SlowGuess/NAACL_2022_OCR huggingface dataset:
2https://www.cs.cmu.edu/~ark/SEMAFOR/ eval/
Model	Full structure F1	Arg F1
KID (GloVe)	75.28	66.35
w/o FSG	74.43	64.99
w/o FKG	74.60	64.96
w/o double-graph	73.34	63.41
KID (BERT w/o exemplar)	79.44	71.59
w/o FSG	78.84	70.63
w/o FKG	79.29	70.86
w/o double-graph	77.77	68.77
Table 3: Ablation study on double-graph architecture. w/o denotes "without". w/o FSG uses LSTM as its decoder and w/o FKG does not use RGCN to encode frames and FEs. We also test the influence of double-graph architecture for KID (BERT).
Model	Full structure F1	Arg F1
KID (GloVe)	75.28	66.35
w/o frame-FE	74.84	65.70
w/o frame-frame	74.97	66.06
w/o intra-frame FE-FE	75.10	66.60
w/o inter-frame FE-FE	75.13	65.87
FI w/o FKG	75.00	65.61
w/o FKG	74.60	64.96
sequence of arguments and their roles that have already been identified as input to predict the next argument. FSG performs better than LSTM because it captures target-argument and argument-argument relations and can model long-distance dependencies. w/o FKG directly uses input vectors of frame knowledge graph encoder, and results also show that knowledge-enhanced representations are better than randomly initialized embeddings. We also test the influence of double-graph structure with pre-trained language models, the results show the double-graph structure is still effective and useful even with the pre-trained language models.
FKG is a multi-relational heterogeneous graph. The ablation study on structures of FKG is shown in Table 4. In addition, we evaluate the performance of FI w/o FKG, which identifies frames with a simple linear classification layer instead of FKG, and the results prove that FKG strengthens interactions between frame identification and role classification.
In addition, we explore the effectiveness of name information of FEs. Whether the name information is used in previous work is unclear and some
Table 4: Ablation study on structures of FKG. We remove each kind of relations of FKG and all get a drop of full structure F1. FI w/o FKG denotes not using FKG in frame identification (FI). w/o FKG uses input vectors of frame knowledge encoder directly.
Model	Full structure F1	Arg F1
KID (GloVe)	75.28	66.35
w/o FKG	74.60	64.96
w/o name information	74.46	64.86
w/o both	73.53	64.01
Table 5: Ablation study on name information. Sharing embeddings for FEs whose names are the same are quite useful. The performance will drop a lot if we remove both name information and FKG, which reveals the importance of FKG.
Model	K
0	4	16	32	full
KID (GloVe)	56.26	63.28	65.32	65.95	70.32
w/o FKG	0.00	50.70	56.40	57.59	63.94
Δ	56.26	12.58	8.92	8.36	6.38
Table 6: Experiments on confirming transfer learning ability of FKG.  $K$  denotes the number of instances of each frame in training set. Full means adding all instances of these frames except those including target get in train and development sets. Lack of labeled instances has much less impact on Arg F1 performance of KID with FKG, which confirms our assumption.
BIO-based approaches like Marcheggiani and Titov (2020) are likely to use name information by regarding FEs with the same name as the same label in role classification. To the best of our knowledge, we are the first one to study the effectiveness of name information. As shown in Table 5, the names of FEs provide rich information for frame semantic parsing and shared embedding strategy can make good use of the name information. Further ablation study of FKG is conducted under the circumstance without name information, the performance will drop 0.9 points if we remove FKG too, showing that knowledge-enhanced representations are important no matter whether we share embeddings for FEs with the same names or not.
6.4 Transfer learning ability of FKG
As we have discussed in Figure 2, if frame B is related to frame A, a sentence with frame A can contribute to parsing another sentence with frame B by inter-frame reasoning. Frame-frame and inter-frame FE-FE relations of FKG can guide KID to learn experience from other frames.
To confirm that FKG has ability of transfer learning, we design zero (few)-shot learning experiments on FN 1.7. Target word get can evoke multiple frames in FrameNet, and we choose instances
including target get with three frames (Arriving, Getting and Transition_to_state) as test instances. We remove all instances with target get from train and development instances, and can selectively add few (or zero) instances including other targets with these three frames into train and development sets. We then compare the performance of KID with KID w/o FKG under zero-shot and few-shot circumstances. If FKG has ability of transfer learning, KID with FKG can learn experience from other related frames like Receiving and its performance will not be influenced so much by the sparsity of labels.
