SlowGuess/ACL_2020_OCR huggingface dataset:
Under the same setting, our model consistently outperforms graph-based models based on static graphs or attention mechanisms. Compared with EoG, our LSR model achieves 3.0 and 2.4 higher  $F_{1}$  on development and test set, respectively. We also have similar observations for the GCNN model, which shows that a static document-level graph may not be able to capture the complex interactions in a document. The dynamic latent structure induced by LSR captures richer non-local dependencies. Moreover, LSR also outperforms GAT and AGGCN. This empirically shows that
compared to the models that use local attention and self-attention (Velicković et al., 2018; Guo et al., 2019a), LSR can induce more informative document-level structures for better reasoning. Our LSR model also shows its superiority under the setting of Ign  $F_{1}$ .
In addition, LSR with GloVe obtains better results than two BERT-based models. This empirically shows that our model is able to capture long-range dependencies even without using powerful context encoders. Following Wang et al. (2019), we leverage BERT as the context encoder. As shown in Table 2, our LSR model with BERT achieves a  $59.05F_{1}$  score on DocRED, which is a new state-of-the-art result. As of the ACL deadline on the 9th of December 2019, we held the first position on the CodaLab scoreboard under the alias diskorak.
3.4 Intra- and inter-sentence performance
In this subsection, we analyze intra- and inter-sentence performance on the development set. An entity pair requires inter-sentence reasoning if the two entities from the same document have no mentions in the same sentence. In DocRED's development set, about  $45\%$  of entity pairs require information aggregation over multiple sentences.
Under the same setting, our LSR model outperforms all other models in both intra- and inter-sentence setting. The differences in  $F_{1}$  scores between LSR and other models in the inter-sentence setting tend to be larger than the differences in the intra-sentence setting. These results demonstrate that the majority of LSR's superiority comes from the inter-sentence relational facts, suggesting that
Model	F1	Intra-F1	Inter-F1
Gu et al. (2017)	61.3	57.2	11.7
Nguyen and Verspoor (2018)	62.3	-	-
Verga et al. (2018)	62.1	-	-
Sahu et al. (2019)	58.6	-	-
Christopoulou et al. (2019)	63.6	68.2	50.9
LSR	61.2	66.2	50.3
LSR w/o MDP Nodes	64.8	68.9	53.1
Peng et al. (2016)	63.1	-	-
Li et al. (2016b)	67.3	58.9	-
Panyam et al. (2018)	60.3	65.1	45.7
Zheng et al. (2018)	61.5	-	-
the latent structure induced by our model is indeed capable of synthesizing the information across multiple sentences of a document.
Furthermore, LSR with GloVe also proves better in the inter-sentence setting compared with two BERT-based (Wang et al., 2019) models, indicating latent structure's superiority in resolving long-range dependencies across the whole document compared with the BERT encoder.
3.5 Results on the Biomedical Datasets
Table 3 depicts the comparisons with state-of-the-art models on the CDR dataset. Gu et al. (2017); Nguyen and Verspoor (2018); Verga et al. (2018) leverage sequence-based models. Convolutional neural networks and self-attention networks are used as the encoders. Sahu et al. (2019); Christopoulou et al. (2019) use graph-based models. As shown in Table 3, our LSR performs worse than the state-of-the-art models. It is challenging for an off-the-shelf parser to get high quality dependency trees in the biomedical domain, as we observe that the MDP nodes extracted by the spaCy parser from the CDR dataset contains much less informative context compared with the nodes from DocRED. Here we introduce a simplified LSR model indicated as "LSR w/o MDP Nodes", which removes the MDP nodes and builds a fully-connected graph using all tokens of a document. It shows that "LSR w/o MDP Nodes" consistently outperforms sequence-based and graph-based models, indicating the effectiveness the latent structure. Moreover, the simplified LSR outperforms most of the models with external resources, except for Li et al. (2016b), which leverages co-training with additional unlabeled training data. We believe such a setting also benefits our LSR model.
Table 3: Results on the test set of the CDR dataset. The methods below the double line take advantage of additional training data and/or incorporate external tools.
