| 1. |
- Wirén, Mats, 1954-, et al.
(författare)
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Annotating the Narrative: A Plot of Scenes, Events, Characters and Other Intriguing Elements
- 2022
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Ingår i: LIVE and LEARN. - Gothenburg : Department of Swedish, Multilingualism, Language Technology. - 9789187850837 ; , s. 161-164
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Analysis of narrative structure in prose fiction is a field which is gaining increased attention in NLP, and which potentially has many interesting and more far-reaching applications. This paper provides a summary and motivation of two different but interrelated strands of work that we have carried out in this field during the last years: on the one hand, principles and guidelines for annotation, and on the other, methods for automatic annotation.
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| 2. |
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| 3. |
- 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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| 4. |
- Kurfali, Murathan, 1990-, et al.
(författare)
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A Multi-Word Expression Dataset for Swedish
- 2020
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Ingår i: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020). - Marseille : European Language Resources Association (ELRA). ; , s. 4402-4409
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Konferensbidrag (refereegranskat)abstract
- We present a new set of 96 Swedish multi-word expressions annotated with degree of (non-)compositionality. In contrast to most previous compositionality datasets we also consider syntactically complex constructions and publish a formal specification of each expression. This allows evaluation of computational models beyond word bigrams, which have so far been the norm. Finally, we use the annotations to evaluate a system for automatic compositionality estimation based on distributional semantics. Our analysis of the disagreements between human annotators and the distributional model reveal interesting questions related to the perception of compositionality, and should be informative to future work in the area.
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| 5. |
- Kurfalı, Murathan, 1990-, et al.
(författare)
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Breaking the Narrative: Scene Segmentation through Sequential Sentence Classification
- 2021
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Konferensbidrag (refereegranskat)abstract
- In this paper, we describe our submission to the Shared Task on Scene Segmentation (STSS). The shared task requires participants to segment novels into coherent segments, called scenes. We approach this as a sequential sentence classification task and offer a BERT-based solution with a weighted cross-entropy loss. According to the results, the proposed approach performs relatively well on the task as our model ranks first and second, in official in-domain and out-domain evaluations, respectively. However, the overall low performances (0.37 F1-score) suggest that there is still much room for improvement.
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| 6. |
- Kurfalı, Murathan, 1990-
(författare)
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Contributions to Shallow Discourse Parsing : To English and beyond
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Discourse is a coherent set of sentences where the sequential reading of the sentences yields a sense of accumulation and readers can easily follow why one sentence follows another. A text that lacks coherence will most certainly fail to communicate its intended message and leave the reader puzzled as to why the sentences are presented together. However, formally accounting for the differences between a coherent and a non-coherent text still remains a challenge. Various theories propose that the semantic links that are inferred between sentences/clauses, known as discourse relations, are the building blocks of the discourse that can be connected to one another in various ways to form the discourse structure. This dissertation focuses on the former problem of discovering such discourse relations without aiming to arrive at any structure, a task known as shallow discourse parsing (SDP). Unfortunately, so far, SDP has been almost exclusively performed on the available gold annotations in English, leading to only limited insight into how the existing models would perform in a low-resource scenario potentially involving any non-English language. The main objective of the current dissertation is to address these shortcomings and help extend SDP to the non-English territory. This aim is pursued through three different threads: (i) investigation of what kind of supervision is minimally required to perform SDP, (ii) construction of multilingual resources annotated at discourse-level, (iii) extension of well-known means to (SDP-wise) low-resource languages. An additional aim is to explore the feasibility of SDP as a probing task to evaluate discourse-level understanding abilities of modern language models is also explored.The dissertation is based on six papers grouped in three themes. The first two papers perform different subtasks of SDP through relatively understudied means. Paper I presents a simplified method to perform explicit discourse relation labeling without any feature-engineering whereas Paper II shows how implicit discourse relation recognition benefits from large amounts of unlabeled text through a novel method for distant supervision. The third and fourth papers describe two novel multilingual discourse resources, TED-MDB (Paper III) and three bilingual discourse connective lexicons (Paper IV). Notably, Ted-MDB is the first parallel corpus annotated for PDTB-style discourse relations covering six non-English languages. Finally, the last two studies directly deal with multilingual discourse parsing where Paper V reports the first results in cross-lingual implicit discourse relation recognition and Paper VI proposes a multilingual benchmark including certain discourse-level tasks that have not been explored in this context before. Overall, the dissertation allows for a more detailed understanding of what is required to extend shallow discourse parsing beyond English. The conventional aspects of traditional supervised approaches are replaced in favor of less knowledge-intensive alternatives which, nevertheless, achieve state-of-the-art performance in their respective settings. Moreover, thanks to the introduction of TED-MDB, cross-lingual SDP is explored in a zero-shot setting for the first time. In sum, the proposed methodologies and the constructed resources are among the earliest steps towards building high-performance multilingual, or non-English monolingual, shallow discourse parsers.
