SlowGuess/ACL_2020_OCR huggingface dataset:
Chuan Wang and Nianwen Xue. 2017. Getting the most out of AMR parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1257-1268, Copenhagen, Denmark. Association for Computational Linguistics.
Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, and Wang Ling. 2017. Learning to compose words into sentences with reinforcement learning. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings.
Sheng Zhang, Xutai Ma, Kevin Duh, and Benjamin Van Durme. 2019a. AMR parsing as sequence-tograph transduction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 80-94, Florence, Italy. Association for Computational Linguistics.
Sheng Zhang, Xutai Ma, Kevin Duh, and Benjamin Van Durme. 2019b. Broad-coverage semantic parsing as transduction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3784-3796, Hong Kong, China. Association for Computational Linguistics.
Sheng Zhang, Xutai Ma, Rachel Rudinger, Kevin Duh, and Benjamin Van Durme. 2018a. Cross-lingual decompositional semantic parsing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1664–1675, Brussels, Belgium. Association for Computational Linguistics.
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A.1 Details of Model Structures and Parameters
GloVe embeddings
dim	300
BERT embeddings
source	BERT-base-cased
dim	768
POS tag embeddings
dim	100
CharCNN
num_filters	100
ngram_filter_sizes	[3]
Graph Encoder
gcn Hidden_dim	512
gcn_layers	1
Latent Graph Generator
HardKuma_support	[-0.1, 1.1]
k_dim	64
v_dim	64
n_heads	8
Encoder
hidden_size	512
num_layers	2
Decoder
hidden_size	1024
num_layers	2
Deep Biaffine Classifier
edge Hidden_size	256
label Hidden_size	128
Optimizer
type	Adam
learning_rate	0.001
max_grad_norm	5.0
Coverage loss weight λ	1.0
Beam size	5
Dropout	0.33
Batch Size
train_batch_size	64
test_batch_size	32
Table 5: Hyper-parameter settings
We select the best hyper-parameters under the results of the development set, and we fix the hyperparameters at the test stage. We use two-layer highway LSTM as the encoder and two-layer LSTM as the decoder for the align-free node generator. Table 5 shows the details.
A.2 More Examples
To discuss the generated latent graph in different situations, We provide two examples from the test set on the next page.
Figure 5 gives the analysis of an interrogative sentence: "What advice could you give me?" It shows that the latent graph of the sentence is going to hold the most information between predicates and arguments. Both the AMR root "advice" and the dependency root "give" are paid more attention from other words, and the fused graph retains more information of the predicates and arguments in the original sentence as well.
For a longer sentence with multiple predicate-argument structures, Figure 6 depicts the latent and fused graph of the sentence "You could go to the library on Saturdays and do a good 8 hours of studying there". In this case, the corresponding latent graph becomes shallower, and the AMR root "and" holds most information from other words. Besides, the fused graph indicates that predicates will receive more information from other words, and to some extent, phrases tend to be connected by the fused graph generator.
Sentence:
What advice could you give me?
Dependency:
Latent Matrix:
AMR:
Fused Matrix:
Figure 5: An analysis of the sentence "What advice could you give me?"
Sentence:
You could go to the library on Saturdays and do a good 8 hours of studying there.
Dependency:
Latent Matrix:
Figure 6: An analysis of the sentence "You could go to the library on Saturdays and do a good 8 hours of studying there."