n\n\n\n- The model is evaluated only on a single dataset.", "questions_to_authors": "The sentence in lines 225-231 lacks clarity, the EDU sequence is concatenated to what? Do you mean that you concatenated the EDU and encode all EDUs using RoBERTa in addition to encoding each document with RoBERTa? Also, I don’t really understand the constraints in page 3 equation 1. How the sum of all EDUs is less than 512 but the sum of the length of all EDUs except the last is greater than 512?", "paper_topic_and_main_contributions": "This paper presents a new model for cross-document event coreference resolution that enriches the pairwise scorer with discourse information. Specifically, the scorer takes as input local information (embeddings of the event trigger and embeddings of the EDU), and global discourse information (lowest parent node of the two EDUs and the shortest path between the two EDUs). To find global discourse information between two event mentions in different documents, the authors first combine the documents by taking the full sentences of the event mentions and compressing the other parts of the texts using a T5 model, then run a discourse parser on this new “document”. \n\nThe model achieves a new state-of-the-art on ECB+, a popular dataset for the task. In addition, the authors provide an ablation study that shows the benefit of the different components of the pairwise scorer.", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Line 22: perpceptron → perceptron \n- Line 228: missing space before the open parentheses\n- Line 284: missing space before the open parentheses"}, "extra_text": {"Missing_References": "- CDLM: Cross-Document Language Modeling (Caciularu et al., 2021)\n- Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference (Held et al., 2021).  \n- Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities (Hsu and Horwood, 2022) \n- What happens before and after: Multi-Event Commonsense in Event Coreference Resolution (Ravi et al., 2023)"}}]

Author rebuttal:

Meta-review: The paper presents a novel approach to incorporate global discourse structures for event coreference resolution, in particular, for cross-document event coreference resolution, the approach will conduct text compression on two documents except for two focus sentences that contain event mentions under scrutiny. Reviewers expressed excitements on this approach, especially on applying text compression to enable effective discourse analysis on the long meta document combining two original documents.
Decision: Accept-Main