Frans Hendrik van Eemeren, Rob Grootendorst, Sally Jackson, Scott Jacobs, et al. 1993. Reconstructing argumentative discourse. University of Alabama Press. Rob Abbott, Marilyn Walker, Pranav Anand, Jean E Fox Tree, Robeson Bowmani, and Joseph King. 2011. How can you say such things?!?: Recognizing disagreement in informal political argument. In Proceedings of the Workshop on Languages in Social Media, pages 2–11. Association for Computational Linguistics. Marilyn A Walker, Jean E Fox Tree, Pranav Anand, Rob Abbott, and Joseph King. 2012b.    

Figure 1 shows a high-level outline of our experimental design, which aims to establish whether injecting structure derived from a variety of syntacto-semantic formalisms into neural language model representations can lead to better correspondence with human brain activation data. We utilize fMRI recordings of human subjects reading a set of texts. Representations of these texts are then derived from the activations of the language models. Following Gauthier and Levy (

Test Type Co-referent? Pass Rate & Total Tests Example test case and outcome for test case Anaphora and Exophora resolution Yes 47.1% (16/34) M1: …to boost the struggling insect’s numbers… [PASS] M2: the annual migration of the monarch butterfly… No    

Abstract While a number of works showed gains from incorporating source-side symbolic syntactic and semantic structure into neural machine translation (NMT), much fewer works addressed the decoding of such structure. We propose a general Transformer-based approach for tree and graph decoding based on generating a sequence of transitions, inspired by a similar approach that uses RNNs by Dyer et al. (2016). Experiments with using the proposed decoder with Universal Dependencies syntax on English-German, German-English and English-Russian show improved performance over […]

In Our model, we generate representations of post message in three methods: tokenized syllables-level text through Bert4News, tokenized word-level text through PhoBERT and tokenized syllables-level text through XLM. We simply concatenate both this three representations with the corresponding post metadata features. This can be considered as a naive model but are proved that can improve performance of systems (Tu et al. (2017), Thanh et al. (

AM LM WER [%] Dev2 Test 1 CNN-TDNNf Alb 14.1 15.5 2 Alb + Wiki 13.6 14.9 3 Alb + Giga 13.6 15.1 4 Alb + Wiki + Giga 13.5 15.0 5