Model	F1	Intra-F1	Inter-F1
NoInf (Christopoulou et al., 2019)	74.6	79.1	49.3
Full (Christopoulou et al., 2019)	80.8	84.1	54.7
EoG (Christopoulou et al., 2019)	81.5	85.2	50.0
LSR	79.6	83.1	49.6
LSR w/o MDP Nodes	82.2	85.4	51.1
Table 4: Results on the test set of the GDA dataset.
Figure 4: Intra- and inter-sentence  $F1$  for different graph structures in QAGCN, EoG, AGGCN and LSR. The number of refinements is ranging from 1 to 4.
Table 4 shows the results on the distantly supervised GDA dataset. Here "Full" indicates EoG model with a fully connected graph as the inputs, while "NoInf" is a variant of EoG model without inference component (Christopoulou et al., 2018). The simplified LSR model achieves the new state-of-the-art result on GDA. The "Full" model (Christopoulou et al., 2019) yields a higher  $F1$  score on the inter-sentence setting while having a relatively low score on the intra-sentence. It is likely because that this model neglects the differences between relations expressed within the sentence and across sentences.
In this subsection, we use the development set of DocRED to demonstrate the effectiveness of the latent structure and refinements.
3.6.1 Does Latent Structure Matter?
We investigate the extent to which the latent structures, that are induced and iteratively refined by the proposed dynamic reasoner, help to improve the overall performance. We experiment with the three different structures defined below. For fair comparisons, we use the same GCN model to perform multi-hop reasoning for all these structures.
Rule-based Structure: We use the rule-based structure in EoG (Christopoulou et al., 2019). Also,
Figure 5: Case study of an example from the development set of DocRED. We visualize the reasoning process for predicting the relation of an entity pair  $\langle \text{Japan, World War II} \rangle$  by LSR and AGGCN in two refinement steps, using the attention scores of the mention World War II in each step. We scale all attention scores by 1000 to illustrate them more clearly. Some sentences are omitted due to space limitation.
We adapt rules from De Cao et al. (2019) for multi-hop question answering, i.e., each mention node is connected to its entity node and to the same mention nodes across sentences, while mention nodes and MDP nodes which reside in the same sentence are fully connected. The model is termed QAGCN.
Attention-based Structure: This structure is induced by AGGCN (Guo et al., 2019a) with multihead attention (Vaswani et al., 2017). We extend the model from sentence-level to document-level.
We explore multiple settings of these models with different block numbers ranging from 1 to 4, where a block is composed of a graph construction component and a densely connected GCN component. As shown in Figure 4, LSR outperforms QAGCN, EoG and AGGCN in terms of overall  $F_{1}$ . This empirically confirms our hypothesis that the latent structure induced by LSR is able to capture a more informative context for the entire document.
3.6.2 Does Refinement Matter?
As shown in Figure 4, our LSR yields the best performance in the second refinement, outperforming the first induction by  $0.72\%$  in terms of overall  $F_{1}$ . This indicates that the proposed LSR is able to induce more accurate structures by iterative refinement. However, too many iterations may lead to an  $F_{1}$  drop due to over-fitting.
3.7 Ablation Study
Table 5 shows  $F_{1}$  scores of the full LSR model and with different components turned off one at
Model	F1	Intra-F1	Inter-F1
Full model	55.17	60.83	48.35
- 1 Refinement	54.42	60.46	47.67
- 2 Structure Induction	51.91	58.08	45.04
- 1 Multi-hop Reasoning	54.49	59.75	47.49
- 2 Multi-hop Reasoning	54.24	60.58	47.15
- MDP nodes	54.20	60.54	47.12
Table 5: Ablation study of LSR on DocRED.
a time. We observe that most of the components contribute to the main model, as the performance deteriorates with any of the components missing. The most significant difference is visible in the structure induction module. Removal of structure induction part leads to a 3.26 drop in terms of  $F_{1}$  score. This result indicates that the latent structure plays a key role in the overall performance.
