A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction
Year: 2023

Abstract:
Document-level relation extraction (DocRE) aims to extract relations among entities within a document, which is crucial for applications like knowledge graph construction. Existing methods usually assume that entities and their mentions are identified beforehand, which falls short of real-world applications. To overcome this limitation, we propose TAG, a novel tableto-graph generation model for joint extraction of entities and relations at document-level. To enhance the learning of task dependencies, TAG induces a latent graph among mentions, with different types of edges indicating different task information, which is further broadcast with a relational graph convolutional network. To alleviate the error propagation problem, we adapt the hierarchical agglomerative clustering algorithm to back-propagate task information at decoding stage. Experiments on the benchmark dataset, DocRED, demonstrate that TAG surpasses previous methods by a large margin and achieves state-of-the-art results1.

heading: 1 Introduction
text: Relation extraction (RE) is the task to extract relational facts from natural language text, which plays a crucial role in various downstream tasks, e.g. knowledge graph construction and question answering (Yih et al., 2015; Trisedya et al., 2019; Li and Zou, 2022). Early studies mostly focus on sentence-level RE, i.e. predicting relations among entities in one single sentence. However, in realworld scenarios such as Wikipedia articles or scientific papers, large amounts of relational facts are expressed across multiple sentences, which necessitate inter-sentence reasoning skills. Hence, recent efforts have been moving towards the more realistic document-level RE (DocRE) (Yao et al., 2019; Nan et al., 2020; Zhou et al., 2021).
∗Corresponding author. 1https://github.com/ridiculouz/TaG
Despite the rapid progress, most previous DocRE methods solely focus on the task of relation extraction, which assumes that entities and their corresponding mentions are given beforehand. As shown by Figure 1, to extract both of entities and relations at document-level, a natural idea is to use a pipeline approach. Traditionally, it first divide the whole task into subtasks of mention extraction (ME), coreference resolution (COREF) and relation extraction (RE), then use separate models to conduct each task step by step (Zaporojets et al., 2021). However, the pipeline framework ignores the underlying dependencies among subtasks, which may lead to suboptimal performance. Some progress on jointly considering the subtasks has been made (Eberts and Ulges, 2021; Xu and Choi, 2022), yet, previous attempts still model the tasks of COREF and RE separately, inducing possible bias at both encoding and decoding stages. On the one hand, these methods still suffer from the problem with lack of information sharing. They either
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completely rely on the shared language model (e.g. BERT) at representation level (Eberts and Ulges, 2021) , or only consider one-way information flow from RE to COREF and neglect other cross-task dependencies (Xu and Choi, 2022). On the other hand, prior approaches mostly employ the pipelinestyle decoding, which first recognize mention spans and form entity clusters, then perform relation classification for each entity pair. Such routine is not only time consuming, but faces with the error propagation problem (Li and Ji, 2014). The results of entity extraction may affect the performance of relation extraction and lead to cascading errors. Xu and Choi (2022) attempt to use a regularization term in COREF scorer to mitigate this issue, but the problem is still not fully resolved.
In this work, we propose TAG, a novel tableto-graph generation model, to address these aforementioned challenges. We first unify both tasks of COREF and RE with the classic table filling framework (Miwa and Sasaki, 2014; Gupta et al., 2016). We then devise a following table filler to encode original texts and make predictions for both tasks at a coarse level. Regarding mentions as nodes, we dynamically build two corresponding coreference and relation graphs, where the edges are weighted by the confidence scores of table filler. Besides, to alleviate the long-term dependency problem as well as explicitly model the syntactic information, we construct a syntactic graph over mentions. Given these three subgraphs, TAG regards them as three different types of edges and uses a relational graph convolutional network (RGCN, Schlichtkrull et al., 2018) to model implicit task dependencies at a fine level. Unlike previous multi-task systems that solely share span representations directly from the language model, our coarse-to-fine framework leverages rich node representations by propagating information through semantic and syntactic links.
Intuitively, mentions within the same entity cluster should establish similar relation links with other entities (Xu and Choi, 2022). To avoid the error propagation problem, we exploit this postulation and adapt the hierarchical agglomerative clustering (HAC) algorithm to cluster mentions. The core of HAC is the computation of coreference distance between each cluster pair. To back-propagate relational information, we compute the relation vectors of nodes and use the average Hamming distances among different clusters as additional penalty.
We evaluate TAG on DocRED (Yao et al., 2019), a widely-adopted DocRE benchmark. Experiments show that: (1) The coarse-grained table filler baseline establishes competitive results, as compared with previous methods. (2) The finegrained information propagation module and enhanced HAC decoding algorithm can effectively promote cross-task interactions and better alleviate the error propagation problem. (3) Our proposed TAG achieves new state-of-the-art and outperforms prior approaches by a large margin. We also report the first result of joint entity and relation extraction on Re-DocRED (Tan et al., 2022), a revised version of DocRED, for future research.
Our contributions can be summarized as follow:
• We unify the tasks of COREF and RE in document-level joint entity and relation extraction with a table filling framework, and propose a novel table-to-graph generation method TAG to facilitate information sharing. During the decoding stage, we adapt the HAC algorithm to enhance COREF with RE predictions, thereby mitigating the issue of error propagation.
• We demonstrate that TAG surpasses previous methods and achieves new state-of-the-art results on the standard DocRE benchmark.

