To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed.In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task.We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task's hypothesis.We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario.Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.

Verb valence information can be derived from corpora by using subcorpora of typical sentences that are constructed in a language independent manner based on frequent POS structures. The inspection of typical sentences with a fixed verb in a certain position can show the valence information directly. Using verb fingerprints, consisting of the most typical sentence patterns the verb appears in, we are able to identify standard valence patterns and compare them against a language’s valence profile. With a very limited number of training data per language, valence information for other verbs can be derived as well. Based on the Norwegian valence patterns we are able to find comparative patterns in German where typical sentences are able to express the same situation in an equivalent way and can so construct verb valence pairs for a bilingual PolyVal dictionary. This contribution discusses this application with a focus on the Norwegian valence dictionary NorVal.

In second language learning, the correct use of prosody plays a vital role.Therefore, an automatic method to evaluate the naturalness of the prosody of a speaker is desirable.We present a novel method to model prosody independently of the text and thus independently of the language as well.For this purpose, the voiced and unvoiced speech segments are extracted and a 187-dimensional feature vector is computed for each voiced segment.This approach is compared to word based prosodic features on a German text passage.Both are confronted with the perceptive evaluation of two native speakers of German.The word-based feature set yielded correlations of up to 0.92 while the text-independent feature set yielded 0.88.This is in the same range as the inter-rater correlation with 0.88.Furthermore, the text-independent features were computed for a Japanese translation of the passage which was also rated by two native speakers of Japanese.Again, the correlation between the automatic system and the human perception of the naturalness was high with 0.83 and not significantly lower than the inter-rater correlation of 0.92.

BACKGROUND: The advancements of proteomics technologies have led to a rapid increase in the number, size and rate at which datasets are generated. Managing and extracting valuable information from such datasets requires the use of data management platforms and computational approaches. RESULTS: We have developed the MAss SPECTRometry Analysis System (MASPECTRAS), a platform for management and analysis of proteomics LC-MS/MS data. MASPECTRAS is based on the Proteome Experimental Data Repository (PEDRo) relational database schema and follows the guidelines of the Proteomics Standards Initiative (PSI). Analysis modules include: 1) import and parsing of the results from the search engines SEQUEST, Mascot, Spectrum Mill, X! Tandem, and OMSSA; 2) peptide validation, 3) clustering of proteins based on Markov Clustering and multiple alignments; and 4) quantification using the Automated Statistical Analysis of Protein Abundance Ratios algorithm (ASAPRatio). The system provides customizable data retrieval and visualization tools, as well as export to PRoteomics IDEntifications public repository (PRIDE). MASPECTRAS is freely available at http://genome.tugraz.at/maspectras CONCLUSION: Given the unique features and the flexibility due to the use of standard software technology, our platform represents significant advance and could be of great interest to the proteomics community.