In scientific papers, arguments are essential for explaining authors’ findings. As substrates of the reasoning process, arguments are often decorated with discourse indicators such as “which shows that” or “suggesting that”. However, it remains understudied whether discourse indicators by themselves can be used as an effective marker of the local argument components (LACs) in the body text that support the main claim in the abstract, i.e., the global argument. In this work, we investigate whether discourse indicators reflect the global premise and conclusion. We construct a set of regular expressions for over 100 word- and phrase-level discourse indicators and measure the alignment of LACs extracted by discourse indicators with the global arguments. We find a positive correlation between the alignment of local premises and local conclusions. However, compared to a simple textual intersection baseline, discourse indicators achieve lower ROUGE recall and have limited capability of extracting LACs relevant to the global argument; thus their role in scientific reasoning is less salient as expected.
Character-Level Translation with Self-attention
Yingqiang Gao | Nikola I. Nikolov | Yuhuang Hu | Richard H.R. Hahnloser
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
We explore the suitability of self-attention models for character-level neural machine translation. We test the standard transformer model, as well as a novel variant in which the encoder block combines information from nearby characters using convolutions. We perform extensive experiments on WMT and UN datasets, testing both bilingual and multilingual translation to English using up to three input languages (French, Spanish, and Chinese). Our transformer variant consistently outperforms the standard transformer at the character-level and converges faster while learning more robust character-level alignments.