| 1. |
- Andersson, Marta, et al.
(författare)
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A sentiment-annotated dataset of English causal connectives
- 2020
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Ingår i: Proceedings of the 14th Linguistic Annotation Workshop. - 9781952148330 ; , s. 24-33
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Konferensbidrag (refereegranskat)abstract
- This paper investigates the semantic prosody of three causal connectives: due to, owing to and because of in seven varieties of the English language. While research in the domain of English causality exists, we are not aware of studies that would cover the domain of causal connectives in English. Our claim is that connectives such as because of link two arguments, (at least) one of which will include a phrase that contributes to the interpretation of the relation as positive or negative, and hence define the prosody of the connective used. As our results demonstrate, the majority of the prosodies identified are negative for all three connectives; the proportions are stable across the varieties of English studied, and contrary to our expectations, we find no significant differences between the functions of the connectives and discourse preferences. Further, we investigate whether automatizing the sentiment annotation procedure via a simple language-model based classifier is possible. The initial results highlights the complexity of the task and the need for complicated systems, probably aided with other related datasets to achieve reasonable performance.
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| 2. |
- Bjerva, Johannes, et al.
(författare)
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Cross-lingual Learning of Semantic Textual Similarity with Multilingual Word Representations
- 2017
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Ingår i: Proceedings of the 21st Nordic Conference on Computational Linguistics. - Linköping : Linköping University Electronic Press. - 9789176856017 ; , s. 211-215
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Konferensbidrag (refereegranskat)abstract
- Assessing the semantic similarity between sentences in different languages is challenging. We approach this problem by leveraging multilingual distributional word representations, where similar words in different languages are close to each other. The availability of parallel data allows us to train such representations on a large amount of languages. This allows us to leverage semantic similarity data for languages for which no such data exists. We train and evaluate on five language pairs, including English, Spanish, and Arabic. We are able to train wellperforming systems for several language pairs, without any labelled data for that language pair.
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| 3. |
- Bjerva, Johannes, et al.
(författare)
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What Do Language Representations Really Represent?
- 2019
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Ingår i: Computational linguistics - Association for Computational Linguistics (Print). - : MIT Press - Journals. - 0891-2017 .- 1530-9312. ; 45:2, s. 381-389
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just as it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, whereas genetic relationships—a convenient benchmark used for evaluation in previous work—appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.
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| 4. |
- Falkenjack, Johan, 1986-
(författare)
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Towards a Model of General Text Complexity for Swedish
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In an increasingly networked world, where the amount of written information is growing at a rate never before seen, the ability to read and absorb written information is of utmost importance for anything but a superficial understanding of life's complexities. That is an example of a sentence which is not very easy to read. It can be said to have a relatively high degree of text complexity. Nevertheless, the sentence is also true. It is important to be able to read and understand written materials. While not everyone might have a job where they have to read a lot, access to written material is necessary in order to participate in modern society. Most information, from news reporting, to medical information, to governmental information, come primarily in a written form.But what makes the sentence at the start of this abstract so complex? We can probably all agree that the length is part of it. But then what? Researches in the field of readability and text complexity analysis have been studying this question for almost 100 years. That research has over time come to include many computational and data driven methods within the field of computational linguistics.This thesis cover some of my contributions to this field of research, though with a main focus on Swedish rather than English text. It aims to explore two primary questions (1) Which linguistic features are most important when assessing text complexity in Swedish? and (2) How can we deal with the problem of data sparsity with regards to complexity annotated texts in Swedish?The first issue is tackled by exploring the task of identifying easy-to-read ("lättläst") text using classification with Support Vector Machines. A large set of linguistic features is evaluated with regards to predictive performance and is shown to separate easy-to-read texts from regular texts with a very high accuracy. Meanwhile, using a genetic algorithm for variable selection, we find that almost the same accuracy can be reached with only 8 features. This implies that this classification problem is not very hard and that results might not generalize to comparing less easy-to-read texts.This, in turn, brings us to the second question. Except for easy-to-read labeled texts, the data with text complexity annotations is very sparse. It consist of multiple small corpora using different scales to label documents. To deal with this problem, we propose a novel statistical model. The model belongs to the larger family of Probit models and is implemented in a Bayesian fashion and estimated using a Gibbs sampler based on extending a well established Gibbs sampler for the Ordered Probit model. This model is evaluated using both simulated and real world readability data with very promising results.
