Publications

Displaying 101 - 106 of 106
  • Vagliano, I., Galke, L., Mai, F., & Scherp, A. (2018). Using adversarial autoencoders for multi-modal automatic playlist continuation. In C.-W. Chen, P. Lamere, M. Schedl, & H. Zamani (Eds.), RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018 (pp. 5.1-5.6). New York: ACM. doi:10.1145/3267471.3267476.

    Abstract

    The task of automatic playlist continuation is generating a list of recommended tracks that can be added to an existing playlist. By suggesting appropriate tracks, i. e., songs to add to a playlist, a recommender system can increase the user engagement by making playlist creation easier, as well as extending listening beyond the end of current playlist. The ACM Recommender Systems Challenge 2018 focuses on such task. Spotify released a dataset of playlists, which includes a large number of playlists and associated track listings. Given a set of playlists from which a number of tracks have been withheld, the goal is predicting the missing tracks in those playlists. We participated in the challenge as the team Unconscious Bias and, in this paper, we present our approach. We extend adversarial autoencoders to the problem of automatic playlist continuation. We show how multiple input modalities, such as the playlist titles as well as track titles, artists and albums, can be incorporated in the playlist continuation task.
  • Van Geenhoven, V. (1999). A before-&-after picture of when-, before-, and after-clauses. In T. Matthews, & D. Strolovitch (Eds.), Proceedings of the 9th Semantics and Linguistic Theory Conference (pp. 283-315). Ithaca, NY, USA: Cornell University.
  • Van Geenhoven, V., & Warner, N. (Eds.). (1999). Max-Planck Institute for Psycholinguistics: Annual report 1999. Nijmegen: Max Planck Institute for Psycholinguistics.
  • Vernes, S. C. (2018). Vocal learning in bats: From genes to behaviour. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 516-518). Toruń, Poland: NCU Press. doi:10.12775/3991-1.128.
  • Von Holzen, K., & Bergmann, C. (2018). A Meta-Analysis of Infants’ Mispronunciation Sensitivity Development. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1159-1164). Austin, TX: Cognitive Science Society.

    Abstract

    Before infants become mature speakers of their native language, they must acquire a robust word-recognition system which allows them to strike the balance between allowing some variation (mood, voice, accent) and recognizing variability that potentially changes meaning (e.g. cat vs hat). The current meta-analysis quantifies how the latter, termed mispronunciation sensitivity, changes over infants’ first three years, testing competing predictions of mainstream language acquisition theories. Our results show that infants were sensitive to mispronunciations, but accepted them as labels for target objects. Interestingly, and in contrast to predictions of mainstream theories, mispronunciation sensitivity was not modulated by infant age, suggesting that a sufficiently flexible understanding of native language phonology is in place at a young age.
  • Walsh Dickey, L. (1999). Syllable count and Tzeltal segmental allomorphy. In J. Rennison, & K. Kühnhammer (Eds.), Phonologica 1996. Proceedings of the 8th International Phonology Meeting (pp. 323-334). Holland Academic Graphics.

    Abstract

    Tzeltal, a Mayan language spoken in southern Mexico, exhibits allo-morphy of an unusual type. The vowel quality of the perfective suffix is determined by the number of syllables in the stem to which it is attaching. This paper presents previously unpublished data of this allomorphy and demonstrates that a syllable-count analysis of the phenomenon is the proper one. This finding is put in a more general context of segment-prosody interaction in allomorphy.

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