Incorporating EDS Graph for AMR Parsing (2021)

Abstract: AMR (Abstract Meaning Representation) and EDS (Elementary Dependency Structures) are two popular meaning representations in NLP/NLU. AMR is more abstract and conceptual, while EDS is more low level, closer to the lexical structures of the given sentences. It is thus not surprising that EDS parsing is easier than AMR parsing. In this work, we consider using information from EDS parsing to help improve the performance of AMR parsing. We adopt a transition-based parser and propose to add EDS graphs as additional semantic features using a graph encoder composed of LSTM layer and GCN layer. Our experimental results show that the additional information from EDS parsing indeed gives a boost to the performance of the base AMR parser used in our experiments.

## introduction semantic parsing has long been considered a difficult task and an important step to natural language understanding. a number of meaning representation formalisms have been proposed. well-known ones include eds (elementary dependency structures; @xcite , ucca (universal conceptual cognitive annotation; @xcite , and amr (abstract meaning representation; @xcite . among them, amr is more abstract from surface tokens and tries to capture the meaning of a sentence using concepts that may not appear in the sentence. if one views an amr encoding as a graph, the amr graph is always composed of fewer nodes than other meaning representations and some nodes in the amr graph cannot be anchored to tokens or strings of tokens in the sentence. but eds tries to build a meaning representation using lexical terms that are presented in the sentence, and nodes in their parse trees are anchored. in comparison, amr has a much more fine-grained classification for the named entities, total of 124 entity types @xcite . thus not surprisingly, amr parsers do not perform as well as the ones for eds. currently the parsing accuracies for amr are in low 80s, while they can be high 90s for eds. in this paper, we propose to use eds improve the performance of the amr parser. @xcite . take node #3 in amr as an example. "percentageentity" is the node label, "value" is the property of this node, and "19" is the specific value. for node #10 in eds, "<35:37>" indicates the span of the corresponding surface string; "card" is the node label, "carg" which means "constant argument" is the property, and "19" is the value. to see how information from eds parsing can be of use to amr, consider the following sentence "imports were at $50. @xcite . its graph encodings in amr and eds are shown in figure 1 . we mentioned that amr is more abstract. this can be seen in the example as the graph for amr is a lot smaller, and the nodes are labeled with conceptual entities. nevertheless, eds and amr edges are labeled using the same semantic roles (e.g., arg1, arg2), indicating the relationship between a predicate and its arguments @xcite . in this example, there are some correspondences between their nodes. for example, the amr nodes "percentageentity", "dollar", and "import-01" correspond to the eds nodes " percen n of", " dollar n 1", and " import n of", respectively. in our task, the most important feature of eds is anchoring. from the eds graph, each node has a corresponding span of text. conversely, we can find all related eds nodes for each token based on the indexes. this suggests that eds parsing may serve as an intermediate to amr parsing, which motivated this work. to incorporate eds parsing into an amr parser, we propose an eds encoder composed of lstm networks that capture the contextual information and a graph convolutional network (gcn, @xcite that extracts the structure knowledge. we feed eds into our proposed encoder and produce token-level features. these eds token-level features are concatenated to word embedding of tokens and participate in the amr parsing process. to demonstrate the effectiveness of our approach, we use the amr dataset from mrp 2019 @xcite and take as our baseline model the hit-scir @xcite , which was the best overall system at mrp 2019 and the 2nd best for amr. our experimental results show that our eds-enhanced parsers clearly outperform the baseline model. in fact, some of our new models beat the best score of the official submitted amr parsers in this benchmark. we also observed that the biggest improvements happened to be on those test data that are least similar to the training data. the rest of this paper is organized as follows: second 2 gives a brief overview on amr parsers; section 3 is concerned with the baseline system we adopt and our eds-enhanced model. we present experimental settings and experimental results in section 4 and conclude in section 5. ## related work we classify amr parsing systems into grammarbased, graph-based, and transition-based ones. the grammar-based ones generate amr graphs directly from grammar trees. several early amr parsing systems were of this type. for example, @xcite used combinatory categorial grammar (ccg) parsing to construct amr, while @xcite made use of synchronous hyperedge replacement grammar (shrg). generally speaking, grammar-based ones suffer from information loss during the processes of both grammar tree generation and amr conversion. they predated the current deep learning approaches. modern amr parsers use deep learning methods. depending on how the eventual amr graphs are generated, we can divide them into graph-based and transition-based. both approaches are popular and their performances are competitive. briefly, a graph-based system splits amr parsing into two tasks, concept identification and edge prediction, and then combines them to generate a final amr graph. the idea seems to appear first in @xcite , and is used in @xcite titov (2018); zhang et al. (2019a); cai and lam (2020 ); zhou et al. (2020). a transition-based system, however, uses a sequence of transition actions to construct the graph incrementally. we can include the systems in wang et al. (2015); ballesteros and al-onaizan (2017); naseem et al. (2019); che et al. (2019); astudillo et al. (2020) in this category. as we mentioned, our work is about incorporating eds information into amr parsing. we note that brandt et al. (2016) considered adding preposition semantic role labeling to an amr parser but found that the extra information did not seem to help. hershcovich and arviv (2019) used a multitask learning model but found multi-task tupa consistently falls behind the single-task one for amr. arviv et al. (2020) used multi-task learning on eds and ucca parsing, however, eds didn't bring any benefits to ucca parsing. adding extra semantic information like eds is not easy. it matters how eds graphs are encoded and incorporated into amr parsing. we conduct our work with the amr dataset from mrp 2019, and pick one of the best performing systems there, hit-scir @xcite , as our baseline model. our experimental results show that adding eds information can indeed give a significant boost to the baseline model. we believe our method is general and can be applied to other amr parsing systems. ## conclusions in this study, we incorporate the eds, a meaning representation that is more accessible than amr, to improve the performance of amr parsing. to encode eds graphs for amr parsing, we used both lstm and gcn layers. as a case study, we enhanced a transition-based amr parser with eds graphs, and showed that on the amr benchmarks that the baseline model already performs well, our eds-enhanced parsers can further improve its performance. the improvements are especially noticeable on the lpps (the little prince) test set where the baseline parser performs poorly and lags behind other amr parsers at mrp 2019. in fact, on the lpps test set, our eds enhanced parsers outperform even the best one submitted there. we can also see some other implications of this work. for us, the ultimate goal of semantic parsing is to use it in downstream tasks such as question answering, reasoning, and knowledge extraction from texts. given that almost all meaning representations are graph-based, we believe our encoding of eds graphs with lstm and gcn layers can be applied in these downstream tasks. we are currently exploring this as a future work. another insight from this work is about possible connections among different meaning representations. we have demonstrated the usefulness of eds graphs for amr parsing. it is likely they can also be useful for other frameworks, even vice versa. more generally, whether there is a universal semantic parser that can take advantages of information from each framework is an interesting question worth investigating.