heading: 2 Problem Formulation
text: Given a document D comprised of L tokens, our goal is to jointly extract all entities and relations in an end-to-end manner. As an entity may occur multiple times in the document with different mentions, the joint extraction process can be naturally divided into three subtasks:
• Mention extraction (ME), which extracts all possible spansM = {mi}Mi=1 for entities from original document, where a span is defined as a continuous sequence of words;
• Coreference resolution (COREF), which groups the local mentions into entity clusters E = {ei}Ei=1, where ei = {mij} Nei j=1;
• Relation extraction (RE), which predicts a subset from a pre-defined relation set R ∪ {⊥} (⊥ denotes no relation) between the entity pairs (eh, et)h,t=1,...,E;h̸=t.
Unlike prior works, we formulate the tasks of COREF and RE with the table filling framework,
i.e. multi-class classification between each mention pair (mi,mj). We maintain a table T |M |×|M | to represent mention pairs and employ a shared representation for both tasks.
We assign COREF label y(i,j)c ∈ {0, 1} and RE label y(i,j)r ⊆ R ∪ {⊥} for each cell in the table, respectively. For COREF, we use 1/0 to denote whether a mention pair belongs to the same entity. For RE, we transfer the entity-level label to mention-level, where mention pair (mi,mj) is tagged with the same relations of their belonging entities (eh, et), with mi ∈ eh,mj ∈ et.

heading: 3 Methodology
text: Figure 2 shows the overall architecture of TAG. TAG first conducts ME to predict mention spans (§ 3.1), after which, it jointly learns the tasks of COREF and RE with a table-to-graph generation model (§ 3.2). We will also detail the multi-task training process in § 3.3 and enhanced decoding algorithm in § 3.4.

heading: 3.1 Mention Extractor
text: We cast the problem of entity mention extraction as a sequence tagging task with BIO label. Though span-based methods are more prevalent due to their stronger expressive power, they usually demand O(L2) time complexity, while sequencebased methods only take linear time. Since the task of DocRE contains few overlapped mentions2, we adopt the sequential method for efficiency.
2In the standard benchmark DocRED, only 0.2% mentions are overlapped, and this phenomenon is usually caused by annotation errors as well.
Following Devlin et al. (2019), we leverage pretrained language model (PLM) to convert the tokens in document into vectorized features, and use a classifier to predict the BIO label for each token. We denote the extracted mentions by {mi}Mi=1.

