Exploiting Knowledge about Discourse Relations for Implicit Discourse Relation Classification (2023)

Abstract: In discourse relation recognition, the classification labels are typically represented as one-hot vectors. However, the categories are in fact not all independent of one another on the contrary, there are several frameworks that describe the labels’ similarities (by e.g. sorting them into a hierarchy or describing them interms of features (Sanders et al., 2021)). Recently, several methods for representing the similarities between labels have been proposed (Zhang et al., 2018; Wang et al., 2018; Xiong et al., 2021). We here explore and extend the Label Confusion Model (Guo et al., 2021) for learning a representation for discourse relation labels. We explore alternative ways of informing the model about the similarities between relations, by representing relations in terms of their names (and parent category), their typical markers, or in terms of CCR features that describe the relations. Experimental results show that exploiting label similarity improves classification results.

## introduction discourse relations (drs) are logical relations between units of text ("arguments 1 and 2") that make the whole text coherent, see e.g. the concession relation in (1). the task of implicit dr recognition (idrr) is particularly challenging because informative discourse connectives (dcs), such as "however" are missing. implicit discourse relation classification tasks using the penn discourse treebank (pdtb) framework @xcite typically distinguish between 11 different labels. however, these labels are not completely independent of one anothersome relations tend to co-occur or be confused more than others. the similarities between relations are represented in the pdtb relation hierarchy, which groups the labels into four top-level classes, or by the ccr feature representation proposed in @xcite . however, these well-known similarities are typically not exploited for discourse relation classification tasks -instead, all labels are treated as if they were independent of one another. @xcite recently proposed the label confusion model (lcm), which seems well-suited for the characteristics of the idrr task: @xcite showed that the method is particularly suitable for problems with many labels, classification problems in which labels are ambiguous and tend to be confused with each other, and/or when there is semantic overlap between the labels. they demonstrated the benefit of the method on several text classification tasks. the goal of the present paper is to test whether the lcm approach is indeed helpful for idrr and experiment with three different ways of capturing the label similarities. (1) we use label embeddings: dr labels are not random words but terms that lexically describe the meaning of the drs, such as reason, precedence, condition, and so on. using label embeddings assumes that similar relations also tend to have names with similar lexical embeddings. however, some relation labels may additionally be associated with a quite different meaning in normal language use (e.g., "concession"), and their embedding may hence not capture the technical meaning well. (2) we characterize a dr by a set of prototypical connectives (e.g., however and nevertheless for a concession relation). ## results ## qualitative analysis in this section, we analyze some examples that demonstrate that the lcm has better captured the implicit drs between two arguments. first, as mentioned in the previous section, the false positive contrast relations predicted by the baseline model are mostly causal relations. in most of these cases, the arg2 contains the tokens now or still, which are often used to mark contrast, as in the following example. in example 2, the baseline model's prediction might have been based on the local markers now and the lexical pair major and minor, while the lcm model infers the positive relation between overcome major hurdle and only minor points remain. secondly, the lcm models overpredict re-statement relations, which are annotated as other relations in the pdtb. we found that for some of these cases, a restatement label could actually be justifiable as a secondary label. in example (3), respect being crucial is the reason that it is counted as a top value, but these two arguments can also be viewed as different ways to state that it is important for managers to repect their employees. in example (4), mr. wachter's comment could be an example of how sotheby's defends mr. paul. however, depending on the context, arg2 can also be interpreted as a restatement. these cases suggest that the lcm tends to confuse relations most easily when they are similar or have semantic overlap. however, we do note that there are cases where the lcm model indeed overpredicts restatement relations, see example ( 5 ). finally, comparing the different versions of the lcm models, the lcm dc model outperforms the other two models in predicting causal and con-junction relations. a possible explanation is that the dc tokens used to represent these relations are indeed strongly prototypical compared with other relations. this suggests that the choice of prototype dcs has a strong effect on the model performance. on the other hand, the lcm parent model has the highest recall of instantiation relations, but these are often co-occurring with re-statement, which is predicted by the other two variants. ## conclusion we proposed to inform an idrr model with knowledge about the drs encoded in the classification labels using the lcm, instead of treating each class independently. in addition, we explored various strategies to encode different types of knowledge into the model and found that they are all beneficial. this approach is flexible and can also be applied to other base models. furthermore, learning the lexical semantics of the label tokens allows a model to train on multiple datasets even if they do not share the same label set, and this is the direction of our future work.