Improving Neural RST Parsing Model with Silver Agreement Subtrees (2021)

Abstract: Most of the previous Rhetorical Structure Theory (RST) parsing methods are based on supervised learning such as neural networks, that require an annotated corpus of sufficient size and quality. However, the RST Discourse Treebank (RST-DT), the benchmark corpus for RST parsing in English, is small due to the costly annotation of RST trees. The lack of large annotated training data causes poor performance especially in relation labeling. Therefore, we propose a method for improving neural RST parsing models by exploiting silver data, i.e., automatically annotated data. We create large-scale silver data from an unlabeled corpus by using a state-of-the-art RST parser. To obtain high-quality silver data, we extract agreement subtrees from RST trees for documents built using the RST parsers. We then pre-train a neural RST parser with the obtained silver data and fine-tune it on the RST-DT. Experimental results show that our method achieved the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2, respectively. Furthermore, we obtained a remarkable gain in the Relation score, 3.0 points, against the previous state-of-the-art parser.

## introduction rhetorical structure theory (rst) @xcite is one of the most widely used theories for representing the discourse structure of a text as a tree. rst trees are a kind of constituent tree, whose leaves are elementary discourse units (edus), i.e., clause-like units, and whose non-terminal nodes cover text spans consisting of either a sequence of edus or a single edu. the label of a non-terminal node represents the attribution of a text span, i.e., nucleus (n) or satellite (s). a discourse relation is also assigned between two adjacent non-terminal nodes. in most cases, rst parsers have been developed on the basis of supervised learning algorithms @xcite @xcite @xcite , which require a high-quality annotated corpus of sufficient size. generally, they train the following three components of the rst parsing: (1) structure prediction by splitting a text span consisting of contiguous edus into two smaller ones or merging two adjacent spans into a larger one, (2) nuclearity status prediction for two adjacent spans by solving a 3-class classification problem, and (3) relation label prediction for two adjacent spans by solving an 18-class classification problem (see section 3.3 for details). however, it is costly to annotate rst trees for a huge collection of documents, and thus it is difficult to obtain a large amount of human-annotated data for rst parsing. as a result, research on rst parsing has focused on english, with the largest annotated corpus being the rst discourse treebank (rst-dt) @xcite , although even this is still small with only 385 documents. 1 many rst parsing methods have recently been developed based on neural models @xcite @xcite @xcite . among them, @xcite is the current state-of-theart system and is based on the neural top-down method. while its span and nuclearity scores achieved the highest level, its relation score still has room for improvement. one of the reasons for its poor relation score might be its small amount of training data for solving the 18-class classification problem. currently, we can refer to various studies on improving neural models for nlp tasks through acquiring large-scale synthetic training data, sometimes called silver data. among them, one of the studies on neural machine translation (nmt) @xcite introduced a simple learning framework: first pre-train an nmt model with silver data, i.e., pseudo-parallel data generated by automatic back-translation, and then fine-tune it with gold data, i.e., real parallel data, to overcome the data sparseness problem. since the frameworks successfully improved the nmt systems, it has become a standard approach. inspired by the above research, we propose a method for improving a student neural parser by exploiting large-scale silver data, thus generating rst trees using an automatic rst parser. 2 specifically, we improve the state-of-the-art neural rst parser @xcite , in terms of relation, by employing another rst parser whose relation score is also state-of-the-art @xcite as a teacher parser to generate the silver data. to yield high-quality silver data, we extract a collection of agreement subtrees (asts), which are common subtrees among multiple rst trees automatically parsed by the teacher parser with different seeds. our method includes an efficient algorithm for extracting the agreement subtrees to handle large-scale data. we first pre-train the student parser by using the obtained silver data. we then fine-tune parameters of the parser on gold data, using the rst-dt. experimental results on the rst-dt clearly indicate the effectiveness of our silver data. our method obtained remarkable nuclearity and relation f 1 scores of 75.0 and 63.2, respectively. ## related work early studies on rst parsing were based on traditional supervised learning methods with handcrafted features and the