Table 6 shows the results of our experiments.  $K = 0$  indicates zero-shot learning while  $K = \{4,16,32\}$  indicates few-shot learning. KID without FKG performs much worse in zero-shot learning. As the number of instances that can be seen in training grows up, the performance of KID with FKG gets a steady increase while the performance of KID without FKG increases rapidly. Results verify our assumption that even with few train instances FKG can guide inter-frame reasoning with its structure and allow models to learn experience from other seen frames.
Frame semantic parsing has caught wide attention since it was released on SemEval 2007 (Baker et al., 2007). The task is to extract frame structures defined in FrameNet (Baker et al., 1998) from text. From then on, a large amount of systems are applied on this task, ranging from traditional machine learning classifiers (Johansson and Nugues, 2007; Das et al., 2010) to fancy neural models like recurrent neural networks (Yang and Mitchell, 2017; Swayamdipta et al., 2017) and graph neural networks (Marcheggiani and Titov, 2020; Bastianelli et al., 2020).
A lot of previous systems neglect interactions between subtasks and relations between arguments. They either focus on one or two subtasks (Hermann et al., 2014; FitzGerald et al., 2015; Marcheggiani and Titov, 2020) of frame semantic parsing or treat all subtasks independently (Das et al., 2014; Peng et al., 2018). Täckström et al. (2015) propose an efficient global graphical model, so they can enumerate all possible argument spans and treat the assignment as the Integer Linear Programming problem. Later systems like FitzGerald et al. (2015); Peng et al. (2018) follow this method. Swayamdipta
et al. (2017); Bastianelli et al. (2020) use sequence-labeling strategy, and Yang and Mitchell (2017) integrate these two methods with a joint model. Only few approaches like Chen et al. (2021) model interactions between subtasks, which use the encoder-decoder architecture to predict arguments and roles sequentially. However, the sequence modeling of Chen et al. (2021) does not consider structure information and is not good at capturing long-distance dependencies. We use graph modeling to enhance structure information and strengthen interactions between target and argument, argument and argument.
Only a few systems utilize linguistic knowledge in FrameNet. Kshirsagar et al. (2015) use FE mappings to share information in FEs. In frame identification, Jiang and Riloff (2021) encode definitions of frames and Su et al. (2021) use frame identification and frame semantic relations. However, they do not utilize ontological frame knowledge in all subtasks while we construct a heterogeneous graph containing both frames and FEs. Besides, our model does not need extra encoders to encode definitions, which reduces parameters of the model.
Some systems also treat constituency parsing or other semantic parsing tasks like AMR as a graph construction problem. Yang and Deng (2020) use GCN to encode intermediate constituency tree to generate a new action on the tree. Cai and Lam (2020) construct AMR graphs with the Transformer (Vaswani et al., 2017) architecture.
In this paper, we incorporate knowledge into frame semantic parsing by constructing Frame Knowledge Graph. FKG provides knowledge-enhanced representations of frames and FEs and can guide intra-frame and inter-frame reasoning. We also propose frame semantic graph to represent frame semantic structures. We regard frame semantic parsing as an incremental graph construction problem. The process to construct FSG is structure-aware and can utilize relations between arguments. Our framework Knowledge-guided Incremental semantic parser with Double-graph (KID) achieves state-of-the-art on FrameNet benchmarks. However, how to utilize linguistic knowledge better is still to be resolved. Future work can focus on better modeling of ontological frame knowledge, which will be useful for frame semantic parsing and transfer learning in frame semantic parsing.
This paper is supported by the National Science Foundation of China under Grant No.61936012, 61876004 and the National Key Research and Development Program of China under Grant No. 2020AAA0106700.
Collin Baker, Michael Ellsworth, and Katrin Erk. 2007. SemEval-2007 task 19: Frame semantic structure extraction. In Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), pages 99–104, Prague, Czech Republic. Association for Computational Linguistics.
Collin F. Baker, Charles J. Fillmore, and John B. Lowe. 1998. The Berkeley FrameNet project. In 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1, pages 86-90, Montreal, Quebec, Canada. Association for Computational Linguistics.
