SlowGuess/NAACL_2021_OCR huggingface dataset:
Furthermore, with the contextualized word representation, the Syn-LSTM-CRF $_{+\text{BERT}}$  achieves much higher performance improvement than any other method. Our model outperforms the strong baseline model DGLSTM-CRF $_{+\text{ELMO}}$  by 4.83 and 2.54 in terms of  $F_{1}$  ( $p < 10^{-5}$ ) on Catalan and Spanish, respectively.
OntoNotes 5.0 English To understand the generalizability of our model, we evaluate the proposed Syn-LSTM-CRF model on large scale OntoNotes 5.0 datasets. Table 3 shows comparisons of our model with baseline models on English. Our SynLSTM-CRF model outperforms all existing methods with 89.04 in terms of  $F_{1}$  score ( $p < 0.01$ ) compared to DGLSTM-CRF, when GloVE word embeddings are used. Our model outperforms the BiLSTM-CRF model by 1.97 in  $F_{1}$ , BiLSTM-GCN-CRF (Jie and Lu, 2019) model by 0.86. Note that our implemented GCN-BiLSTM-CRF outperforms the previous DGLSTM-CRF (Jie and Lu, 2019) by 0.14 in  $F_{1}$ . Our Syn-LSTM-CRF further brings the improvement to 0.52. Moreover, with the contextualized word representation BERT, our method achieves an  $F_{1}$  score of 90.85 ( $p < 10^{-5}$ ) compared to DGLSTM-CRF $_{+ELMO}$ . Our method outperforms the previous model (Luo et al., 2020), which relies on document-level information, by 0.55 in  $F_{1}$ . Furthermore, the performance improvement on recall is more prominent as compared to precision. This shows that the proposed SynLSTM-CRF is able to extract more entities.
OntoNotes 5.0 Chinese We present the experimental results on the OntoNotes 5.0 Chinese test set in Table 4. Our model still consistently outper
Models	P.	R.	F1
Chiu and Nichols (2016a)	86.04	86.53	86.28
Li et al. (2017)	88.00	86.50	87.21
Strubell et al. (2017)	-	-	86.84
Ghaddar and Langlais (2018)	-	-	87.95
BiLSTM-CRF†	87.21	86.93	87.07
BiLSTM-GCN-CRF†	88.30	88.06	88.18
GCN-BiLSTM-CRF*	88.56	88.76	88.66
DGLSTM-CRF (2019)	88.53	88.50	88.52
Luo et al. (2020)	-	-	87.98
Syn-LSTM-CRF (Ours)	88.96	89.13	89.04
+ Contextualized Word Representation
Akbik et al. (2018)	-	-	89.30
BERT-CRF*	88.42	88.33	88.37
Wolf et al. (2019)*	88.39	90.29	89.33
BiLSTM-CRF+ELMO†	89.14	88.59	88.87
BiLSTM-CRF+BERT*	89.32	90.02	89.67
BiLSTM-GCN-CRF+ELMO†	89.40	89.71	89.55
GCN-BiLSTM-CRF+BERT*	89.34	91.26	90.29
DGLSTM-CRF (2019)+ELMO	89.59	90.17	89.88
DGLSTM-CRF+BERT*	89.63	89.87	89.75
Luo et al. (2020)+BERT	-	-	90.30
Syn-LSTM-CRF+BERT (Ours)	90.14	91.58	90.85
Table 3: Experimental results [%] on OntoNotes 5.0 English test set. The models with * symbol are our implementations. The models with † symbol are retrieved from Jie and Lu (2019). There are also other methods (Li et al., 2020a,b) that use external information, (Yu et al., 2020) use document-level information to encode the sentence, which are not direct comparisons to ours.
forms the baseline models, specifically by 2.04 in  $F_{1}$  compared to BiLSTM-CRF, by 2.39 compared to BiLSTM-GCN-CRF, by 1.86 compared to GCN-BILSTM-CRF and by 1.11 ( $p < 10^{-5}$ ) compared to DGLSTM-CRF when FastText is used. Note that the baseline BiLSTM-GCN-CRF model is 0.35 points worse than BiLSTM-CRF. Such results further confirm the effectiveness of our proposed SynLSTM-CRF for incorporating structured information. We find a similar behavior when the contextualized word representation BERT is used. With the contextualized word representation, we achieve a higher  $F_{1}$  score of 80.20.