3.8 Case Study
In Figure 5, we present a case study to analyze why the latent structure induced by our proposed LSR performs better than the structures learned by AGCN. We use the entity World War II to illustrate the reasoning process and our goal here is to predict the relation of the entity pair  $\langle$  Japan, World War II  $\rangle$ . As shown in Figure 5, in the first refinement of LSR, Word War II interacts with several local mentions with higher attention scores, e.g., 0.43 for the mention Lake Force, which will be used as a bridge between the mention Japan and World War II. In the second refinement, the attention scores of several non-local mentions, such as Japan and Imperial Japanese Army, significantly increase from
0.09 to 0.41, and 0.17 to 0.37, respectively, indicating that information is propagated globally at this step. With such intra- and inter-sentence structures, the relation of the entity pair  $\langle$  Japan, World War II  $\rangle$  can be predicted as "participant of", which is denoted by  $P1344$ . Compared with LSR, the attention scores learned by AGGCN are much more balanced, indicating that the model may not be able to construct an informative structure for inference, e.g., the highest score is 0.27 in the second head, and most of the scores are near 0.11.
We also depict the predicted relations of ContextAware, AGGCN and LSR on the graph on the right side of the Figure 5. Interested reader could refer to (Yao et al., 2019) for the definition of a relation, such as  $P607$ ,  $P17$ , etc. The LSR model proves capable of filling out the missing relation for  $\langle \text{Japan, World War II} \rangle$  that requires reasoning across sentences. However, LSR also attends to the mention New Ireland with a high score, thus failing to predict that the entity pair  $\langle \text{New Ireland, World War II} \rangle$  actually has no relation (NIL type).
Document-level relation extraction. Early efforts focus on predicting relations between entities within a single sentence by modeling interactions in the input sequence (Zeng et al., 2014; Wang et al., 2016; Zhou et al., 2016; Zhang et al., 2017; Guo et al., 2020) or the corresponding dependency tree (Xu et al., 2015a,b; Liu et al., 2015; Miwa and Bansal, 2016; Zhang et al., 2018). These approaches do not consider interactions across mentions and ignore relations expressed across sentence boundaries. Recent work begins to explore cross-sentence extraction (Quirk and Poon, 2017; Peng et al., 2017; Gupta et al., 2018; Song et al., 2018c, 2019). Instead of using discourse structure understanding techniques (Liu et al., 2019a; Lei et al., 2017, 2018), these approaches leverage the dependency graph to capture inter-sentence interactions, and their scope is still limited to several sentences. More recently, the extraction scope has been expanded to the entire document (Verga et al., 2018; Jia et al., 2019; Sahu et al., 2019; Christopoulou et al., 2019) in the biomedical domain by only considering a few relations among chemicals. Unlike previous work, we focus on document-level relation extraction datasets (Yao et al., 2019; Li et al., 2016a; Wu et al., 2019) from different domains with a large number of relations
and entities, which require understanding a document and performing multi-hop reasoning.
Structure-based relational reasoning. Structural information has been widely used for relational reasoning in various NLP applications including question answering (Dhingra et al., 2018; De Cao et al., 2019; Song et al., 2018a) and relation extraction (Sahu et al., 2019; Christopoulou et al., 2019). Song et al. (2018a) and (De Cao et al., 2019) leverage co-reference information and set of rules to construct document-level entity graph. GCNs (Kipf and Welling, 2017) or GRNs (Song et al., 2018b) are applied to perform reasoning for multi-hop question answering (Welbl et al., 2018). Sahu et al. (2019) also utilize co-reference links to construct the dependency graph and use labelled edge GCNs (Marcheggiani and Titov, 2017) for document-level relation extraction. Instead of using GNNs, Christopoulou et al. (2019) use the edge-oriented model (Christopoulou et al., 2018) for logical inference based on a heterogeneous graph constructed by heuristics. Unlike previous approaches that use syntactic trees, co-references or heuristics, LSR model treats the document-level structure as a latent variable and induces it in an iteratively refined fashion, allowing the model to dynamically construct the graph for better relational reasoning.
We introduce a novel latent structure refinement (LSR) model for better reasoning in the document-level relation extraction task. Unlike previous approaches that rely on syntactic trees, co-references or heuristics, LSR dynamically learns a document-level structure and makes predictions in an end-to-end fashion. There are multiple avenues for future work. One possible direction is to extend the scope of structure induction for constructions of nodes without relying on an external parser.