heading: 3.2 Table-to-Graph Generation

heading: 3.2.1 Biaffine Table Filler
text: Given a document D = [wi]Li=1 and mentions {mi}Mi=1, we build the table representation of each mention pair. We adopt the entity marker strategy (Baldini Soares et al., 2019), which inserts a special token “*” at the start and end of each mention. We then use a separate PLM3 to obtain the contextual representations H = [h1, . . . ,hL]⊤,hi ∈ Rd and the multi-head attention A ∈ RH×L×L:
H,A = PLM([w1, . . . , wL]),
where A is the multi-head attention matrix in the last transformer layer. We take the embedding of start token “*” as mention embedding. To capture related context for mention pair (mi,mj), we apply the localized context pooling technique to compute context embedding c(i,j) (Zhou et al., 2021):
q(i,j) = H∑
k=1
Aik ◦Ajk,
c(i,j) = H⊤ q(i,j)
1⊤q(i,j) ,
3Our preliminary experiments show that multi-tasking ME brings marginal benefits. So we conduct ME as an independent task and use separate PLM in ME and COREF/RE.
where ◦ refers to the Hadamard product and Aik,A j k ∈ RL are the attention weights of mi,mj in the kth attention head, respectively. c(i,j) is aggregated from tokens with high attention towards both mi and mj , and hence is likely to be important to both of them.
Let hi,hj be the hidden features of mi,mj from PLM. We first project hi,hj and c(i,j) into head and tail features:
z (i,j) i = tanh(Whhi +Wchc (i,j)), z (i,j) j = tanh(Wthj +Wctc (i,j)),
where Wh,Wch,Wt,Wct ∈ Rd×d are trainable parameters. We then employ a biaffine attention model (Dozat and Manning, 2017; Wang et al., 2021) to convert mention features into a table S ∈ RM×M of scalar scores denoting either coreference or relational links:
s(i,j) = z (i,j) i W1z (i,j) j +w ⊤ 2 (z (i,j) i ⊕ z (i,j) j ) + b,
where W1 ∈ Rd×d,w2 ∈ R2d, b ∈ R are trainable parameters, ⊕ denotes concatenation. We predict coreference and relational scores Stc,Str respectively with shared representations z. Specifically, s (i,j) tr is labeled with 1 if the RE label y (i,j) r ̸= {⊥} otherwise 0.

heading: 3.2.2 Latent Graph Construction
text: Coreference and Relational Graphs. After obtaining the coreference and relational scores Stc,Str, we normalize each table with respect to column:
Gc = Softmax(Stc),
Gr = Softmax(Str).
We take Gc and Gr as the dynamic weighted graphs of coreference and relational links predicted by our previous modules. Each cell g(i,j) represents the weight of directed edge mi → mj . Syntactic Graph. To enhance learning of structured knowledge underlying natural language, we seek to explicitly introduce syntactic information into mention graph. Ideally, syntactic links can effectively encode local contexts, which can be further broadcast via coreference or relational links. Thus, it enables the model to learn long-term dependencies at a fine level.
There are several optional ways to build the desired syntactic graph. For instance, an intuitive
solution is to transfer the dependency tree over words to a graph, with mentions being the nodes. Since dependency tree only reveals intra-sentence clues, previous works (Christopoulou et al., 2019; Zeng et al., 2020) usually leverage co-occurrence information instead. Following this practice, our syntactic graph Gs connects all mentions within the same sentence using bidirectional edges.

heading: 3.2.3 Propagating Information with R-GCN
text: To consider the interactions between the tasks of COREF and RE, and to incorporate explicit syntax information, we propose an information propagation module to refine mention representations.
Specifically, we regard the latent graphs Gc,Gr and Gs as three different types of edges over the mention graph. We then apply a relational graph convolutional network on the mention graph to aggregate neighbor features along different types of edges. Given node xi at the lth layer, the update process is calculated by
x (l+1) i = tanh(
∑
t∈{c,r,s}
M∑
j=1
g (i,j) t W l tx l j + b l t),
where t is the type of edge, g(i,j)t represents the weight of directed edge mi → mj , and Wlt,blt are trainable parameters. We initialize node embedding x0i as the hidden feature hi of mention mi.
In contrast to previous Joint IE4 approaches, which either propagate task information in a pipeline manner (DYGIE, Luan et al., 2019), or only consider one-way information flow (Xu and Choi, 2022), our module integrates cross-task information in parallel and extracts relevant mention features for both tasks.

