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Article summary:
1. Implicit discourse relation classification is a challenging task due to the lack of con-nectives as strong linguistic cues.
2. Shi et al. proposed to acquire additional data by exploiting connectives in translation, which can improve discourse relation parsing performance.
3. This paper investigates whether the choice of translation language matters and whether multiple translations into different languages can be effectively used to improve the quality of the additional data.
Article analysis:
The article provides an overview of the research conducted by Shi et al., which proposes a method for acquiring additional data for implicit discourse relation classification in the biomedical domain by exploiting connectives in translation. The article is well-written and provides a clear explanation of the research conducted, as well as its implications for improving discourse relation parsing performance. The authors provide evidence from their experiments that suggest that their proposed method can even marginally outperform state-of-the-art models, and that it is robust across domains.
The article does not appear to have any biases or one-sided reporting, as it presents both sides of the argument equally and objectively. It also does not contain any unsupported claims or missing points of consideration, as all claims are backed up with evidence from experiments conducted by the authors. Furthermore, there is no promotional content or partiality present in the article, as it focuses solely on presenting and discussing research findings without attempting to promote any particular viewpoint or agenda. Finally, possible risks associated with using this method are noted throughout the article, making it clear that further research needs to be done before this method can be implemented in practice.