Melbourne School of Psychological Sciences - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 11
  • Item
    Thumbnail Image
    Semantic Shifts in Mental Health-Related Concepts
    Baes, N ; Haslam, N ; Vylomova, E ; Tahmasebi, N ; Montariol, S ; Dubossarsky, H ; Kutuzov, A ; Hengchen, S ; Alfter, D ; Periti, F ; Cassotti, P (Association for Computational Linguistics, 2023)
    The present study evaluates semantic shifts in mental health-related concepts in two diachronic corpora spanning 1970-2016, one academic and one general. It evaluates whether their meanings have broadened to encompass less severe phenomena and whether they have become more pathology related. It applies a recently proposed methodology (Baes et al., 2023) to examine whether words collocating with a sample of mental health concepts have become less emotionally intense and develops a new way to examine whether the concepts increasingly co-occur with pathology-related terms. In support of the first hypothesis, mental health-related concepts became associated with less emotionally intense language in the psychology corpus (addiction, anger, stress, worry) and in the general corpus (addiction, grief, stress, worry). In support of the second hypothesis, mental health-related concepts came to be more associated with pathology-related language in psychology (addiction, grief, stress, worry) and in the general corpus (grief, stress). Findings demonstrate that some mental health concepts have become normalized and/or pathologized, a conclusion with important social and cultural implications.
  • Item
    Thumbnail Image
    The SIGTYP 2022 Shared Task on the Prediction of Cognate Reflexes
    List, JM ; Vylomova, E ; Forkel, R ; Hill, NW ; Cotterell, RD (ACL, 2022-01-01)
    This study describes the structure and the results of the SIGTYP 2022 shared task on the prediction of cognate reflexes from multilingual wordlists. We asked participants to submit systems that would predict words in individual languages with the help of cognate words from related languages. Training and surprise data were based on standardized multilingual wordlists from several language families. Four teams submitted a total of eight systems, including both neural and non-neural systems, as well as systems adjusted to the task and systems using more general settings. While all systems showed a rather promising performance, reflecting the overwhelming regularity of sound change, the best performance throughout was achieved by a system based on convolutional networks originally designed for image restoration.
  • Item
    Thumbnail Image
    The SIGMORPHON 2022 Shared Task on Morpheme Segmentation
    Batsuren, K ; Bella, G ; Arora, A ; Martinovic, V ; Gorman, K ; Žabokrtský, Z ; Ganbold, A ; Dohnalová, Š ; Ševčíková, M ; Pelegrinová, K ; Giunchiglia, F ; Cotterell, R ; Vylomova, E (Association for Computational Linguistics, 2022-01-01)
    The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections. Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29% F1 score across all languages, ranging English (93.84%) to Latin (99.38%). Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Morfessor2) by 30.71% absolute. To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets.
  • Item
    Thumbnail Image
    Morphology is not just a naive Bayes - UniMelb Submission to SIGMORPHON 2022 ST on Morphological Inflection
    Scherbakov, A ; Vylomova, E (Association for Computational Linguistics, 2022-01-01)
    The paper describes the Flexica team's submission to the SIGMORPHON 2022 Shared Task 1 Part 1: Typologically Diverse Morphological Inflection. Our team submitted a non-neural system that extracted transformation patterns from alignments between a lemma and inflected forms. For each inflection category, we chose a pattern based on its abstractness score. The system outperformed the non-neural baseline, the extracted patterns covered a substantial part of possible inflections. However, we discovered that such score that does not account for all possible combinations of string segments as well as morphosyntactic features is not sufficient for a certain proportion of inflection cases.
  • Item
    Thumbnail Image
    SIGMORPHON-UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection
    Kodner, J ; Khalifa, S ; Batsuren, K ; Dolatian, H ; Cotterell, R ; Akkuş, F ; Anastasopoulos, A ; Andrushko, T ; Arora, A ; Bella, NAG ; Budianskaya, E ; Ghanggo Ate, Y ; Goldman, O ; Guriel, D ; Guriel, S ; Guriel-Agiashvili, S ; Kieraś, W ; Krizhanovsky, A ; Krizhanovsky, N ; Marchenko, I ; Markowska, M ; Mashkovtseva, P ; Nepomniashchaya, M ; Rodionova, D ; Sheifer, K ; Serova, A ; Yemelina, A ; Young, J ; Vylomova, E (Association for Computational Linguistics, 2022-01-01)
    The 2022 SIGMORPHON-UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe. We emphasize generalization along different dimensions this year by evaluating test items with unseen lemmas and unseen features separately under small and large training conditions. Across the six submitted systems and two baselines, the prediction of inflections with unseen features proved challenging, with average performance decreased substantially from last year. This was true even for languages for which the forms were in principle predictable, which suggests that further work is needed in designing systems that capture the various types of generalization required for the world's languages.
  • Item
    Thumbnail Image
    SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
    Pimentel, T ; Ryskina, M ; Mielke, SJ ; Wu, S ; Chodroff, E ; Leonard, B ; Nicolai, G ; Ghanggo Ate, Y ; Khalifa, S ; Habash, N ; El-Khaissi, C ; Goldman, O ; Gasser, M ; Lane, W ; Coler, M ; Oncevay, A ; Montoya Samame, JR ; Silva Villegas, GC ; Ek, A ; Bernardy, J-P ; Shcherbakov, A ; Bayyr-ool, A ; Sheifer, K ; Ganieva, S ; Plugaryov, M ; Klyachko, E ; Salehi, A ; Krizhanovsky, A ; Krizhanovsky, N ; Vania, C ; Ivanova, S ; Salchak, A ; Straughn, C ; Liu, Z ; Washington, JN ; Ataman, D ; Kieraś, W ; Woliński, M ; Suhardijanto, T ; Stoehr, N ; Nuriah, Z ; Ratan, S ; Tyers, FM ; Ponti, EM ; Aiton, G ; Hatcher, RJ ; Prud'hommeaux, E ; Kumar, R ; Hulden, M ; Barta, B ; Lakatos, D ; Szolnok, G ; Ács, J ; Raj, M ; Yarowsky, D ; Cotterell, R ; Ambridge, B ; Vylomova, E (Association for Computational Linguistics, 2021)
  • Item
    Thumbnail Image
    Evaluation of Semantic Change of Harm-Related Concepts in Psychology
    Vylomova, K ; Murphy, S ; Haslam, N (ASSOC COMPUTATIONAL LINGUISTICS-ACL, 2019)
  • Item
    Thumbnail Image
    SIGTYP 2020 Shared Task: Prediction of Typological Features
    Bjerva, J ; Salesky, E ; Mielke, SJ ; Chaudhary, A ; Giuseppe, C ; Ponti, EM ; Vylomova, E ; Cotterell, R ; Augenstein, I (Association for Computational Linguistics, 2020)
  • Item
    Thumbnail Image
    SIGTYP 2021 Shared Task: Robust Spoken Language Identification
    Salesky, E ; Abdullah, BM ; Mielke, S ; Klyachko, E ; Serikov, O ; Ponti, EM ; Kumar, R ; Cotterell, R ; Vylomova, E (Association for Computational Linguistics, 2021)
  • Item
    Thumbnail Image
    Anlirika: An LSTM–CNN Flow Twister for Spoken Language Identification
    Scherbakov, A ; Whittle, L ; Kumar, R ; Singh, S ; Coleman, M ; Vylomova, E (Association for Computational Linguistics, 2021)