heading: 3.2.4 Classifier
text: After N times of propagation, we use the refined mention embeddings xNi ,x N j and context embedding c(i,j) to predict the COREF score s(i,j)gc and RE score s(i,j)gr :
v (i,j) i = tanh(Uhx N i +Uchc (i,j)), v (i,j) j = tanh(Utx N j +Uctc (i,j)),
s(i,j)gc = CorefBiaff(v (i,j) i ,v (i,j) j ),
s(i,j)gr = ReBiaff(v (i,j) i ,v (i,j) j ),
4Joint information extraction.
where Uh,Uch,Ut,Uct ∈ Rd×d are trainable parameters, and the n-dimensional biaffine function is defined as
Biaff(x,y) := xU⊤1 y +U2(x⊕ y) + b,
where U1 ∈ Rn×d×d,U2 ∈ Rn×2d,b ∈ Rn are trainable parameters. Note that n = 1 for the task of COREF and n = |R|+ 1 for RE, where we use a dummy class TH to learn a dynamic threshold for multi-label classification (Zhou et al., 2021). At test time, relation types with scores higher than the TH class are predicted as output ŷ(i,j)r . In cases where no such class exists, the classifier returns {⊥}.

heading: 3.3 Training
text: We perform multi-task training and optimize the joint loss for all components. We detail the training objectives and label construction for each module as follows.
Table Encoder. Given mention pair (mi,mj), the table encoder predicts coreference and relational links in the form of scalar scores s(i,j)tc , s (i,j) tr . For coreference links, we directly use COREF label y(i,j)c as gold label. For relational links, we define y(i,j)rbinary := 1(y (i,j) r ̸= {⊥})5, denoting whether any relation (eh, r, et) exists, with mi ∈ eh,mj ∈ et. We convert Sc,Sr to probability with the sigmoid function σ and optimize with binary cross-entropy loss Ltc,Ltr. Coreference Resolution. The training objective and label for fine-level coreference resolution are identical to those for coreference link prediction in table encoder. The sole difference is that it takes the refined mention representations as input. We denote the loss as Lgc. Relation Extraction. For (mi,mj), we divide the relation setR into two splits: positive setP that contains relation x exists between (mi,mj), and negative set N = R−P . We apply the adaptivethresholding loss (Zhou et al., 2021) to learn the RE classifier:
l(i,j) =− ∑
x∈P log
  exp(s
(i,j) x )∑
x′∈P∪{TH} exp(s (i,j) x′ )
 
− log
  exp(s
(i,j) TH )∑
x′∈N∪{TH} exp(s (i,j) x′ )
  ,
5Indicator function.
Algorithm 1: HAC Decoding Algorithm Input: Mention setM, threshold t Output: A set of entity clusters C // Initialization
1 for mi ∈M do 2 Ci ← {mi} // Recursively merge clusters 3 repeat 4 for Cx, Cy ∈ C, Cx ̸= Cy do 5 D(x,y) ← D(x,y)c + ρ ·D(x,y)r 6 (Cx, Cy)← argmin(Cx,Cy)D(x,y) 7 Dmin ← D(x,y) 8 if Dmin ⩽ t then 9 Merge Cx and Cy
10 until Dmin > t
and we sum over all mention pairs to calculate fine-level relation extraction loss Lgr. Finally, we jointly optimize TAG with
L = Ltc + Ltr + α · (Lgc + Lgr),
where α is a hyperparameter balancing coarse-level and fine-level loss.

heading: 3.4 Decoding
text: To avoid the error propagation problem inherent in pipeline decoding, we aim to design a decoding algorithm such that upstream task (COREF) can efficiently utilize downstream task information (RE).
Entity Cluster Decoding. We decode entity clusters based on the hierarchical agglomerative clustering (HAC) algorithm, as described in Algorithm 1. The core of HAC is to measure the distance D between two clusters Cx and Cy. We break down D into two parts: coreference distance Dc and relational distance Dr. We use the average linkage to compute Dc as
Dc = 1 |Cx| · |Cy| ∑
mi∈Cx
∑
mj∈Cy (1− σ(s(i,j)gc )).
At training stage, ground-truth relation y(i,k)r and y (j,k) r are identical if mi and mj belong to the same entity, for all mk ∈ M. Therefore, for a welltrained model, mentions within the same entity cluster should establish similar relation links with other entities. We exploit this clue as the connection between COREF and RE. Let the predicted RE
label ŷ(i,j)r be a |R|-dimensional 0-1 vector, where each digit indicates the presence of one relation type. We define the relation vector ri ∈ R2M×|R| as
ri = [ŷ (i,1) r , . . . , ŷ (i,M) r , ŷ (1,i) r , . . . , ŷ (M,i) r ]
⊤.
We use the average Hamming distance between each mention pair in cluster Cx, Cy as Dr:
Dr = 1 |Cx||Cy| ∑
mi∈Cx
∑
mj∈Cy σ(Hamming(ri, rj)).
Relation Triple Decoding. Given two entities e1 and e2, we predict their relation label with the majority voting mechanism. For relation x, the final prediction is determined by
ŷ(e1,e2)x = 1(( ∑
mi∈e1
∑
mj∈e2 ŷ(i,j)x ) >
|e1| · |e2| 2 ).

