247 research outputs found
個人が用いる単語の意味のモデル化とその応用
学位の種別: 修士University of Tokyo(東京大学
PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Prompts
The meanings of words and phrases depend not only on where they are used
(contexts) but also on who use them (writers). Pretrained language models
(PLMs) are powerful tools for capturing context, but they are typically
pretrained and fine-tuned for universal use across different writers. This
study aims to improve the accuracy of text understanding tasks by personalizing
the fine-tuning of PLMs for specific writers. We focus on a general setting
where only the plain text from target writers are available for
personalization. To avoid the cost of fine-tuning and storing multiple copies
of PLMs for different users, we exhaustively explore using writer-specific
prompts to personalize a unified PLM. Since the design and evaluation of these
prompts is an underdeveloped area, we introduce and compare different types of
prompts that are possible in our setting. To maximize the potential of
prompt-based personalized fine-tuning, we propose a personalized intermediate
learning based on masked language modeling to extract task-independent traits
of writers' text. Our experiments, using multiple tasks, datasets, and PLMs,
reveal the nature of different prompts and the effectiveness of our
intermediate learning approach.Comment: 11 page
The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias
The way the media presents events can significantly affect public perception,
which in turn can alter people's beliefs and views. Media bias describes a
one-sided or polarizing perspective on a topic. This article summarizes the
research on computational methods to detect media bias by systematically
reviewing 3140 research papers published between 2019 and 2022. To structure
our review and support a mutual understanding of bias across research domains,
we introduce the Media Bias Taxonomy, which provides a coherent overview of the
current state of research on media bias from different perspectives. We show
that media bias detection is a highly active research field, in which
transformer-based classification approaches have led to significant
improvements in recent years. These improvements include higher classification
accuracy and the ability to detect more fine-granular types of bias. However,
we have identified a lack of interdisciplinarity in existing projects, and a
need for more awareness of the various types of media bias to support
methodologically thorough performance evaluations of media bias detection
systems. Concluding from our analysis, we see the integration of recent machine
learning advancements with reliable and diverse bias assessment strategies from
other research areas as the most promising area for future research
contributions in the field
A Dynamic Embedding Model of the Media Landscape
Information about world events is disseminated through a wide variety of news
channels, each with specific considerations in the choice of their reporting.
Although the multiplicity of these outlets should ensure a variety of
viewpoints, recent reports suggest that the rising concentration of media
ownership may void this assumption. This observation motivates the study of the
impact of ownership on the global media landscape and its influence on the
coverage the actual viewer receives. To this end, the selection of reported
events has been shown to be informative about the high-level structure of the
news ecosystem. However, existing methods only provide a static view into an
inherently dynamic system, providing underperforming statistical models and
hindering our understanding of the media landscape as a whole.
In this work, we present a dynamic embedding method that learns to capture
the decision process of individual news sources in their selection of reported
events while also enabling the systematic detection of large-scale
transformations in the media landscape over prolonged periods of time. In an
experiment covering over 580M real-world event mentions, we show our approach
to outperform static embedding methods in predictive terms. We demonstrate the
potential of the method for news monitoring applications and investigative
journalism by shedding light on important changes in programming induced by
mergers and acquisitions, policy changes, or network-wide content diffusion.
These findings offer evidence of strong content convergence trends inside large
broadcasting groups, influencing the news ecosystem in a time of increasing
media ownership concentration
Methods for detecting and mitigating linguistic bias in text corpora
Im Zuge der fortschreitenden Ausbreitung des Webs in alle Aspekte des täglichen
Lebens wird Bias in Form von Voreingenommenheit und versteckten Meinungen zu einem
zunehmend herausfordernden Problem. Eine weitverbreitete Erscheinungsform ist Bias in
Textdaten. Um dem entgegenzuwirken hat die Online-Enzyklopädie Wikipedia das Prinzip
des neutralen Standpunkts (Englisch: Neutral Point of View, kurz: NPOV) eingeführt,
welcher die Verwendung neutraler Sprache und die Vermeidung von einseitigen oder subjektiven
Formulierungen vorschreibt. Während Studien gezeigt haben, dass die Qualität von
Wikipedia-Artikel mit der Qualität von Artikeln in klassischen Enzyklopädien vergleichbar
ist, zeigt die Forschung gleichzeitig auch, dass Wikipedia anfällig für verschiedene Typen
von NPOV-Verletzungen ist. Bias zu identifizieren, kann eine herausfordernde Aufgabe sein,
sogar für Menschen, und mit Millionen von Artikeln und einer zurückgehenden Anzahl von
Mitwirkenden wird diese Aufgabe zunehmend schwieriger. Wenn Bias nicht eingedämmt
wird, kann dies nicht nur zu Polarisierungen und Konflikten zwischen Meinungsgruppen
führen, sondern Nutzer auch negativ in ihrer freien Meinungsbildung beeinflussen. Hinzu
kommt, dass sich Bias in Texten und in Ground-Truth-Daten negativ auf Machine Learning
Modelle, die auf diesen Daten trainiert werden, auswirken kann, was zu diskriminierendem
Verhalten von Modellen führen kann.