Emanuele Bastianelli, Andrea Vanzo, and Oliver Lemon. 2020. Encoding syntactic constituency paths for frame-semantic parsing with graph convolutional networks. arXiv preprint arXiv:2011.13210.
Deng Cai and Wai Lam. 2020. AMR parsing via graph-sequence iterative inference. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1290-1301, Online. Association for Computational Linguistics.
Xudong Chen, Ce Zheng, and Baobao Chang. 2021. Joint multi-decoder framework with hierarchical pointer network for frame semantic parsing. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2570-2578, Online. Association for Computational Linguistics.
James Cross and Liang Huang. 2016. Span-based constituency parsing with a structure-label system and provably optimal dynamic oracles. arXiv preprint arXiv:1612.06475.
Dipanjan Das, Desai Chen, André F. T. Martins, Nathan Schneider, and Noah A. Smith. 2014. Frame-semantic parsing. Computational Linguistics, 40(1):9-56.
Dipanjan Das, Nathan Schneider, Desai Chen, and Noah A. Smith. 2010. Probabilistic frame-semantic parsing. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 948–956, Los Angeles, California. Association for Computational Linguistics.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of
deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics.
Charles J Fillmore and Collin F Baker. 2001. Frame semantics for text understanding. In Proceedings of WordNet and Other Lexical Resources Workshop, NAACL, volume 6.
Nicholas FitzGerald, Oscar Täckström, Kuzman Ganchev, and Dipanjan Das. 2015. Semantic role labeling with neural network factors. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 960-970, Lisbon, Portugal. Association for Computational Linguistics.
Daniel Gildea and Daniel Jurafsky. 2002. Automatic labeling of semantic roles. Computational linguistics, 28(3):245-288.
Shaoru Guo, Ru Li, Hongye Tan, Xiaoli Li, Yong Guan, Hongyan Zhao, and Yueping Zhang. 2020. A frame-based sentence representation for machine reading comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 891-896, Online. Association for Computational Linguistics.
Prakhar Gupta, Jeffrey Bigham, Yulia Tsvetkov, and Amy Pavel. 2021. Controlling dialogue generation with semantic exemplars. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3018-3029, Online. Association for Computational Linguistics.
Karl Moritz Hermann, Dipanjan Das, Jason Weston, and Kuzman Ganchev. 2014. Semantic frame identification with distributed word representations. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1448-1458, Baltimore, Maryland. Association for Computational Linguistics.
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780.
Tianyu Jiang and Ellen Riloff. 2021. Exploiting definitions for frame identification. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2429-2434, Online. Association for Computational Linguistics.
Richard Johansson and Pierre Nugues. 2007. LTH: Semantic structure extraction using nonprojective dependency trees. In Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), pages 227-230, Prague, Czech Republic. Association for Computational Linguistics.
Aditya Kalyanpur, Or Biran, Tom Breloff, Jennifer Chu-Carroll, Ariel Diertani, Owen Rambow, and Mark Sammons. 2020. Open-domain frame semantic parsing using transformers. arXiv preprint arXiv:2010.10998.
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
Meghana Kshirsagar, Sam Thomson, Nathan Schneider, Jaime Carbonell, Noah A. Smith, and Chris Dyer. 2015. Frame-semantic role labeling with heterogeneous annotations. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 218–224, Beijing, China. Association for Computational Linguistics.
Diego Marcheggiani and Ivan Titov. 2017. Encoding sentences with graph convolutional networks for semantic role labeling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1506-1515, Copenhagen, Denmark. Association for Computational Linguistics.
Diego Marcheggiani and Ivan Titov. 2020. Graph convolutions over constituent trees for syntax-aware semantic role labeling. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3915-3928, Online. Association for Computational Linguistics.
Hiroki Ouchi, Hiroyuki Shindo, and Yuji Matsumoto. 2018. A span selection model for semantic role labeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1630-1642, Brussels, Belgium. Association for Computational Linguistics.
Hao Peng, Sam Thomson, Swabha Swayamdipta, and Noah A. Smith. 2018. Learning joint semantic parsers from disjoint data. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1492-1502, New Orleans, Louisiana. Association for Computational Linguistics.
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543, Doha, Qatar. Association for Computational Linguistics.