We would like to thank the anonymous reviewers for their thoughtful and constructive comments. This research is supported by Ministry of Education, Singapore, under its Academic Research Fund (AcRF) Tier 2 Programme (MOE AcRF Tier 2 Award No: MOE2017-T2-1-156). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the Ministry of Education, Singapore.
Razvan C. Bunescu and Raymond J. Mooney. 2005. A shortest path dependency kernel for relation extraction. In Proc. of EMNLP.
Rui Cai, Xiaodong Zhang, and Houfeng Wang. 2016. Bidirectional recurrent convolutional neural network for relation classification. In Proc. of ACL.
Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2018. A walk-based model on entity graphs for relation extraction. In Proc. of ACL.
Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2019. Connecting the dots: Document-level neural relation extraction with edge-oriented graphs. In Proc. of EMNLP.
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2019. Question answering by reasoning across documents with graph convolutional networks. In Proc. of NAACL-HLT.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proc. of NAACL-HLT.
Bhuwan Dhingra, Qiao Jin, Zhilin Yang, William Cohen, and Ruslan Salakhutdinov. 2018. Neural models for reasoning over multiple mentions using coreference. In Proc. of NAACL-HLT.
George R. Doddington, Alexis Mitchell, Mark A. Przybocki, Lance A. Ramshaw, Stephanie Strassel, and Ralph M. Weischedel. 2004. The automatic content extraction (ace) program - tasks, data, and evaluation. In Proc. of LREC.
Elisa Ferracane, Greg Durrett, Junyi Jessy Li, and Katrin Erk. 2019. Evaluating discourse in structured text representations. In Proc. of ACL.
Jinghang Gu, Fuqing Sun, Longhua Qian, and Guodong Zhou. 2017. Chemical-induced disease relation extraction via convolutional neural network. Database: The Journal of Biological Databases and Curation, 2017.
Zhijiang Guo, Guoshun Nan, Wei Lu, and Shay B. Cohen. 2020. Learning latent forests for medical relation extraction. In Proc. of IJCAI.
Zhijiang Guo, Yan Zhang, and Wei Lu. 2019a. Attention guided graph convolutional networks for relation extraction. In Proc. of ACL.
Zhijiang Guo, Yan Zhang, Zhiyang Teng, and Wei Lu. 2019b. Densely connected graph convolutional networks for graph-to-sequence learning. Transactions of the Association for Computational Linguistics, 7:297-312.
Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Bernt Andrassy, and Thomas A. Runkler. 2018. Neural relation extraction within and across sentence boundaries. In Proc. of AAAI.
Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid O Seaghda, Marco Pennacchiotti, Lorenzo Romano, and Stan Szpakowicz. 2009. Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In Proc. of NAACL-HLT.
Robin Jia, Cliff Wong, and Hoifung Poon. 2019. Document-level n-ary relation extraction with multiscale representation learning. In Proc. of NAACL-HLT.
Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush. 2017. Structured attention networks. In Proc. of ICLR.
Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proc. of ICLR.
Terry K Koo, Amir Globerson, Xavier Carreras, and Michael Collins. 2007. Structured prediction models via the matrix-tree theorem. In Proc. of EMNLP-ConNLL.
Wenqiang Lei, Xuancong Wang, Meichun Liu, Ilija Ilievski, Xiangnan He, and Min-Yen Kan. 2017. Swim: A simple word interaction model for implicit discourse relation recognition. In Proc of. IJCAI.
Wenqiang Lei, Yuanxin Xiang, Yuwei Wang, Qian Zhong, Meichun Liu, and Min-Yen Kan. 2018. Linguistic properties matter for implicit discourse relation recognition: Combining semantic interaction, topic continuity and attribution. In Proc of AAAI.
Jiao Li, Yueping Sun, Robin J. Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J. Mattingly, Thomas C. Wiegers, and Zhiyong Lu. 2016a. Biocreative v cdr task corpus: a resource for chemical disease relation extraction. Database : the journal of biological databases and curation, 2016.
Zhiheng Li, Zhihao Yang, Hongfei Lin, Jian Wang, Yingyi Gui, Yin Zhang, and Lei Wang. 2016b. Cidextractor: A chemical-induced disease relation extraction system for biomedical literature. In Proc. of BIBM.
