SlowGuess/NAACL_2022_OCR huggingface dataset:
Table 2: The results of sorting order on Dart
Methods	B-4	R-L	CIDEr	Chrf++
With local POS	63.10	80.00	5.40	81.0
With global POS	61.28	79.32	5.28	79.2
Table 3: Performance comparisons of our model with different POS information (local POS and global POS) on WebNLG dataset.
Figure 2: The results of different sliding windows on WebNLG dataset under B-4, R-L, CIDEr and CHrf++ metrics.
provement (comparing "TS" with "NS"). When the size of knowledge graphs is lower than three, these orders have similar performances. This is mainly due to the fact that less triplets have less effect for the model. However, "TS" significantly outperforms "NS" and "RS". Moreover, as the results reported in Table 4, "TS" consistently outperforms "RS" and "NS" in terms of METEOR and MoverScore metrics, and obtains similar performance to "GT" in BERTScore and BLEURT metrics.
6 Sliding Window Size in Semantic Context Scoring
As the B-4 result reported in the main paper, the other metric results in Figure 2 also show that the model achieves the best performance when the sliding window size is 3.
7 Global and Local POS Information
We further introduce two forms of POS tag information, i.e., the global tag information (the final last hidden state of POS generator) and local tag information (the last hidden states of POS generator at each time step). The results of the comparison are reported in Table 3.
We can observe that the model equipping with local POS information performs better than that of equipping with global information. This is mainly because the local POS information can provide more fine-grained syntactic information. Thus, it can further ensure the authenticity of the generated sentences.
Junyi Li, Tianyi Tang, Wayne Xin Zhao, Zhicheng Wei, Nicholas Jing Yuan, and Ji-Rong Wen. 2021. Few-shot knowledge graph-to-text generation with pretrained language models. arXiv preprint arXiv:2106.01623.
Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, et al. 2020. Dart: Open-domain structured data record to text generation. arXiv preprint arXiv:2007.02871.
Datasets	<3	4-6	>7
Metrics	METEOR	MoverScore	BERTScore	BLEURT	METEOR	MoverScore	BERTScore	BLEURT	METEOR	MoverScore	BERTScore	BLEURT
GT	0.51	0.75	0.97	0.61	0.41	0.57	0.94	0.40	0.40	0.53	0.94	0.39
TS	0.50	0.74	0.96	0.60	0.40	0.56	0.94	0.40	0.39	0.52	0.94	0.38
NS	0.49	0.72	0.97	0.58	0.39	0.54	0.94	0.37	0.38	0.49	0.94	0.38
RS	0.47	0.71	0.97	0.57	0.38	0.52	0.93	0.36	0.37	0.47	0.93	0.35
Table 4: The results (METEOR, MoverScore, BERTScore and BLEURT metrics) of different sorting orders in the three subsets with difference KG sizes on Dart dataset.