In dieser Arbeit beschäftigen wir uns mit Bias, indem wir uns auf drei zentrale Aspekte
konzentrieren: Bias-Inhalte in Form von geschriebenen Aussagen, Bias von Crowdworkern
während des Annotierens von Daten und Bias in Word Embeddings Repräsentationen.
Wir stellen zwei Ansätze für die Identifizierung von Aussagen mit Bias in Textsammlungen
wie Wikipedia vor. Unser auf Features basierender Ansatz verwendet Bag-of-Word
Features inklusive einer Liste von Bias-Wörtern, die wir durch das Identifizieren von Clustern
von Bias-Wörtern im Vektorraum von Word Embeddings zusammengestellt haben.
Unser verbesserter, neuronaler Ansatz verwendet Gated Recurrent Neural Networks, um
Kontext-Abhängigkeiten zu erfassen und die Performance des Modells weiter zu verbessern.
Unsere Studie zum Thema Crowd Worker Bias deckt Bias-Verhalten von Crowdworkern
mit extremen Meinungen zu einem bestimmten Thema auf und zeigt, dass dieses Verhalten
die entstehenden Ground-Truth-Label beeinflusst, was wiederum Einfluss auf die Erstellung
von Datensätzen für Aufgaben wie Bias Identifizierung oder Sentiment Analysis hat. Wir
stellen Ansätze für die Abschwächung von Worker Bias vor, die Bewusstsein unter den
Workern erzeugen und das Konzept der sozialen Projektion verwenden.
Schließlich beschäftigen wir uns mit dem Problem von Bias in Word Embeddings,
indem wir uns auf das Beispiel von variierenden Sentiment-Scores für Namen konzentrieren.
Wir zeigen, dass Bias in den Trainingsdaten von den Embeddings erfasst und an
nachgelagerte Modelle weitergegeben wird. In diesem Zusammenhang stellen wir einen
Debiasing-Ansatz vor, der den Bias-Effekt reduziert und sich positiv auf die produzierten
Label eines nachgeschalteten Sentiment Classifiers auswirkt
Dynamic Contextualized Word Embeddings
Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and extralinguistic context. Based on a pretrained language model (PLM), dynamic contextualized word embeddings model time and social space jointly, which makes them attractive for a range of NLP tasks involving semantic variability. We highlight potential application scenarios by means of qualitative and quantitative analyses on four English datasets
The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphs
Knowledge graphs are increasingly used in a plethora of downstream tasks or
in the augmentation of statistical models to improve factuality. However,
social biases are engraved in these representations and propagate downstream.
We conducted a critical analysis of literature concerning biases at different
steps of a knowledge graph lifecycle. We investigated factors introducing bias,
as well as the biases that are rendered by knowledge graphs and their embedded
versions afterward. Limitations of existing measurement and mitigation
strategies are discussed and paths forward are proposed.Comment: Accepted to AACL-IJCNLP 202
Explainability in Music Recommender Systems
The most common way to listen to recorded music nowadays is via streaming
platforms which provide access to tens of millions of tracks. To assist users
in effectively browsing these large catalogs, the integration of Music
Recommender Systems (MRSs) has become essential. Current real-world MRSs are
often quite complex and optimized for recommendation accuracy. They combine
several building blocks based on collaborative filtering and content-based
recommendation. This complexity can hinder the ability to explain
recommendations to end users, which is particularly important for
recommendations perceived as unexpected or inappropriate. While pure
recommendation performance often correlates with user satisfaction,
explainability has a positive impact on other factors such as trust and
forgiveness, which are ultimately essential to maintain user loyalty.
In this article, we discuss how explainability can be addressed in the
context of MRSs. We provide perspectives on how explainability could improve
music recommendation algorithms and enhance user experience. First, we review
common dimensions and goals of recommenders' explainability and in general of
eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which
these apply -- or need to be adapted -- to the specific characteristics of
music consumption and recommendation. Then, we show how explainability
components can be integrated within a MRS and in what form explanations can be
provided. Since the evaluation of explanation quality is decoupled from pure
accuracy-based evaluation criteria, we also discuss requirements and strategies
for evaluating explanations of music recommendations. Finally, we describe the
current challenges for introducing explainability within a large-scale
industrial music recommender system and provide research perspectives.Comment: To appear in AI Magazine, Special Topic on Recommender Systems 202
Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function
Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which
computational analyses align best with the targeted neurocognitive/psychological functions that we want to
assess. In this paper we reflect on two decades of experience with the application of language-based assessment
to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it
should be measured and why we are measuring the phenomena. We address the questions by advocating for a
principled framework for aligning computational models to the constructs being assessed and the tasks being
used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the
accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled
approach can further the goal of transitioning language-based computational assessments to part of clinical
practice while gaining the trust of critical stakeholders
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