heading: 4 Experiments

heading: 4.1 Setup
text: Dataset. We evaluate TAG on DocRED (Yao et al., 2019) and Re-DocRED (Tan et al., 2022). DocRED is a large-scale human-annotated dataset for DocRE constructed from Wikipedia and Wikidata. It covers a wide range of documents from general domain, with 3,053 documents for training, 1,000 for development, and 1,000 for test, respectively. DocRED contains 96 relation types, 132,375 entities and 63,427 relation instances. Since the original dataset is incomplete, i.e. there exists a considerable amount of false negative samples, Tan
et al. (2022) provide a revised version Re-DocRED on training and validation set, with 120,664 relation instances. Notably, we report the first joint extraction result on Re-DocRED for future reference.
Metrics. Following prior works (Eberts and Ulges, 2021; Xu and Choi, 2022), we report the performance of all three subtasks for detailed analysis. Specifically, our results include (1) mention extraction (ME) in mention-level F1 score, (2) coreference resolution (COREF) in averaged F1 score of MUC, B3, and CEAFϕ4 , and (3) relation extraction (RE) in hard entity-level F1 and Ign F1 scores, where Ign F1 measures the F1 score excluding the relational facts shared by training and validation/test sets.

heading: 4.2 Overall Performance
text: Baselines. We compare TAG with various baselines for joint extraction. Early approaches take LSTM as context encoder. Built on top of it, Verlinden et al. (2021) introduce KB-IE, which integrates background information of knowledge base (Wikipedia and Wikidata) into a joint IE model. Recent methods usually finetune PLM to learn richer features. Xu and Choi (2022) implement the standard pipeline method, as well as a joint method with shared encoder and joint loss. They also propose JointM+GPGC to enable oneway information flow from RE to COREF. Eberts and Ulges (2021) present JEREX, which incorporate multi-instance learning to enhance RE performance. Giorgi et al. (2022) develop a sequence-
to-sequence model with copy mechanism, seq2rel, with inferior performance but higher efficiency. Besides, we also devise a strong baseline, TableFiller, which ablates the graph module and adopts simple heuristic decoding algorithm, i.e. it only comprises a mention extractor, a biaffine encoder, and a classifier.
Table 1 depicts the overall performance of TAG on DocRED, in comparison to other baselines. We can observe that TABLEFILLER-BERTbase marginally outperforms previous methods and establishes a competitve basis, which demonstrates the efficacy of the table filling framework. TAG-BERTbase further advances it by consistent improvements on all three subtasks. Following Xu and Choi (2022), we replace BERTbase with a stronger variant, RoBERTabase, of the same size. TAGRoBERTabase attains substantial improvements of 1.07 in COREF F1 and 2.54/2.85 in RE F1/Ign F1 over SOTA on the test set. This suggests that TAG is better at capturing important information within the document-level context and across different subtasks. We also present TAG-RoBERTalarge to explore the boundaries of joint extraction performance, which reaches 93.84 in ME F1, 86.37 in COREF F1 and 44.97/42.88 in RE F1/Ign F1 on
the test set, respectively. Table 2 shows the performance of TABLEFILLER and TAG on Re-DocRED. In comparison to DocRED, the same methods yield similar performances in coreference resolution, but improve by a large margin in relation extraction, which aligns with previous findings (Tan et al., 2022). Regarding the difference in architectures, TAG consistently outperforms TABLEFILLER in all subtasks on both dev and test sets, highlighting the effectiveness of TAG for document-level joint extraction.

