Implicit Sense-labeled Connective Recognition as Text Generation (2023)

Abstract: Implicit Discourse Relation Recognition (IDRR) involves identifying the sense label of an implicit connective between adjacent text spans. This has traditionally been approached as a classification task. However, some downstream tasks require more than just a sense label as well as the specific connective used. This paper presents Implicit Sense-labeled Connective Recognition (ISCR), which identifies the implicit connectives and their sense labels between adjacent text spans. ISCR can be treated as a classification task, but a large number of potential categories, sense labels, and uneven distribution of instances among them make this difficult. Instead, this paper handles the task as a text-generation task, using an encoder-decoder model to generate both connectives and their sense labels. Here, we explore a classification method and three kinds of text-generation methods. From our evaluation results on PDTB-3.0, we found that our method outperforms the conventional classification-based method.

## introduction discourse relations are often given adjacent text spans, such as clauses and sentences, without explicit connectives. recognizing the implicit discourse relations (i.e., implicit discourse relation recognition (idrr)) has been addressed as the main task of shallow discourse parsing. figure 1 shows an example of iddr. in pdtb-3.0 @xcite , a standard benchmark dataset for idrr, 4 coarse-grained and 22 fine-grained discourse relations are defined between two text spans, referred to as arg1 and arg2. recent studies have exploited pre-trained language models as vector representations for arg1 and arg2. in those cases, a model predicts a label of discourse relation by classifying the obtained vectors into classification layers. in other words, the previous studies focused on identifying implicit discourse relations rather than implicit connectives. however, for downstream tasks such as machine translation, it may be necessary to handle both discourse relations and connectives @xcite @xcite . here, connectives and discourse relations do not necessarily involve a one-to-one correspondence. when only a discourse relation is given, we must consider multiple candidate connectives corresponding to it. furthermore, the candidate connective may already correspond to another discourse relation. therefore, when exploiting idrr in machine translation, incorrect translation may result from the failure to perform disambiguation of the connectives. this problem can also occur when only a single connective is provided. figure 2 shows examples of such issues. this limitation has motivated us to identify both the connectives and their discourse relations, or sense-labeled connectives. this paper addresses the task of predicting not only implicit discourse relation between text spans but also the connective, and we refer to this as implicit sense-labeled connective recognition (iscr). iscr is similar to idrr in that it is also regarded as a classification task. however, iscr is particularly challenging because it involves many classes in combining discourse relations and connectives. moreover, the uneven distribution of instances belonging to classes also makes this task difficult. therefore, inspired by li et al. (2018), we regard our classification task as a text generation a connective since corresponds to contingency.cause and to temporal.asynchronous. we explore three types of text generation to predict the implicit sense-labeled connectives: (1) generating connectives with discourse relations only, (2) generating connectives with discourse relations between arg1 and arg2, (3) generating connectives between arg1 and arg2 and then predicting discourse relations based on a classifier using the connectives. the experimental results obtained from pdtb-3.0 demonstrate that generationbased approaches outperform a simple classification approach in terms of accuracy, macro-f1, and weighted-f1. ## text generation for iscr in iscr, the number of sense-labeled connectives to be predicted is significantly larger than that of idrr, and the distribution of instances belonging to each class is unbalanced. the label of the iscr task is composed of a discourse relation label and connective words. considering that it is not difficult to generate connective words with the pre-trained encoder-decoder model, we employ a text generation approach following @xcite and @xcite . one possible way of improving the iscr task is to incorporate contextual information obtained from arg1 and arg2 in the encoder-decoder model. therefore, we examined the following three generation methods using encoder-decoder models. connective-label generation (clg): connectives and discourse relation labels, or 'connective (discourse relation label),' are generated using an encoder-decoder model such as li et al. (2018) (figure 3 , left). the reason for using the expression 'connective (discourse relation label)' is that the training data for the pre-trained encoder-decoder model contain many expressions 'some expression + (its description).' when the decoder outputs a parenthesis after a particular word, we expect it to predict a description of the word. 2019) foot_0 to evaluate the performance of iscr. this dataset contains annotated discourse relations in articles from the wall street journal. while they have relation labels at three levels, this study used labels only up to the second level, following the previous idrr studies @xcite @xcite . in addition, connective words provided by annotators were used. here, we also used the standard train/val/test split of the dataset @xcite . training data consisted of sections 2-20, validation data used sections 0-1, and testing used sections 21-22. the pdtb-3.0 dataset contains 99 iscr classes at the top level and 133 at the second level in test set, while there are only four classes at the top level and 17 at the second level for idrr. 2 since iscr predicts sense-labeled connectives, the number of classes increases, which results in unbalanced data. ## results and discussion results of iscr: to discuss the effectiveness of clg+pf in more detail, we show f1 scores of the vanilla classifier and clg+pf for each label in figure 4 . note that only labels that occur more than 20 times in the test set are listed. while the vanilla classifier does not predict many labels, clg+pf can predict labels in most cases. ## conclusion this paper investigated iscr as a way to predict implicit sense-labeled connectives between adjacent text spans. here, we leveraged a text-generation approach, using encoder-decoder models, rather than a simple classification approach. this strategy was motivated by the large number of sense-labeled connectives and their unbalanced distribution. experimental results obtained from pdtb-3.0 show that our method is superior to the simple classification method. when generating connectives and corresponding relation labels together with arg1 and arg2, the proposed method achieved the best performance on weighted-f1. this method obtained comparable performance in the idrr setting and better performance for connective prediction than a simple classification approach.