Robustness Analysis To study the robustness of our model and check whether our model can regulate the flow of information from the graph-encoded representation, we analyze the influence of the quality of dependency trees. We train and evaluate an additional dependency parser (Dozat and Manning, 2017). Specifically, we train the
Models	P.	R.	F1
Pradhan et al. (2013)	78.20	66.45	71.85
Lattice LSTM (2018)	76.34	77.01	76.67
BiLSTM-CRF†	78.45	74.59	76.47
BiLSTM-GCN-CRF†	76.35	75.89	76.12
GCN-BiLSTM-CRF*	78.30	75.07	76.65
DGLSTM-CRF (2019)	77.40	77.41	77.40
Syn-LSTM-CRF (Ours)	77.95	79.07	78.51
+ Contextualized Word Representation
BERT-CRF*	79.83	79.68	79.75
Wolf et al. (2019)*	77.35	81.74	79.49
BiLSTM-CRF+ELMO†	79.20	79.21	79.20
BiLSTM-CRF+BERT*	78.45	81.24	79.82
BiLSTM-GCN-CRF+ELMO†	78.71	79.29	79.00
GCN-BiLSTM-CRF+BERT*	79.03	80.98	80.00
DGLSTM-CRF (2019)+ELMO	78.86	81.00	79.92
DGLSTM-CRF+BERT*	77.79	81.65	79.67
Syn-LSTM-CRF+BERT (Ours)	78.66	81.80	80.20
Table 4: Experimental results [%] on OntoNotes 5.0 Chinese test set. The models with * symbol are our implementations. The models with † symbol are retrieved from Jie and Lu (2019). There are also other methods (Li et al., 2020a,b) that use external information, which are not direct comparisons to ours.
dependency parser $^6$  on the given training datasets and select the best model based on the dev sets. Then we apply the best model to the test sets to obtain dependency trees. We also train and evaluate our model with random dependency trees. Table 8 presents the comparisons between Syn-LSTM-CRF $_{+ \text{BERT}}$  and DGLSTM-CRF $_{+ \text{ELMO}}$  with given, predicted and random dependency trees. We observe that both models encounter a performance drop when we use the predicted parse trees and random trees. Our performance differences with the given parse trees are relatively smaller than the corresponding differences in DGLSTM-CRF $_{+ \text{ELMO}}$ . Such an observation demonstrates the robustness of our proposed model against structured information from the trees of different quality. It is worthwhile to note that, with the predicted dependencies, our proposed Syn-LSTM-CRF $_{+ \text{BERT}}$  is still able to outperform the strong baseline DGLSTM-CRF $_{+ \text{ELMO}}$  even with the given parse trees on Catalan, English, and Chinese datasets.
To further study the robustness, we conduct an analysis to investigate if the gate  $\mathbf{m}_{\mathbf{t}}$  (Figure 2) has the ability to regulate the flow of information from the graph-encoded representation. Intuitively, the gate  $\mathbf{m}_{t}$  should tend to have a small value when
Figure 4: Left: Catalan, Right: Spanish.  $x$ -axis: the value of gate  $\mathbf{m}_t$ .  $y$ -axis: the number of words.