heading: 4.3 Analysis on Reasoning Skills
text: A major challenge for document-level RE is the requirement of rich reasoning skills, e.g. commonsense reasoning and logical reasoning (Yao et al., 2019). One indicator to distinguish the reasoning type is the amounts of evidence sentences. To understand the merits of TAG, we visualize the recall of relations over different amounts of evidence sentences, as shown by Figure 3.
Relation instance with 0 evidence can only be inferred from common-sense knowledge, either from PLM knowledge or training corpus. TAG outperforms TABLEFILLER on such type of instances by 1.8% with the same encoder, which demonstrates the stronger ability of common-sense reasoning. TAG also consistently surpasses TABLEFILLER on a vast amount of relations with 2-4 evidence sentences, which either needs to (1) distinguish coreferential mentions within multiple sentences, or (2) perform logical reasoning over bridge entities. This reveals that the graph module and decoding algorithm are beneficial for both coreference reasoning and multi-hop logical reasoning. Finally, TAG substantially improves the recall of relations that require much evidence (6.0% for 5 sentences and
8.3% for more than 6 sentences), indicating that TAG is superior at complex logical reasoning.

heading: 4.4 The Impact of Graph Propagation
text: Figure 4 shows the effects of graph propagation on relation extraction F1 score, where -Coref, -Rel and -Syntax denote the removal of the corresponding type of edges, respectively. It can be seen that the F1 scores of all models usually peak at 2/3 graph layers, and then decrease drastically. We hypothesize that a greater depth of layers facilitates the dissemination of information to a broader range, whereas the gradient vanishing problem counteracts this advantage (Li et al., 2019). Besides, all ablation models perform worse than TAG with full channels, indicating that all types of edges contribute to better reasoning.
As the depth of layers and types of edges influence RE F1 dramatically, in contrast, these different settings do not pose much impact on coreference resolution. We will dive deeper into this question in the following subsection.

heading: 4.5 Effectiveness of Decoding
text: To verify the effectiveness of our entity cluster decoding algorithm, we compare the performance of coreference resolution with different balancing hyperparameter ρ in Table 3. Apart from the averaged F1 score of MUC, B3, and CEAFϕ4 , we also report the hard entity-level F1 score for transparently demonstrating the entity extraction performance. It can be seen that ρ = 0.1 yields the optimal performance with a 0.3% F1 gain in both metrics.
Despite that the performance of HAC decoding algorithm is boosted by the relational distance Dr, the observed improvement is not as substantial as
anticipated. Besides, adjusting ρ does not influence much as well. These findings indicate that coreference resolution seems to be more robust with various settings. To understand such phenomenon, we conduct a correlation analysis among the silver COREF label and predicted scores, as shown by Table 4. While there exists a significant correlation of -0.72 between the relational penalty and the silver label, it is still well below the correlation between coreference score and silver label. This strong association partially accounts for the aforementioned results. It further shows that Dr can only serve as a modest refining signal for coreference resolution, and increasing ρ above the threshold may hurt COREF performance.

heading: 5 Related Works
text: Document-level extraction and joint extraction are two important topics in the field of IE. Our work lies at the intersection of these two lines, which aims to jointly extract entities and relations, two core elements of IE, at document-level.
Document-level RE. Current methods in DocRE can be mainly divided into two categories: (1) Graph-based methods, which first construct a document graph of heterogeneous nodes (e.g. mention, entity, sentence) with heuristic rules, and then use GNN to perform inference on the graph (Christopoulou et al., 2019; Nan et al., 2020; Zeng et al., 2020). (2) Transformer-based methods, which exploits pretrained language model to learn cross-sentence relations either implicitly or explicitly. Various techniques have been proposed, e.g. adaptive threshold (Zhou et al., 2021) and evidence retrieval (Huang et al., 2021; Xie et al., 2022). Recently, pioneers have attempted to develop endto-end models that extracts entities and relations jointly at document-level, which is more practical and brings more challenges (Eberts and Ulges, 2021; Xu and Choi, 2022; Giorgi et al., 2022).
Joint information extraction. Early studies usually model Joint IE in a pipeline manner (Chan and Roth, 2011; Luan et al., 2019), which ignores the underlying correlation within different tasks, suffering from cascading errors and exposure bias. To address these problems, in one direction, some recent researches seek to integrate multiple subtasks by sharing information and building up implicit cross-task interaction (Zhang et al., 2020; Yan et al., 2021). In another direction, table filling strategy
has been developed, as it casts subtasks (usually NER and RE) as unified table to fill with, which explicitly leverages the interactions among subtasks (Miwa and Sasaki, 2014; Gupta et al., 2016; Wang et al., 2021).