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020. Stanza: A python natural language processing toolkit for many human languages. In Proceedings of the 58th Annual Meeting of the Association for Computational
Linguistics: System Demonstrations, pages 101-108, Online. Association for Computational Linguistics.
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In European semantic web conference, pages 593-607. Springer.
Xuefeng Su, Ru Li, Xiaoli Li, Jeff Z. Pan, Hu Zhang, Qinghua Chai, and Xiaoqi Han. 2021. A knowledge-guided framework for frame identification. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5230-5240, Online. Association for Computational Linguistics.
Swabha Swayamdipta, Sam Thomson, Chris Dyer, and Noah A. Smith. 2017. Frame-semantic parsing with softmax-margin segmental rnns and a syntactic scaffold. ArXiv, abs/1706.09528.
Swabha Swayamdipta, Sam Thomson, Kenton Lee, Luke Zettlemoyer, Chris Dyer, and Noah A. Smith. 2018. Syntactic scaffolds for semantic structures. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3772-3782, Brussels, Belgium. Association for Computational Linguistics.
Oscar Täckström, Kuzman Ganchev, and Dipanjan Das. 2015. Efficient inference and structured learning for semantic role labeling. Transactions of the Association for Computational Linguistics, 3:29-41.
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 Advances in neural information processing systems, pages 5998-6008.
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. arXiv preprint arXiv:1506.03134.
Wenhui Wang and Baobao Chang. 2016. Graph-based dependency parsing with bidirectional LSTM. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2306-2315, Berlin, Germany. Association for Computational Linguistics.
Bishan Yang and Tom Mitchell. 2017. A joint sequential and relational model for frame-semantic parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1247–1256, Copenhagen, Denmark. Association for Computational Linguistics.
Kaiyu Yang and Jia Deng. 2020. Strongly incremental constituency parsing with graph neural networks. arXiv preprint arXiv:2010.14568.
Hongyan Zhao, Ru Li, Xiaoli Li, and Hongye Tan. 2020. Cfsre: Context-aware based on frame-semantics for distantly supervised relation extraction. Knowledge-Based Systems, 210:106480.
A.1 Graph Convolutional Network
Graph convolution is introduced in Kipf and Welling (2016). A GCN layer is defined as follow:
$$
f \left(H ^ {(l)}, G\right) = \sigma \left(\tilde {D} ^ {- \frac {1}{2}} \tilde {A} \tilde {D} ^ {- \frac {1}{2}} H ^ {(l)} W ^ {(l)}\right) \tag {18}
$$
where  $H^{(l)}$  denotes hidden representations of nodes in  $l$ -th layer,  $\sigma$  is the non-linear activation function (e.g. ReLU) and  $W$  is the weight matrix.  $\tilde{D}$  and  $\tilde{A}$  are separately the degree and adjacency matrices for the graph  $G$ . From a node-level perspective, a GCN layer can be also formalized as follow:
$$
h _ {i} ^ {(l + 1)} = \sigma \left(\sum_ {j \in N (i)} \frac {1}{c _ {j i}} h _ {j} ^ {(l)} W ^ {(l)}\right) \tag {19}
$$
where  $N(i)$  is the set of neighbors of node  $i$ , and  $c_{ji} = \sqrt{|N(j)|}\sqrt{|N(i)|}$ .
By stacking  $L$  different GCN layers, we get final GCN module  $\mathrm{GCN}(H^{(0)},G)$
A.2 Relational Graph Convolutional Network
Type information of graph edges is ignored in GCN, and RGCN (Schlichtkrull et al., 2018) is proposed to model relational data from which we can benefit to model the multi-relational graph FKG. Different edge types use different weights and only edges of the same relation type  $r$  are associated with the same projection weight  $W_{r}$ . From a node's view:
$$
h _ {i} ^ {(l + 1)} = \sigma \left(h _ {i} ^ {(l)} W _ {0} ^ {(l)} + \sum_ {r \in R} \sum_ {j \in N _ {i} ^ {r}} \frac {1}{c _ {i , r}} h _ {j} ^ {(l)} W _ {r} ^ {(l)}\right) \tag {20}
$$
where  $N_{i}^{r}$  denotes the set of neighbor indices of node  $i$  under relation  $r\in R$  and  $c_{i,r}$  is a normalization constant i.e.  $\left|N_i^r\right|$
In KID, we use tanh as activation function of RGCN for normalization because we need to calculate normalized dot product similarities between frames/FEs and target/arguments.