Figure 5: Left: Catalan, Right: Spanish.  $x$ -axis: sentence length.  $y$ -axis:  $F_{1}$  score (\%). Note that DGLSTM-CRF $_{+\text{ELMO}}$  have better performance compared to DGLSTM-CRF $_{+\text{BERT}}$  based on Table 2, 3, 4.
the quality of the parse tree is not good (e.g., with random trees). We statistically plot the number of words with respect to different gate value ranges  $(\mathbf{m}_t)$ . Figure 4 shows the comparison between the models of using random trees and given trees on Catalan and Spanish<sup>7</sup>. We observe that the gate  $\mathbf{m}_t$  is more likely to open (the value is higher) when we use the given parse trees compared with random parse trees. Such behavior demonstrates that our proposed model can selectively aggregate the information from the graph-encoded representation.
Effect of Sentence Length We compare the performance of our Syn-LSTM-CRF $_{+\text{BERT}}$  with BiLSTM-CRF $_{+\text{BERT}}$  and DGLSTM-CRF $_{+\text{ELMO}}$  models with respect to sentence length, and the results are shown in Figure 5. We observe that the SynLSTM-CRF $_{+\text{BERT}}$  model consistently outperforms the two baseline models on the four languages $^8$ . In particular, although the performance tends to drop as the sentence length increases, our proposed model shows relatively better performance when the sentence length is  $\geq 60$ . This confirms that the proposed Syn-LSTM-CRF $_{+\text{BERT}}$  is able to effectively incorporate structured information. Note that our 2-layer GCN is computed based on the
<sup>7</sup>We found a similar behavior for OntoNotes 5.0 English and Chinese datasets, and the detailed result can be found in the Appendix.
See the Appendix for the results on OntoNotes 5.0 English and Chinese datasets.
The performance of the dependency parser can be found in the Appendix.
Models	Catalan	Spanish	English	Chinese
P.	R.	F1	P.	R.	F1	P.	R.	F1	P.	R.	F1
DGLSTM-CRF+ELMO (Given)	84.71	83.75	84.22	87.79	87.33	87.56	89.59	90.17	89.88	78.86	81.00	79.92
DGLSTM-CRF+ELMO (Predicted)	-	-	82.37	-	-	83.92	-	-	89.64	-	-	79.59
Differences	-	-	-1.85	-	-	-3.64	-	-	-0.24	-	-	-0.33
DGLSTM-CRF+ELMO (Random)	78.99	79.31	79.15	82.11	80.89	81.49	88.80	88.91	88.85	77.68	80.60	79.11
Differences	-5.72	-4.44	-5.07	-5.68	-6.44	-6.07	-0.79	-1.26	-1.03	-1.18	-0.40	-0.81
Syn-LSTM-CRF+BERT (Given)	89.07	89.04	89.05	89.66	90.54	90.10	90.14	91.58	90.85	78.66	81.80	80.20
Syn-LSTM-CRF+BERT (Predicted)	87.33	87.42	87.38	86.50	87.49	86.99	89.91	91.27	90.58	78.86	81.57	80.19
Differences	-1.74	-1.62	-1.67	-3.16	-3.05	-3.11	-0.23	-0.31	-0.27	+0.20	-0.23	-0.01
Syn-LSTM-CRF+BERT (Random)	84.57	85.53	85.05	84.61	86.61	85.59	89.24	90.46	89.84	77.25	81.91	79.51
Differences	-4.50	-3.51	-4.00	-5.05	-3.93	-4.51	-0.90	-1.12	-1.01	-1.41	-0.11	-0.69
dependency trees, which include both short-range dependencies and long-range dependencies. With the graph-encoded representation and the proposed Syn-LSTM-CRF $_{+\text{BERT}}$ , the individual word representation is enhanced by both contextual and structured information. Therefore, for the sentences with length of  $\leq 14$ , we can still observe obvious improvements. The significant performance improvements on the four datasets show the capability of our Syn-LSTM-CRF to capture the structured information despite the sentence length.