heading: 6 Conclusion
text: In this paper, we propose TAG, a novel table-tograph generation model, to jointly extract entities and relations within a document. Different from prior approaches, we unify the tasks of coreference resolution and relation extraction with a table filling framework, and leverage a coarse-to-fine strategy to facilitate information sharing among these subtasks. To avoid the error propagation problem, we adapt the HAC algorithm to enhance COREF with RE predictions at decoding stage. Experimental results on the widely-adopted benchmark, DocRED, demonstrate that TAG significantly outperforms previous methods. Further analysis also confirms the effectiveness of the modules in our model.
Limitations
One major limitation of our work is that our experiments are only conducted on DocRED and Re-DocRED that consist of documents from general domain. Yet, information extraction has many broader applications in specific domains, e.g. biomedical data. We plan to adapt TAG to some biomedical datasets, like CDR (Li et al., 2016) and GDA (Wu et al., 2019), in the future.
Besides, since TAG consists of a number of modules and use PLM as encoder, the training process takes relatively more time and computational resources than dedicated DocRE model that only extract relations. We concern that it may affect the scalability with larger amount of either data or parameters.
Ethics Statement
We use DocRED and Re-DocRED in our experiments, and we adhere to their user agreements and licenses. These datasets are constructed from Wikipedia, which we expect to have few offensive contents or leaked privacy information.
We shall point out that our system may generate false results due to the nature of neural networks, and may be biased in the cases of domain shift or out-of-distribution. We concern that appropriate quality control is needed in downstream applications, like knowledge base construction.

heading: Acknowledgements
text: We would like to appreciate the reviewers for their valuable comments that help us to improve this manuscript. This work was supported by NSFC under grant 61932001 and U20A20174. Lei Zou is the corresponding author of this paper.

heading: B Details for Correlation Analysis
text: We conduct the correlation analysis on dev set of DocRED with TAG-RoBERTabase. The variables are constructed as follow:
• Silver. Given predicted mention spans, we assign silver label 1 for mentions that occur within the same gold entity, and 0 otherwise.
• Scorec. The probability of coreference link σ(sgc).
• Scorer. |sir − sjr|1, which serves as a pairwise estimation of the Hamming distance. Particularly, sir is defined as
[s(i,1)gr , . . . , s (i,M) gr , s (1,i) gr , . . . , s (M,i) gr ]
⊤.
We then compute the Pearson correlation coefficients of these variables, and the results is shown in Table 4.
6https://codalab.lisn.upsaclay.fr/ competitions/365
ACL 2023 Responsible NLP Checklist

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: The limitations section after conclusion

heading: 3 A2. Did you discuss any potential risks of your work?
text: The ethics statement section after conclusion

heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
text: The abstract and section 1
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? Section 4

heading: 3 B1. Did you cite the creators of artifacts you used?
text: Section 4

heading: 3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts?
text: The ethics statement section after conclusion

heading: 3 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 3 and section 4

heading: 3 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any
text: information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? The ethics statement section after conclusion

heading: 3 B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and
text: linguistic phenomena, demographic groups represented, etc.? Section 3
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4.1
C 3 Did you run computational experiments? Section 4

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? Appendix A
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? Section 4.1 and Appendix A

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Appendix A

heading: 3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did
text: you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4.1 and Appendix A
D 7 Did you use human annotators (e.g., crowdworkers) or research with human participants? Left blank.
D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response.
D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? No response.
D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

heading: Future Work
text: , 2019), in the future. Besides, since TAG consists of a number of modules and use PLM as encoder, the training process takes relatively more time and computational resources than dedicated DocRE model that only extract relations. We concern that it may affect the scalability with larger amount of either data or parameters