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| 7. |
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| 8. |
- Kurfali, Murathan, 1990-
(författare)
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Labeling Explicit Discourse Relations Using Pre-trained Language Models
- 2020
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Ingår i: Text, Speech, and Dialogue. - Cham : Springer. - 9783030583231 - 9783030583224 ; , s. 79-86
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Bokkapitel (refereegranskat)abstract
- Labeling explicit discourse relations is one of the most challenging sub-tasks of the shallow discourse parsing where the goal is to identify the discourse connectives and the boundaries of their arguments. The state-of-the-art models achieve slightly above 45% of F-score by using hand-crafted features. The current paper investigates the efficacy of the pre-trained language models in this task. We find that the pre-trained language models, when finetuned, are powerful enough to replace the linguistic features. We evaluate our model on PDTB 2.0 and report the state-of-the-art results in extraction of the full relation. This is the first time when a model outperforms the knowledge intensive models without employing any linguistic features.
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| 9. |
- Kurfali, Murathan, 1990-, et al.
(författare)
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Let’s be explicit about that : Distant supervision for implicit discourse relation classification via connective prediction
- 2021
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Konferensbidrag (refereegranskat)abstract
- In implicit discourse relation classification, we want to predict the relation between adjacent sentences in the absence of any overt discourse connectives. This is challenging even for humans, leading to shortage of annotated data, a fact that makes the task even more difficult for supervised machine learning approaches. In the current study, we perform implicit discourse relation classification without relying on any labeled implicit relation. We sidestep the lack of data through explicitation of implicit relations to reduce the task to two sub-problems: language modeling and explicit discourse relation classification, a much easier problem. Our experimental results show that this method can even marginally outperform the state-of-the-art, in spite of being much simpler than alternative models of comparable performance. Moreover, we show that the achieved performance is robust across domains as suggested by the zero-shot experiments on a completely different domain. This indicates that recent advances in language modeling have made language models sufficiently good at capturing inter-sentence relations without the help of explicit discourse markers.
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| 10. |
- Kurfali, Murathan, 1990-, et al.
(författare)
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Probing Multilingual Language Models for Discourse
- 2021
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Konferensbidrag (refereegranskat)abstract
- Pre-trained multilingual language models have become an important building block in multilingual natural language processing. In the present paper, we investigate a range of such models to find out how well they transfer discourse-level knowledge across languages. This is done with a systematic evaluation on a broader set of discourse-level tasks than has been previously been assembled. We find that the XLM-RoBERTa family of models consistently show the best performance, by simultaneously being good monolingual models and degrading relatively little in a zero-shot setting. Our results also indicate that model distillation may hurt the ability of cross-lingual transfer of sentence representations, while language dissimilarity at most has a modest effect. We hope that our test suite, covering 5 tasks with a total of 22 languages in 10 distinct families, will serve as a useful evaluation platform for multilingual performance at and beyond the sentence level.
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