shift-reduce or cky-like parsing algorithms @xcite @xcite @xcite . recently, @xcite proposed a shift-reduce parser based on svms and achieved the current best results in classical statistical models on the rst-dt. the method first built nuclearity-labeled rst trees and then assigned relation labels between two adjacent spans consisting of a single or multiple edus. inspired by the success of neural networks in 2 nguyen et al. (2020) proposed a similar approach in nmt and introduced a method named data diversification: it diversifies the training data by using multiple forward and backward translation models. we can find some weak supervision approaches for other discourse representation formalisms such as @xcite . many nlp tasks, several neural network-based models have been proposed for rst parsing @xcite @xcite . @xcite proposed a shift-reduce parser based on neural networks and leveraged the information from their neural dependency parsing model within a sentence for rst parsing. the best relation score on the rst-dt, i.e., f 1 of 60.2, was achieved with their method. recently, a top-down neural parser was proposed @xcite for use only at the sentencelevel. the method parses a tree in a depth-first manner with a pointer-generator network. @xcite extended the method and applied it to document-level rst parsing. @xcite proposed another top-down rst parsing method exploiting multiple granularity levels in a document and achieved the best span and nuclearity scores on the rst-dt, i.e., f 1 of 87.0 and 74.6, respectively. since the rst-dt, the largest treebank, contains only 385 documents, several studies have been conducted on overcoming the problem of a limited number of training data. @xcite leveraged multi-task learning not only with 13 related tasks as an auxiliary task but also for multiple views of discourse structures, such as constituent, nuclearity, and relation. @xcite used multilingual rst discourse datasets that share the same underlying linguistic theory. @xcite adopted distant supervision with an auxiliary task of sentiment classification to create largescale training data, i.e., they trained a two-stage rst parser @xcite with rst trees automatically built based on attention and sentiment scores from the multiple-instance learning network, which was trained with a review dataset. however, these studies need other annotated corpora than the rst-dt, which means we still face the problem of being dependent on costly annotated corpora. @xcite proposed a framework for enriching training data based on co-training to improve the performance for infrequent relation labels. however, the method failed to improve the overall relation score, while they did not aim at improving the span and nuclearity scores. unsupervised rst parsing methods have also been proposed recently @xcite ). since they are unsupervised, they do not require any annotated corpora. however, they can predict only tree structures and p stu p stu p @xmath0: rst-dt unlabeled documents cnn silver data: agreement subtrees teacher parsers autoparsed trees student parser fine-tuning pre-training gold data: rst-dt training autoparsed trees cannot predict nucleus and relation labels. therefore, the predicted trees cannot be used for learning for predicting relation labels. we should mention the relationship of our work with semi-supervised learning as a machine learning framework. first, the reason why we do not adopt self-training, where the student and teacher parsers are the same, but instead use two different parsers that rely on different parsing algorithms is that we can acquire instances that the student parser cannot correctly parse yet the teacher parser can parse as the training data. second, using multiple different rst parsers in a semi-supervised manner in our work might seem reminiscent of co-or tritraining. while co-or tri-training is attractive, it is time consuming to repeat the step of alternately training multiple different neural network-based parsers many times. thus, previous studies have focused on simplifying the repetition step in constituency and dependency parsing @xcite @xcite @xcite . we believe our method is similar to these simplified version as a semi-supervised framework with two different rst parsers. ## conclusion to solve the problem of the limited amount of training data available for neural rst parsing, we proposed a method of exploiting agreement subtrees as silver data: we pre-train a parser with the silver data and fine-tune it with the gold data. we also presented an algorithm that efficiently extracts overlapping subtrees as the agreement subtrees from multiple trees. experimental results on the rst-dt demonstrated that our method significantly improves the performance of relation-labeled and fully-labeled f 1 scores, which are strongly affected by data sparseness due to a small number of training data. furthermore, the results showed that our method achieves the state-of-the-art nuclearity-labeled, relation-labeled, and fully-labeled f 1 scores. a effects of parameter l min