| 1. |
- Buchanan, E. M., et al.
(författare)
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The Psychological Science Accelerator's COVID-19 rapid-response dataset
- 2023
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data.
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| 2. |
- Kurfali, Murathan, et al.
(författare)
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A distantly supervised Grammatical Error Detection/Correction system for Swedish
- 2023
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Ingår i: Proceedings of the 12th Workshop on NLP for Computer Assisted Language Learning. - 9789180752503 ; , s. 35-39
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Konferensbidrag (refereegranskat)abstract
- This paper presents our submission to the first Shared Task on Multilingual Grammatical Error Detection (MultiGED-2023). Our method utilizes a transformer-based sequence-to-sequence model, which was trained on a synthetic dataset consisting of 3.2 billion words. We adopt a distantly supervised approach, with the training process relying exclusively on the distribution of language learners' errors extracted from the annotated corpus used to construct the training data. In the Swedish track, our model ranks fourth out of seven submissions in terms of the target F0.5 metric, while achieving the highest precision. These results suggest that our model is conservative yet remarkably precise in its predictions.
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| 3. |
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| 4. |
- Kutlu, Ferhat, et al.
(författare)
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Toward a shallow discourse parser for Turkish
- 2023
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Ingår i: Natural Language Engineering. - 1351-3249 .- 1469-8110.
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Tidskriftsartikel (refereegranskat)abstract
- One of the most interesting aspects of natural language is how texts cohere, which involves the pragmatic or semantic relations that hold between clauses (addition, cause-effect, conditional, similarity), referred to as discourse relations. A focus on the identification and classification of discourse relations appears as an imperative challenge to be resolved to support tasks such as text summarization, dialogue systems, and machine translation that need information above the clause level. Despite the recent interest in discourse relations in well-known languages such as English, data and experiments are still needed for typologically different and less-resourced languages. We report the most comprehensive investigation of shallow discourse parsing in Turkish, focusing on two main sub-tasks: identification of discourse relation realization types and the sense classification of explicit and implicit relations. The work is based on the approach of fine-tuning a pre-trained language model (BERT) as an encoder and classifying the encoded data with neural network-based classifiers. We firstly identify the discourse relation realization type that holds in a given text, if there is any. Then, we move on to the sense classification of the identified explicit and implicit relations. In addition to in-domain experiments on a held-out test set from the Turkish Discourse Bank (TDB 1.2), we also report the out-domain performance of our models in order to evaluate its generalization abilities, using the Turkish part of the TED Multilingual Discourse Bank. Finally, we explore the effect of multilingual data aggregation on the classification of relation realization type through a cross-lingual experiment. The results suggest that our models perform relatively well despite the limited size of the TDB 1.2 and that there are language-specific aspects of detecting the types of discourse relation realization. We believe that the findings are important both in providing insights regarding the performance of the modern language models in a typologically different language and in the low-resource scenario, given that the TDB 1.2 is 1/20th of the Penn Discourse TreeBank in terms of the number of total relations.
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| 5. |
- Östling, Robert, 1986-, et al.
(författare)
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Language Embeddings Sometimes Contain Typological Generalizations
- 2023
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Ingår i: Computational linguistics - Association for Computational Linguistics (Print). - 0891-2017 .- 1530-9312. ; 49:4, s. 1003-1051
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Tidskriftsartikel (refereegranskat)abstract
- To what extent can neural network models learn generalizations about language structure, and how do we find out what they have learned? We explore these questions by training neural models for a range of natural language processing tasks on a massively multilingual dataset of Bible translations in 1,295 languages. The learned language representations are then compared to existing typological databases as well as to a novel set of quantitative syntactic and morphological features obtained through annotation projection. We conclude that some generalizations are surprisingly close to traditional features from linguistic typology, but that most of our models, as well as those of previous work, do not appear to have made linguistically meaningful generalizations. Careful attention to details in the evaluation turns out to be essential to avoid false positives. Furthermore, to encourage continued work in this field, we release several resources covering most or all of the languages in our data: (1) multiple sets of language representations, (2) multilingual word embeddings, (3) projected and predicted syntactic and morphological features, (4) software to provide linguistically sound evaluations of language representations.
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