Effect of Entity Length We conduct another evaluation on BiLSTM-CRF $_{+\text{BERT}}$ , DGLSTM-CRF $_{+\text{ELMO}}$ , and Syn-LSTM-CRF $_{+\text{BERT}}$  models with respect to entity length  $\in \{1,2,3,4,5,\geq 6\}$  on the four languages. Table 6 shows the performance comparison of two models with respect to entity length. With the structured information, both DGLSTM-CRF $_{+\text{ELMO}}$  and Syn-LSTM-CRF $_{+\text{BERT}}$  achieve better performance compared to BiLSTM-CRF $_{+\text{BERT}}$ . When the length of entity is  $\leq 3$ , Syn-LSTM-CRF $_{+\text{BERT}}$  achieves better results compared to DGLSTM-CRF $_{+\text{ELMO}}$ . This confirms that our proposed method can effectively incorporate the structured information. Our model consistently outperforms BiLSTM-CRF $_{+\text{BERT}}$ , and the performance tends to have more improvements when entities are getting longer except on the Chinese dataset. We note there are some special characteristics of the Chinese language. As mentioned by Jie and Lu (2019), the percentage of entities that are able to perfectly form a sub-tree is only  $92.9\%$  for OntoNotes Chinese, as compared to  $98.5\%$ ,  $100\%$ ,  $100\%$  for OntoNotes English, SemEval Catalan and Spanish. Furthermore, the ratio of long entities is much higher for Catalan and Spanish compared
Table 5: Performance comparison between adopting the given, predicted and random dependencies on SemEval 2010 Task 1 Catalan and Spanish, and OntoNotes 5.0 English and Chinese datasets. Note that DGLSTM-CRF $_{+ELMO}$  have better performance compared to DGLSTM-CRF $_{+BERT}$  based on Table 2, 3, 4.
Dataset	Model	Entity Length
1	2	3	4	5	≥6
Catalan	BiLSTM-CRF+BERT	82.4	84.4	77.8	53.3	31.8	36.2
DGLSTM-CRF+ELMO	85.4	85.1	84.1	78.9	60.9	59.3
Syn-LSTM-CRF+BERT	90.5	91.1	87.2	77.8	63.8	60.6
Spanish	BiLSTM-CRF+BERT	85.1	84.2	81.5	33.7	43.1	27.2
DGLSTM-CRF+ELMO	89.3	87.4	90.8	74.1	67.7	64.4
Syn-LSTM-CRF+BERT	92.7	90.9	91.1	73.0	75.4	58.5
English	BiLSTM-CRF+BERT	92.9	88.3	83.1	85.5	80.5	77.9
DGLSTM-CRF+ELMO	91.8	90.1	85.4	87.0	80.8	78.7
Syn-LSTM-CRF+BERT	92.9	90.8	87.7	87.4	80.6	79.8
Chinese	BiLSTM-CRF+BERT	82.5	74.6	71.4	65.0	69.8	52.5
DGLSTM-CRF+ELMO	82.2	75.5	71.8	64.1	58.5	41.1
Syn-LSTM-CRF+BERT	82.5	75.6	73.1	66.4	66.1	42.5
Table 6:  $F_{1}$  score  $[\%]$  based on entity length on Catalan, Spanish, English and Chinese datasets. Note that DGLSTM-CRF $_{+\mathrm{ELMO}}$  have better performance compared to DGLSTM-CRF $_{+\mathrm{BERT}}$  based on the results in the main paper.
to English and Chinese. The experimental results on Catalan and Spanish datasets show significant improvements for long entities. Such results show that the structured information conveyed by the dependency trees can be more crucial when entity length becomes longer.
Number of GCN Layers To fully explore the impact of the number of GCN layers, we conduct another experiment on Syn-LSTM-CRF $_{+ \text{BERT}}$  model with the number of GCN layers  $\in \{1, 2, 3\}$ , and Figure 6 shows the performance on the dev set of the four languages. The last bar, indicated as AVG, is obtained by averaging the dev results on the four datasets. We observe that the overall performance is better when the number of GCN layers equals 2. Note that similar behavior can also be found in the work by Kipf and Welling (2017) for document classification and node classification. Therefore, we evaluate our proposed Syn-LSTM-CRF model with 2-layer GCN.
