8,000 research outputs found
GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering
In this work, we present an end-to-end Knowledge Graph Question Answering
(KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text
pre-trained language model. The model takes a question in natural language as
input and produces a simpler form of the intended SPARQL query. In the simpler
form, the model does not directly produce entity and relation IDs. Instead, it
produces corresponding entity and relation labels. The labels are grounded to
KG entity and relation IDs in a subsequent step. To further improve the
results, we instruct the model to produce a truncated version of the KG
embedding for each entity. The truncated KG embedding enables a finer search
for disambiguation purposes. We find that T5 is able to learn the truncated KG
embeddings without any change of loss function, improving KGQA performance. As
a result, we report strong results for LC-QuAD 2.0 and SimpleQuestions-Wikidata
datasets on end-to-end KGQA over Wikidata.Comment: 16 pages single column format accepted at ESWC 2023 research trac
Foundations for programming and implementing effect handlers
First-class control operators provide programmers with an expressive and efficient
means for manipulating control through reification of the current control state as a first-class object, enabling programmers to implement their own computational effects and
control idioms as shareable libraries. Effect handlers provide a particularly structured
approach to programming with first-class control by naming control reifying operations
and separating from their handling.
This thesis is composed of three strands of work in which I develop operational
foundations for programming and implementing effect handlers as well as exploring
the expressive power of effect handlers.
The first strand develops a fine-grain call-by-value core calculus of a statically
typed programming language with a structural notion of effect types, as opposed to the
nominal notion of effect types that dominates the literature. With the structural approach,
effects need not be declared before use. The usual safety properties of statically typed
programming are retained by making crucial use of row polymorphism to build and
track effect signatures. The calculus features three forms of handlers: deep, shallow,
and parameterised. They each offer a different approach to manipulate the control state
of programs. Traditional deep handlers are defined by folds over computation trees,
and are the original con-struct proposed by Plotkin and Pretnar. Shallow handlers are
defined by case splits (rather than folds) over computation trees. Parameterised handlers
are deep handlers extended with a state value that is threaded through the folds over
computation trees. To demonstrate the usefulness of effects and handlers as a practical
programming abstraction I implement the essence of a small UNIX-style operating
system complete with multi-user environment, time-sharing, and file I/O.
The second strand studies continuation passing style (CPS) and abstract machine
semantics, which are foundational techniques that admit a unified basis for implementing deep, shallow, and parameterised effect handlers in the same environment. The
CPS translation is obtained through a series of refinements of a basic first-order CPS
translation for a fine-grain call-by-value language into an untyped language. Each refinement moves toward a more intensional representation of continuations eventually
arriving at the notion of generalised continuation, which admit simultaneous support for
deep, shallow, and parameterised handlers. The initial refinement adds support for deep
handlers by representing stacks of continuations and handlers as a curried sequence of
arguments. The image of the resulting translation is not properly tail-recursive, meaning some function application terms do not appear in tail position. To rectify this the
CPS translation is refined once more to obtain an uncurried representation of stacks
of continuations and handlers. Finally, the translation is made higher-order in order to
contract administrative redexes at translation time. The generalised continuation representation is used to construct an abstract machine that provide simultaneous support for
deep, shallow, and parameterised effect handlers. kinds of effect handlers.
The third strand explores the expressiveness of effect handlers. First, I show that
deep, shallow, and parameterised notions of handlers are interdefinable by way of typed
macro-expressiveness, which provides a syntactic notion of expressiveness that affirms
the existence of encodings between handlers, but it provides no information about the
computational content of the encodings. Second, using the semantic notion of expressiveness I show that for a class of programs a programming language with first-class
control (e.g. effect handlers) admits asymptotically faster implementations than possible in a language without first-class control
A productive response to legacy system petrification
Requirements change. The requirements of a legacy information system change, often in unanticipated ways, and at a more rapid pace than the rate at which the information system itself can be evolved to support them. The capabilities of a legacy system progressively fall further and further behind their evolving requirements, in a degrading process termed petrification. As systems petrify, they deliver diminishing business value, hamper business effectiveness, and drain organisational resources. To address legacy systems, the first challenge is to understand how to shed their resistance to tracking requirements change. The second challenge is to ensure that a newly adaptable system never again petrifies into a change resistant legacy system. This thesis addresses both challenges. The approach outlined herein is underpinned by an agile migration process - termed Productive Migration - that homes in upon the specific causes of petrification within each particular legacy system and provides guidance upon how to address them. That guidance comes in part from a personalised catalogue of petrifying patterns, which capture recurring themes underlying petrification. These steer us to the problems actually present in a given legacy system, and lead us to suitable antidote productive patterns via which we can deal with those problems one by one. To prevent newly adaptable systems from again degrading into legacy systems, we appeal to a follow-on process, termed Productive Evolution, which embraces and keeps pace with change rather than resisting and falling behind it. Productive Evolution teaches us to be vigilant against signs of system petrification and helps us to nip them in the bud. The aim is to nurture systems that remain supportive of the business, that are adaptable in step with ongoing requirements change, and that continue to retain their value as significant business assets
Chinese Benteng Women’s Participation in Local Development Affairs in Indonesia: Appropriate means for struggle and a pathway to claim citizen’ right?