A.3 Encoding Dependency Tree
We follow previous studies (Marcheggiani and Titov, 2020; Bastianelli et al., 2020) to use syntax structures like dependency tree  $\mathcal{T}$  of  $S$  in KID
Hyper-parameter	Value
batch size	32
learning rate	6e-5/1e-4
lr decay	0.6 per 30 epochs
optimizer	Adam
pretrain epochs	10/20/30/40/50
epoch	100
activation function	ReLU
FFN Layers	2
LSTM Layers	2
GCN Layers	1/2
dh, dn	512, 256
λ1, λ2, λ3	0.1, 0.3, 0.3
Table 7: Hyper-parameter settings of KID (GloVe).
because syntax structure is proved beneficial to semantic parsing. We use Stanza (Qi et al., 2020), an open-source python NLP toolkit to parse dependency syntactic structure for instances, and depGCN (Marcheggiani and Titov, 2017) to encode the syntactic structure. We simplify depGCN by ignoring directions and labels of edges in dependency tree, which means if token  $i$  is head or dependent of token  $j$ , we will have  $A_{ij}^{\mathcal{T}} = A_{ji}^{\mathcal{T}} = 1$  in adjacency matrix  $A^{\mathcal{T}}$  of  $\mathcal{T}$ .
In addition, if we use BERT as encoder, the tokens are sub-word level and the adjacency matrix will be a little bit different. Specifically, if token  $i$  is the sub-word of some word  $u$ , token  $j$  is the sub-word of some word  $v$  and  $u$  is head or dependent word of  $v$ , we will have  $A_{ij}^{\mathcal{T}} = A_{ji}^{\mathcal{T}} = 1$  in adjacency matrix  $A^{\mathcal{T}}$ .
B Hyper-parameter Setting
For replicability of our work, we list hyperparameter settings of KID (GloVe) and KID (BERT) in Table 7 and 8. We use the development set to manually tune the optimal hyper-parameters based on Full structure F1. The values of hyperparameters finally selected are in bold. Token embeddings we use in KID (GloVe) are the same as Chen et al. (2021), including word, lemma and POS tag embeddings with a binary type embedding to distinguish whether a token is a target or not.
We compare KID with following baselines:
Hyper-parameter	Value
bert version	bert-base-uncased
batch size	16
learning rate	6e-6/1e-5
optimizer	BertAdam
pretrain epochs	10/20/30/40/50
epoch	100
activation function	ReLU
FFN Layers	2
LSTM Layers	2
GCN Layers	2
dh, dn	512, 256
λ1, λ2, λ3	0.1, 0.3, 0.3
SEMAFOR: a widely-used open-resource statistical model proposed by Das et al. (2010, 2014).
SEMAFOR (HI): an improved version of SE-MAFOR using exemplar instances and hierarchy features (FE mappings) proposed by Kshirsagar et al. (2015)
Hermann et al. (2014): a neural network-based model learning representations of words and frames.
Täckström et al. (2015): identifying arguments with a global graphical model.
FitzGerald et al. (2015): an extension of Tackström et al. (2015) learning neural representations of frames and FEs.
open-SESAME: a syntax-free open-resource semantic parser proposed by Swayamdipta et al. (2017).
Swayamdipta et al. (2018): an extension version of open-SESAME with multi-task and exemplar instances.
Yang and Mitchell (2017): a joint model integrating both sequential and relational models.
Peng et al. (2018): a joint model using latent structure variables.
Chen et al. (2021): a joint encoder-decoder model predicting arguments and roles sequentially.
Marcheggiani and Titov (2020): a GCN-based model over constituency trees.
Bastianelli et al. (2020) (JL): a GCN-based model encoding syntactic constituency path. JL denotes joint learning on all subtasks of frame semantic parsing.
Kalyanpur et al. (2020): a T5-based model treating frame semantic parsing as a sequence-to-
Table 8: Hyper-parameter settings of KID (BERT).
Model	Precision	Recall	F1-score
Peng et al. (2018)	78.0	72.1	75.0
KID (GloVe)	77.0	79.8	78.4
KID (BERT)	81.1	83.3	82.2