D yn GL - SDP : Dynamic Graph Learning for Semantic Dependency Parsing (2022)

Abstract: A recent success in semantic dependency parsing shows that graph neural networks can make significant accuracy improvements, owing to its powerful ability in learning expressive graph representations. However, this work learns graph representations based on a static graph constructed by an existing parser, suffering from two drawbacks: (1) the static graph might be error-prone (e.g., noisy or incomplete), and (2) graph construction stage and graph representation learning stage are disjoint, the errors introduced in the graph construction stage cannot be corrected and might be accumulated to later stages. To address these two drawbacks, we propose a dynamic graph learning framework and apply it to semantic dependency parsing, for jointly learning graph structure and graph representations. Experimental results show that our parser outperforms the previous parsers on the SemEval-2015 Task 18 dataset in three languages (English, Chinese, and Czech).

## introduction semantic dependency parsing (sdp) represents a sentence as a directed acyclic graph (dag), also called semantic dependency graph (sdg), to capture between-word semantic relationships that are more closely related to the meaning of the sentence. sdp has been widely applied in many downstream tasks of natural language processing (nlp), including sentiment analysis @xcite , abstractive summarization @xcite , natural language understanding @xcite , etc. several semantic dependency parsers are presented in recent years. their parsing mechanisms are either transition-based or graph-based. a transition-based parser generates a sequence of transition actions to incrementally build an sdg @xcite @xcite and @xcite . transition-based parsers can parse a sentence efficiently. however, they are more prone to suffer from error propagation since the transition prediction stage is sequential and greedy. graph-based parsers overcome the shortcomings of transition-based parsers. they score each edge (or combination of them) of a possible sdg and globally search for the highestscoring sdg @xcite @xcite @xcite @xcite . a recent success in sdp is the model of @xcite , which is a graph-based model relying on graph neural networks (gnns). their model utilizes an existing parser to construct an initial static graph, and then use gnns to learn node embeddings based on the static graph to predict dependency relationships between words, as shown in figure 1 @xcite . this model achieves state-of-the-art performance in three languages (english, chinese, and czech), owing to the powerful ability of gnns in learning expressive graph representations. despite the promising performance of @xcite , there are still two drawbacks in their model: to address these two drawbacks mentioned above, we propose a dynamic graph learning framework and apply it to sdp, as shown in figure 1 (b), for jointly learning graph structure and graph representations in this paper. two gnns variants, graph convolutional network (gcn) @xcite and graph attention network (gat) @xcite have been investigated in dyngl-sdp. experiments are conducted on the semeval-2015 task 18 dataset in three languages (english, chinese, and czech). experimental results demonstrate the effectiveness of dyngl-sdp, which outperforms previous studies and achieves a new state-of-the-art performance. in addition, dyngl-sdp shows more advantages with respect to parsing speed. contributions the major contributions of this paper are summarized as follows: (1) we propose a dynamic graph learning framework for jointly learning graph structure and graph representations; (2) we apply the framework in sdp to propose a graph-based semantic dependency parser; (3) we conduct sufficient experiments and show that our parser achieves a new state-of-the-art result in three languages. our code is publicly available at https://github.com/libinnlp/ dyngl-sdp . ## related work in this section, the studies of sdp and dynamic graph learning will be summarized as follows. ## dyngl-sdp dyngl-sdp is a graph-based parser using the dynamic graph learning framework. an overview of dyngl-sdp is shown as figure 2 . given sentence s with n words [w 1 , w 2 , . . . , w n ], there are four stages to parse it as an sdg: (1) contextualized representation learning-a bidirectional long shortterm memory network (bilstm) is used to learn the contextualized representation of each word; (2) graph structure learning-a graph structure learning module is used to learn the adjacency matrix of a potential sdg; (3) graph representation learningthe contextualized representations and the learned adjacency matrix will be fed into the gnns, to learn expressive node embeddings; (4) dependency relationship learning-the concatenation of node embeddings and contextualized representations are fed into biaffine attention-based parser, to learn dependency relationships between words. ## conclusions in this paper, we propose a dynamic graph learning framework and apply it in semantic dependency parsing. experimental results show that our model achieves a new state-of-the-art performance on the semeval-2015 task 18 dataset in three languages (english, chinese, and czech). the outstanding performance of our model demonstrates that the proposed dynamic graph learning framework is able to learn the expressive graph representations without depending on an initial static graph.