Bill Yuchen Lin, Xinyue Chen, Jamin Chen, and Xi-ang Ren. 2019. _Kagnet: Knowledge-aware graph networks for commonsense reasoning_. In _Proc. of EMNLP_
Jiangming Liu, Shay B. Cohen, and Mirella Lapata. 2019a. Discourse representation parsing for sentences and documents. In Proc of ACL.
Yang Liu and Mirella Lapata. 2018. Learning structured text representations. Transactions of the Association for Computational Linguistics, 6:63-75.
Yang Liu, Ivan Titov, and Mirella Lapata. 2019b. Single document summarization as tree induction. In Proc. of NAACL-HLT.
Yang Liu, Furu Wei, Sujian Li, Heng Ji, Ming Zhou, and Houfeng Wang. 2015. A dependency-based neural network for relation classification. In Proc. of ACL.
Diego Marcheggiani and Ivan Titov. 2017. Encoding sentences with graph convolutional networks for semantic role labeling. In Proc. of EMNLP.
Makoto Miwa and Mohit Bansal. 2016. End-to-end relation extraction using lstms on sequences and tree structures. In Proc. of ACL.
Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Proc. of ICML.
Dat Quoc Nguyen and Karin M. Verspoor. 2018. Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings. In Proc. of BioNLP.
Nagesh C Panyam, Karin Verspoor, Trevor Cohn, and Kotagiri Ramamohanarao. 2018. Exploiting graph kernels for high performance biomedical relation extraction. Journal of Biomedical Semantics, 9(1):7.
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary Devito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in pytorch.
Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen tau Yih. 2017. Cross-sentence n-ary relation extraction with graph lstms. Transactions of the Association for Computational Linguistics, 5:101-115.
Yifan Peng, Chih-Hsuan Wei, and Zhiyong Lu. 2016. Improving chemical disease relation extraction with rich features and weakly labeled data. Journal of Cheminformatics, 8.
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Proc. of EMNLP.
Chris Quirk and Hoifung Poon. 2017. Distant supervision for relation extraction beyond the sentence boundary. In Proc. of EACL.
Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010. Modeling relations and their mentions without labeled text. In Proc. of ECML/PKDD.
Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2019. Inter-sentence relation extraction with document-level graph convolutional neural network. In Proc. of ACL.
Mike Schuster and Kuldip K. Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Trans. Signal Processing, 45:2673-2681.
Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, and Daniel Gildea. 2018a. Exploring graph-structured passage representation for multihop reading comprehension with graph neural networks. arXiv preprint arXiv:1809.02040.
Linfeng Song, Yue Zhang, Daniel Gildea, Mo Yu, Zhiguo Wang, and Jinsong Su. 2019. Leveraging dependency forest for neural medical relation extraction. In Proc. of EMNLP-IJCNLP.
Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2018b. A graph-to-sequence model for AMR-to-text generation. In Proc. of ACL.
Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2018c. N-ary relation extraction using graph state lstm. In Proc. of EMNLP.
Daniil Sorokin and Iryna Gurevych. 2017. Context-aware representations for knowledge base relation extraction. In Proc. of EMNLP.
Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati, and Christopher D. Manning. 2012. Multi-instance multi-label learning for relation extraction. In Proc. of EMNLP-CoNLL.
Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, and Bowen Zhou. 2019. Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs. In Proc. of ACL.
William Thomas Tutte. 1984. Graph theory. Clarendon Press.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proc. of NeurIPS.
Petar Velicković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph Attention Networks. In Proc. of ICLR.
Patrick Verga, Emma Strubell, and Andrew McCallum. 2018. Simultaneously self-attending to all mentions for fullabstract biological relation extraction. In Proc. of NAACL-HLT.
Hong Wang, Christfried Focke, Rob Sylvester, Nilesh Mishra, and William Wang. 2019. Fine-tune bert for docred with two-step process. arXiv preprint arXiv:1909.11898.
Linlin Wang, Zhu Cao, Gerard de Melo, and Zhiyuan Liu. 2016. Relation classification via multi-level attention cnns. In Proc. of ACL.
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018. Constructing datasets for multi-hop reading comprehension across documents. Transactions of the Association for Computational Linguistics, 6:287-302.