It had been more than two decades passing by aftermath the devastating Asia’s Financial Crisis in 1997, subsequently followed by Suharto’s step down from his presidential throne which he occupied for more than three decades. The financial turmoil turned to a political disaster furthermore has led to massive looting that severely impacted Indonesians of Chinese descendant, including unresolved mystery of the most atrocious sexual violation against women and covert killings of students and democracy activists in this country. Since then, precisely aftermath May 1998, which publicly known as “Reformasi”1, Indonesia underwent political reform that eventually corresponded positively to its macroeconomic growth. Twenty years later, in 2018, Indonesia captured worldwide attention because it has successfully hosted two internationally renowned events, namely the Asian Games 2018 – the most prestigious sport events in Asia – conducted in Jakarta and Palembang; and the IMF/World Bank Annual Meeting 2018 in Bali. Particularly in the IMF/World Bank Annual Meeting, this event has significantly elevated Indonesia’s credibility and international prestige in the global economic powerplay as one of the nations with promising growth and openness. However, the narrative about poverty and inequality, including increasing racial tension, religious conservatism, and sexual violation against women are superseded by friendly climate for foreign investment and eventually excessive glorification of the nation’s economic growth. By portraying the image of promising new economic power, as rhetorically promised by President Joko Widodo during his presidential terms, Indonesia has swept the growing inequality in this highly stratified society that historically compounded with religious and racial tension under the carpet of digital economy.Arte y Humanidade
QAnon Propaganda on Twitter as Information Warfare: Influencers, Networks, and Narratives
QAnon refers to a set of far-right, conspiratorial ideologies that have risen
in popularity in the U.S. since their initial promotion in 2017 on the 4chan
internet message board. A central narrative element of QAnon is that a powerful
group of elite, liberal members of the Democratic Party engage in morally
reprehensible practices, but that former U.S. President Donald J. Trump was
prosecuting them. Five studies investigated the influence and network
connectivity of accounts promoting QAnon on Twitter from August, 2020 through
January, 2021. Selection of Twitter accounts emphasized on-line influencers and
"persons of interest" known or suspected of participation in QAnon propaganda
promotion activities. Evidence of large-scale coordination among accounts
promoting QAnon was observed, demonstrating rigorous, quantitative evidence of
"astroturfing" in QAnon propaganda promotion on Twitter, as opposed to strictly
"grassroots" activities of citizens acting independently. Further, evidence was
obtained supporting that networks of extreme far-right adherents engaged in
organized QAnon propaganda promotion, as revealed by network overlap among
accounts promoting far-right extremist (e.g., anti-Semitic) content and
insurrectionist themes; New Age, occult, and "esoteric" themes; and internet
puzzle games like Cicada 3301 and other "alternate reality games." Based on
well-grounded theories and findings from the social sciences, it is argued that
QAnon propaganda on Twitter in the months circa the 2020 U.S. Presidential
election likely reflected joint participation of multiple actors, including
nation-states like Russia, in innovative misuse of social media toward
undermining democratic processes by promoting "magical" thinking, ostracism of
Democrats and liberals, and salience of White extinction narratives common
among otherwise ideologically diverse groups on the extreme far-right.Comment: 60 pages, 14 figure
WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
Existing benchmarks for grounding language in interactive environments either
lack real-world linguistic elements, or prove difficult to scale up due to
substantial human involvement in the collection of data or feedback signals. To
bridge this gap, we develop WebShop -- a simulated e-commerce website
environment with million real-world products and crowd-sourced
text instructions. Given a text instruction specifying a product requirement,
an agent needs to navigate multiple types of webpages and issue diverse actions
to find, customize, and purchase an item. WebShop provides several challenges
for language grounding including understanding compositional instructions,
query (re-)formulation, comprehending and acting on noisy text in webpages, and
performing strategic exploration. We collect over human demonstrations
for the task, and train and evaluate a diverse range of agents using
reinforcement learning, imitation learning, and pre-trained image and language
models. Our best model achieves a task success rate of , which
outperforms rule-based heuristics () but is far lower than human expert
performance (). We also analyze agent and human trajectories and ablate
various model components to provide insights for developing future agents with
stronger language understanding and decision making abilities. Finally, we show
that agents trained on WebShop exhibit non-trivial sim-to-real transfer when
evaluated on amazon.com and ebay.com, indicating the potential value of WebShop
in developing practical web-based agents that can operate in the wild.Comment: Project page with code, data, demos: https://webshop-pnlp.github.io.
