Iterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling (2022)

Abstract: Semantic Role Labeling (SRL) is the task of labeling semantic arguments for marked semantic predicates. Semantic arguments and their predicates are related in various distinct manners, of which certain semantic arguments are a necessity while others serve as an auxiliary to their predicates. To consider such roles and relations of the arguments in the labeling order, we introduce iterative argument identification (IAI), which combines global decoding and iterative identification for the semantic arguments. In experiments, we first realize that the model with random argument labeling orders outperforms other heuristic orders such as the conventional left-to-right labeling order. Combined with simple reinforcement learning, the proposed model spontaneously learns the optimized labeling orders that are different from existing heuristic orders. The proposed model with the IAI algorithm achieves competitive or outperforming results from the existing models in the standard benchmark datasets of span-based SRL: CoNLL-2005 and CoNLL-2012.

## introduction semantic role labeling @xcite is the task of identifying and resolving the relations between semantic predicates and their arguments based on the propbank @xcite predicate-argument structure. in span-based srl, semantic predicates comprise several semantic arguments that are expressed as spans of tokens in the sentence. recent span-based srl models incorporate neural networks into a global decoding approach. here syntactic features are injected into the neural network model @xcite , span-based scoring for semantic argument is adapted @xcite , and the unified representations for both span-based and dependency-based srl are applied @xcite . however, in these approaches, the labeling order is not determined inside the neu-they can directly look at the agreement with us because ## related work the idea of optimizing the labeling orders in decoding is a branch of the easy-first strategy @xcite @xcite . in srl, @xcite proposed the srl model with the pseudo teacher approaches for the processing orders in srl. they exploit violation fixing perceptron and their parser explores the states of the highest scored path along with the word frequency ordering baseline. since their proposed model of "easy-first dynamic" follows the highest scoring action, their model explores limited transition spaces during training. refinement of existing srl is also examined in dependency-based srl @xcite . reinforcement learning is also applied in broad syntactic and semantic parsing studies @xcite @xcite . multi-task neural network is often applied to such structured syntactic analyses @xcite . it is notable that adversarial training is also applied to extract knowledge from unannotated corpora in japanese predicate-argument structure analysis @xcite . lattice-based approach is also a promising approach for srl in traditional @xcite and neural models @xcite . however, they rely on external dynamic programming decoding. choi and palmer (2011) proposed the transition-based model for dependency-based srl. they applied a set of transition actions that are similar to the shift-reduce parser @xcite in syntactic parsing. they also adapted the selflearning clustering technique for predicates that are unseen in training. @xcite address a sequence-to-sequence labeling model which performs competitive with sequence-labeling models. indeed, most of the recent srl resolving studies address the global-decoding approach @xcite @xcite @xcite or the sequential labeling approach @xcite @xcite @xcite in both span-based and dependencybased srl. in this paper, we introduce the iterative approach for the global argument selection and enable models to determine the ordering of resolving semantic arguments with modern neural networks and reinforcement learning for span-based srl. ## experiments we conducted experiments with the datasets provided from conll-2005 and 2012 shared tasks @xcite . we followed the standard splits of the datasets provided from conll and used the official and standard evaluation script. 4 in conll-2005, sections 2nd-21st of the wall street journal (wsj) corpus are for the training set and section 24th for the development set. the wsj section 23rd is for the in-domain test set while brown corpus 3rd sections is for the out-of-domain test set. conll-2012 dataset is from the ontonotes v5.0 corpus. see @xcite for more details of conll-2012. in the evaluation, the srl labels of "v" are omitted following the official evaluation script because they are obvious from the marked predicates. ## conclusion we develop the iterative argument identification (iai) algorithm for the global decoding and iterative resolving for span-based srl. our model with iai is capable of identifying semantic arguments one by one in arbitrary orders. in empirical experiments, we enhance our model with policy-gradient-based transition exploration. our model out-performs the existing models with the same pretrained model in both conll-05 and conll-12 datasets. in the analyses, we confirm that reinforcement learning enable models to learn a different resolving orders from existing heuristic orders and slightly enhance the performance, which suggest the emergence of the transition path through the training. figure 7 : the ratio for the role labels, on the horizontal axis, that are identified after the role labels on the vertical axis. the bright color represents the labels on the horizontal axis is likely to be identified after the labels on the vertical axis. left: imitation learning (random). right: reinforcement learning (random+rl). analysis on the development set of conll-2005. a.5 how reinforcement learning affects the argument distance from the predicates? figure 8 presents the distribution of the distance from predicates to their argument. we draw four distribution lines that correspond to the 1st, 2nd, 3rd and 4th transitions. here we count the number of sub-tokens between the predicates and their arguments as the distance. arguments at the right of their predicates have the positive distance and other arguments have the negative distance. in both random and random+rl, models tend to choose the arguments that are placed right after the predicates. however, this tendency becomes clear in random+rl: the model firstly chooses the arguments right after the predicates and later this model chooses arguments that are placed before the predicates. this suggests that the model learns the new ordering of the argument identification during rein-forcement learning.