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- 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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| 2. |
- Devinney, Hannah, 1995-
(författare)
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Gender and representation : investigations of bias in natural language processing
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Natural Language Processing (NLP) technologies are a part of our every day realities. They come in forms we can easily see as ‘language technologies’ (auto-correct, translation services, search results) as well as those that fly under our radar (social media algorithms, 'suggested reading' recommendations on news sites, spam filters). NLP fuels many other tools under the Artificial Intelligence umbrella – such as algorithms approving for loan applications – which can have major material effects on our lives. As large language models like ChatGPT have become popularized, we are also increasingly exposed to machine-generated texts.Machine Learning (ML) methods, which most modern NLP tools rely on, replicate patterns in their training data. Typically, these language data are generated by humans, and contain both overt and underlying patterns that we consider socially undesirable, comprising stereotypes and other reflections of human prejudice. Such patterns (often termed 'bias') are picked up and repeated, or even made more extreme, by ML systems. Thus, NLP technologies become a part of the linguistic landscapes in which we humans transmit stereotypes and act on our prejudices. They may participate in this transmission by, for example, translating nurses as women (and doctors as men) or systematically preferring to suggest promoting men over women. These technologies are tools in the construction of power asymmetries not only through the reinforcement of hegemony, but also through the distribution of material resources when they are included in decision-making processes such as screening job applications.This thesis explores gendered biases, trans and nonbinary inclusion, and queer representation within NLP through a feminist and intersectional lens. Three key areas are investigated: the ways in which “gender” is theorized and operationalized by researchers investigating gender bias in NLP; gendered associations within datasets used for training language technologies; and the representation of queer (particularly trans and nonbinary) identities in the output of both low-level NLP models and large language models (LLMs). The findings indicate that nonbinary people/genders are erased by both bias in NLP tools/datasets, and by research/ers attempting to address gender biases. Men and women are also held to cisheteronormative standards (and stereotypes), which is particularly problematic when considering the intersection of gender and sexuality. Although it is possible to mitigate some of these issues in particular circumstances, such as addressing erasure by adding more examples of nonbinary language to training data, the complex nature of the socio-technical landscape which NLP technologies are a part of means that simple fixes may not always be sufficient. Additionally, it is important that ways of measuring and mitigating 'bias' remain flexible, as our understandings of social categories, stereotypes and other undesirable norms, and 'bias' itself will shift across contexts such as time and linguistic setting.
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