v2 adds transfer to eBa
Masculinities, vulnerability and negotiated identity: Understanding the reporting behaviours of men who experience violence or otherwise harmful behaviour, within a sex work context
Context The focus of sex work related discussions most commonly falls on female providers of sexual services, and male purchasers. As a result, the often victim-oriented policy response in England and Wales falls short of truly addressing the needs of men who are involved in the sale of sex, with there being limited support available for them and a systemic approach which does not fully recognise the potential for men to face harm within this context. Methods The aim of this study is to explore experiences of and reactions to violence, and otherwise harmful behaviours, faced by men in the context of their sex working, by understanding the lived realities of a sample of men who engage in this type of work. The study takes a phased approach which combines an initial informative quantitative survey, with three subsequent phases of semi-structured interviews with male sex workers, sex work-focused practitioners and police officers. The method is guided by feminist research principles which suggest that reality is situated within those with lived experience, and also by an element of co-creation which has grounded this study within the perspectives of male sex workers from its conception. Findings The findings of this research suggest that all of the men involved in the study had faced at least one of the violent or otherwise harmful behaviours outlined, though reporting of these behaviours was not at all common. Discussions with the male sex working participants, practitioners and the police highlighted the issues related to the structural influences of authority, such as the police, and the social environment, and the internalisation of these wider factors, which create barriers to reporting for groups such as male sex workers and others who face similar social marginalisation. Conclusions This study challenges existing gendered understandings of violence and otherwise harmful behaviour within a sex work context, by highlighting the harmful experiences of men. By exploring these experiences and the reporting behaviours of those involved, the study also proposes a new framework for understanding barriers to reporting, which suggests that these are formed through the influences of formal and informal measures of social control, and the internalisation of these outside influences by the individual. By better understanding the experiences of men, and the barriers to their reporting, this study attempts to nuance a gendered discussion. Within, I propose that in order to better support male sex workers, responses must begin by appreciating the heterogeneity of those involved in sex work and the influence of their individual circumstances and the social environment on their willingness to seek support
"This is my unicorn, Fluffy": Personalizing frozen vision-language representations
Large Vision & Language models pretrained on web-scale data provide
representations that are invaluable for numerous V&L problems. However, it is
unclear how they can be used for reasoning about user-specific visual concepts
in unstructured language. This problem arises in multiple domains, from
personalized image retrieval to personalized interaction with smart devices. We
introduce a new learning setup called Personalized Vision & Language (PerVL)
with two new benchmark datasets for retrieving and segmenting user-specific
"personalized" concepts "in the wild". In PerVL, one should learn personalized
concepts (1) independently of the downstream task (2) allowing a pretrained
model to reason about them with free language, and (3) does not require
personalized negative examples. We propose an architecture for solving PerVL
that operates by extending the input vocabulary of a pretrained model with new
word embeddings for the new personalized concepts. The model can then reason
about them by simply using them in a sentence. We demonstrate that our approach
learns personalized visual concepts from a few examples and can effectively
apply them in image retrieval and semantic segmentation using rich textual
queries
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Few-Shot Natural Language Processing by Meta-Learning Without Labeled Data
Humans show a remarkable capability to accurately solve a wide range of problems efficiently -- utilizing a limited amount of computation and experience. Deep learning models, by stark contrast, can be trained to be highly accurate on a narrow task while being highly inefficient in terms of the amount of compute and data required to reach that accuracy. Within natural language processing (NLP), recent breakthroughs in unsupervised pretraining have enabled reusable models that can be applied to many NLP tasks, however, learning of new tasks is still inefficient. This has led to research on few-shot learning, where the goal is to generalize to new tasks with very few labeled instances. Meta-learning, or learning to learn, treats the learning process itself as a learning problem from data with the goal of learning systems that can generalize to new tasks efficiently. This has the potential to produce few-shot learners that can accurately solve a wide range of new tasks. However, meta-learning requires a distribution over tasks with relevant labeled data that can be difficult to obtain, severely limiting the practical utility of meta-learning methods. In this dissertation, we develop methods to enable large-scale meta-learning from unlabeled text data and improve the few-shot generalization ability of NLP models.
We contribute methods that propose tasks synthetically created from unlabeled text, allowing for a large task distribution for meta-learning. This leads to rapid learning of new tasks by meta-learning from millions of self-supervised tasks and minimizes the train-test mismatch in few-shot learning by optimizing the pre-training directly for future fine-tuning with a few examples. Since real-world applications of NLP require learning diverse tasks with different numbers of classes, we first introduce an optimization-based meta-learning method that can learn from multiple NLP classification tasks with any number of classes. We then leverage the proposed self-supervised approach to create meta-training tasks, with a diverse number of classes, and meta-train models for few-shot learning using this task distribution. This leads to better representation learning, learning key hyper-parameters like learning rates, can be combined with supervised tasks to regularize supervised meta-learning, and leads to accurate few-shot learning on a diverse set of NLP classification tasks. We further explore the space of self-supervised tasks for meta-learning by considering important aspects like task diversity, difficulty, type, domain, and curriculum, and investigate how they affect meta-learning performance. Our analysis shows that all these factors meaningfully alter the task distribution, some inducing significant improvements in downstream few-shot accuracy of the meta-learned models.
Our findings yield accurate and efficient meta-learning methods that improve few-shot generalization to diverse tasks and should enable many future applications of meta-learning in NLP, such as hyper-parameter optimization, continual learning, efficient learning, learning in low-resource languages, and more
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