Abstract: Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connective as strong linguistic cues. A principle bottleneck to further improvement is the shortage of training data (ca.~16k instances in the PDTB). Shi et al. (2017) proposed to acquire additional data by exploiting connectives in translation: human translators mark discourse relations which are implicit in the source language explicitly in the translation. Using back-translations of such explicitated connectives improves discourse relation parsing performance. This paper addresses the open question of whether the choice of the translation language matters, and whether multiple translations into different languages can be effectively used to improve the quality of the additional data.

Introduction: Discourse relations connect two sentences/clauses to each other. The identification of discourse relations is an important step in natural language understanding and is beneficial to various downstream NLP applications such as text summarization BIBREF1 , BIBREF2 , question answering BIBREF3 , BIBREF4 , machine translation BIBREF5 , BIBREF6 , and so on. Discourse relations can be marked explicitly using a discourse connective or discourse adverbial such as “because”, “but”, “however”, see example SECREF1 . Explicitly marked relations are relatively easy to classify automatically BIBREF7 . In example SECREF2 , the causal relation is not marked explicitly, and can only be inferred from the texts. This second type of case is empirically even more common than explicitly marked relations BIBREF8 , but is much harder to classify automatically. The difficulty in classifying implicit discourse relations stems from the lack of strong indicative cues. Early work has already shown that implicit relations cannot be learned from explicit ones BIBREF9 , making human-annotated relations the currently only source for training relation classification. Due to the limited size of available training data, several approaches have been proposed for acquiring additional training data using automatic methods BIBREF10 , BIBREF11 . The most promising approach so far, BIBREF0 , exploits the fact that human translators sometimes insert a connective in their translation even when a relation was implicit in the original text. Using a back-translation method, BIBREF0 showed that such instances can be used for acquiring additional labeled text.  BIBREF0 however only used a single target langauge (French), and had no control over the quality of the labels extracted from back-translated connectives. In this paper, we therefore systematically compare the contribution of three target translation languages from different language families: French (a Romance language), German (from the Germanic language family) and Czech (a Slavic language). As all three of these languages are part of the EuroParl corpus, this also allows us to directly test whether higher quality can be achieved by using those instances that were consistently explicitated in several languages.

Question: How much additional data do they manage to generate from translations?
Answer: In case of 2-votes they used 9,298 samples and in case of 3-votes they used 1,298 samples.