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| 5. |
- Gärtner, Manja, 1986-
(författare)
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Prosocial Behavior and Redistributive Preferences
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This Ph.D. thesis contains four independent essays. The essays are summarized as follows.Essay I: Status quos and the prosociality of intuitive decision makingThis study investigates how the prosociality of intuitive choices depends on the presence of a status quo. I present the results of a dictator game experiment with a non-student sample. The dictator game is a choice between a selfish option and a fair and efficient option, and has either no status quo, a selfish status quo or a fair status quo. Intuitive choices are elicited in two ways, by an exogenous variation in time pressure and by measuring response times. I find that time pressure decreases the share of fair choices in decisions without a status quo, but has no effect in the presence of a status quo. Fair and selfish choices have equal response times in a decision without a status quo, whereas the status quo option is always chosen faster, i.e. fast choices are fair under a fair status quo and selfish under a selfish status quo. This suggests that the decision context critically affects whether intuitive choices are prosocial or selfish.Essay II: Risk preferences and the demand for redistributionIf individuals view redistributive policy as an insurance against future negative economic shocks, then the demand for redistribution increases in individual risk aversion. We provide a direct test of the correlation between the demand for redistribution and individual risk aversion in a customized survey and find that they are strongly and robustly positively correlated: more risk averse people demand more redistribution. We also replicate the results from previous literature and, on the one hand, find that the demand for redistribution is positively correlated with altruism, the belief that individual economic success is the result of luck rather than effort, a working-class parental background and downward mobility experience and expectations. On the other hand, preferences for redistribution are negatively correlated with income, a conservative political ideology and upward mobility experience and expectations. The magnitude of the correlation between risk aversion and the demand for redistribution is comparable to the magnitude of these previously identified, and here replicated, correlates. Essay III: Omission effects in trolley problems with economic outcomesThis paper tests how ethical views and hypothetical choices in a trolley problem with economic outcomes depend on whether an outcome is the result of an action or an omission. In a vignette experiment, subjects read about a spectator that harms one person in order to save five others from harm either by taking an action or by omission, whereas the outcomes are either death or loss of property. The results show that the distinction between harmful actions and harmful omissions is significantly smaller in the economic domain, suggesting that omission effects in trolley problems are domain-specific. A comparison of moral views about harmful actions across outcome domains shows that this difference is driven by subjects being more outcome-focused when property rather than lives are at stake. Essay IV: Is there an omission effect in prosocial behavior?We investigate whether individuals are more prone to act selfishly if they can passively allow for an outcome to be implemented (omission) rather than having to make an active choice (commission). In most settings, active and passive choice alternatives differ in terms of factors such as the presence of a suggested option, costs of taking an action, and awareness. We isolate the omission effect from confounding factors in two experiments, and find no evidence that the distinction between active and passive choices has an independent effect on the propensity to implement selfish outcomes. This suggests that increased selfishness through omission, as observed in various economic choice situations, is driven by other factors than a preference for selfish omissions.
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| 6. |
- 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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| 7. |
- 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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| 8. |
- 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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| 9. |
- 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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| 10. |
- Levshina, Natalia, et al.
(författare)
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Revered and reviled : a sentiment analysis of female and male referents in three languages
- 2024
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Ingår i: Frontiers in Communication. - 2297-900X. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Our study contributes to the less explored domain of lexical typology, focusing on semantic prosody and connotation. Semantic derogation, or pejoration of nouns referring to women, whereby such words acquire connotations and further denotations of social pejoration, immorality and/or loose sexuality, has been a very prominent question in studies on gender and language (change). It has been argued that pejoration emerges due to the general derogatory attitudes toward female referents. However, the evidence for systematic differences in connotations of female- vs. male-related words is fragmentary and often fairly impressionistic; moreover, many researchers argue that expressed sentiments toward women (as well as men) often are ambivalent. One should also expect gender differences in connotations to have decreased in the recent years, thanks to the advances of feminism and social progress. We test these ideas in a study of positive and negative connotations of feminine and masculine term pairs such as woman - man, girl - boy, wife - husband, etc. Sentences containing these words were sampled from diachronic corpora of English, Chinese and Russian, and sentiment scores for every word were obtained using two systems for Aspect-Based Sentiment Analysis: PyABSA, and OpenAI's large language model GPT-3.5. The Generalized Linear Mixed Models of our data provide no indications of significantly more negative sentiment toward female referents in comparison with their male counterparts. However, some of the models suggest that female referents are more infrequently associated with neutral sentiment than male ones. Neither do our data support the hypothesis of the diachronic convergence between the genders. In sum, results suggest that pejoration is unlikely to be explained simply by negative attitudes to female referents in general.
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