Exploiting a lexical resource for discourse connective disambiguation in G erman (2020)

Abstract: In this paper we focus on connective identification and sense classification for explicit discourse relations in German, as two individual sub-tasks of the overarching Shallow Discourse Parsing task. We successively augment a purely-empirical approach based on contextualised embeddings with linguistic knowledge encoded in a connective lexicon. In this way, we improve over published results for connective identification, achieving a final F1-score of 87.93; and we introduce, to the best of our knowledge, first results for German sense classification, achieving an F1-score of 87.13. Our approach demonstrates that a connective lexicon can be a valuable resource for those languages that do not have a large PDTB-style-annotated coprus available.

## introduction an important difference between a text and a random collection of sentences is the amount of coherence it exhibits. in a text, sentences, or propositions therein, are connected through particular relations that can be, for example, causal, temporal or contrastive. such relations can be obvious from the semantics of the involved text segments alone, as in (1), where the contrastive relation between male and female worker's earnings is easily inferred. or they can be explicitly signaled by (discourse) connectives. connectives are usually understood to be a closed class of syntactically heterogeneous words and phrases, and are known to be ambiguous in two different ways. in (2), there are no two particular propositions that are related to each other by the potential connective once, thus it is said to have sentential reading, as opposed to its discourse reading in (3) and (4). furthermore, once is ambiguous with regard to the particular relations it can express, and signals a temporal relation in (3) and a conditional relation in (4). 1 (1) earnings of year-round, full-time male workers fell 1.3% in 1988 after adjusting for higher prices. earnings of female workers were unchanged. (wsj 1815) the availability of annotated data for the task of discourse parsing as a whole, and consequently its sub-tasks, is limited. with the pdtb being by far the largest corpus annotated for discourse relations, containing ~53k annotated relations in its 3.0 version @xcite , any language other than english can be considered a low-resource language. however, in recent years, many connective lexicons, listing all connectives of the respective language and some of their properties, have become available for several languages. 2 the main contribution of this paper is to investigate to what extent linguistic knowledge encoded in such lexicons can augment a purely-empirical approach to connective disambiguation in a low-resource scenario. particularly, at first we fine-tune bert @xcite to the task of both connective identification and sense classification for german, and then attempt to improve over this approach by exploiting dimlex @xcite , a german connective lexicon. additionally, we experiment with syntactically inspired features in the tradition of @xcite . our results demonstrate that exploiting a connective lexicon can improve performance both within-domain and across-domain in situations where limited training data is available. the rest of this paper is structured as follows: section 2 provides a more detailed definition of connectives and discusses related work on connective identification and sense classification. section 3 explains the resources we use in our experiments. section 4 outlines our method of combining bert with information from the connective lexicon, and also syntactically inspired features for connective disambiguation. section 5 presents and discusses the results, including the question of generalizing the approach to other languages. finally, section 6 sums up the key findings and provides an outlook on future work. ## conclusion & future work we reported our experiments on two sub-tasks of discourse parsing: connective identification and sense classification (for explicit relations). working on german, which in this respect can be considered a low-resource language, we use a state-of-the-art language modeling tool (bert) that has proven to be successful in a variety of tasks for which typically considerably more training data is available. we demonstrate the effectiveness of augmenting this robust approach with linguistic knowledge as encoded in a connective lexicon, and with manually crafted and syntactically inspired features. in the best-scoring setup, we obtain an f 1 -score of 87.93 for connective identification, and 87.13 for sense classification for explicit relations. this improves over previously published results for german connective identification and sets a first benchmark for german sense classification (for which, to the best of our knowledge, no published results are available). furthermore, we move beyond the german corpus that is regularly used in this kind of work (pcc) and evaluate our approach also on annotated data samples from wikipedia and news texts and report a significant increase in performance. due to the relatively low agreement scores on this annotated data though, we consider improving the quality of the annotations an important piece of future work. our experiments are done in the context of developing an end-to-end system for german discourse parsing. the vast majority of such end-to-end systems use a pipeline architecture, in which connective identification is the first, and (explicit relation) sense classification the third component. this paper presents sense classification results using gold connectives only, and we consider it an important piece of future work to put the individual components together to get an idea of performance in a pipeline setup, with error propagation. finally, we note that many discourse relations in a text are not signaled explicitly (at least not by connectives); in the pdtb2, explicit relations make up ~45% of all annotated relations. the processing of non-explicit relations is not covered in this paper, and is equally an important piece of future work. the recent literature using the english pdtb corpus has usually tackled this with neural approaches, but for low-resource languages, other solutions will probably have to be found.