Figure 6: Performance of different number of layers of GCN on dev set.
Model	P.	R.	F1
Syn-LSTM-CRF+BERT	90.14	91.58	90.85
-1 layer GCN	89.93	91.30	90.61
-2 layer GCN	89.50	89.93	89.72
- original dependency	89.91	91.27	90.58
-dependency embedding	89.85	91.31	90.58
- POS embedding	89.84	90.95	90.46
Table 7: Ablation study of the Syn-LSTM-CRF $_{+\text{BERT}}$  model on OntoNotes 5.0 English. - means removing.
Ablation Study To understand the contribution of each component, we conduct an ablation study on the OntoNotes 5.0 English dataset, and Table 7 presents the detailed results of our model with contextualized representation. We find that the performance drops by  $0.24F_{1}$  score when we only use 1-layer GCN. Without GCN at all, the score drops by  $1.13F_{1}$ . The original dependency contributes  $0.27F_{1}$  score. Removing the dependency relation embedding also decreases the performance by  $0.27F_{1}$ . When we remove the POS tags embedding, the result drops by  $0.39F_{1}$ .
LSTM LSTM has demonstrated its great effectiveness in many NLP tasks and becomes a standard module for many state-of-the-art models (Wen et al., 2015; Ma and Hovy, 2016; Dozat and Manning, 2017). However, the sequential nature of the LSTM makes it challenging to capture long-range dependencies. Zhang et al. (2018a) propose the S-LSTM model to include a sentence state to allow both local and global information exchange simultaneously. Mogrifier LSTM (Melis et al., 2020) mutually gates the current input and the previous output to enhance the interaction between the input and the context. These two works do not consider structured information for the LSTM design. Since natural language is usually structured, Shen et al.
(2018) propose ON-LSTM to add a hierarchical bias to allow the neurons to be updated by following certain order. While the ON-LSTM is learning the latent constituency parse trees, we focus on incorporating the explicit structured information conveyed by the dependency parse trees.
NER Early work (Sasano and Kurohashi, 2008) uses syntactic dependency features to improve the SVM performance on Japanese NER task. Liu et al. (2010) propose to construct skip-edges to link similar words or words having typed dependencies to capture long-range dependencies. The later works (Collobert et al., 2010; Lample et al., 2016; Chiu and Nichols, 2016b) focus on using neural networks to extract features and achieved the state-of-the-art performance. Jie et al. (2017) find that some relations between the dependency edges and the entities can be used to reduce the search space of their model, which significantly reduces the time complexity. Yu et al. (2020) employ pre-trained language model to encode document-level information to explore all spans with the graph-based dependency graph based ideas. The pre-trained language models (e.g., BERT (Devlin et al., 2019), ELMO (Peters et al., 2018)) further improve neural-based approaches with a good contextualized representation. However, previous works did not focus on investigating how to effectively integrate structured and contextual information well.
In this paper, we propose a simple and robust SynLSTM model to better integrate the structured information leveraged from the long-range dependencies. Specifically, we introduce an additional graph-encoded representation to each recurrent unit. Such a graph-encoded representation can be obtained via GNNs. Through the newly designed gating mechanism, the hidden states are enhanced by contextual information captured by the linear sequence and structured information captured by the dependency trees. We present the Syn-LSTM-CRF for NER and adopt the GCN on dependency trees to obtain the graph-encoded representations. Our extensive experiments and analysis on the datasets with four languages demonstrate that the proposed Syn-LSTM is able to effectively incorporate both contextual and structured information. The robustness analysis demonstrates that our model is capable of selectively aggregating the information from the graph-encoded representation.
We would like to thank the anonymous reviewers for their helpful comments. This research is partially 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.
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