2,583 research outputs found
Cognitive modeling and learning with sparse binary hypervectors
Following the general theoretical framework of VSA (Vector Symbolic
Architecture), a cognitive model with the use of sparse binary hypervectors is
proposed. In addition, learning algorithms are introduced to bootstrap the
model from incoming data stream, with much improved transparency and
efficiency. Mimicking human cognitive process, the training can be performed
online while inference is in session. Word-level embedding is re-visited with
such hypervectors, and further applications in the field of NLP (Natural
Language Processing) are explored.Comment: 12 page
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Modeling the Multi-mode Distribution in Self-Supervised Language Models
Self-supervised large language models (LMs) have become a highly-influential and foundational tool for many NLP models. For this reason, their expressivity is an important topic of study. In near-universal practice, given the language context, the model predicts a word from the vocabulary using a single embedded vector representation of both context and dictionary entries. Note that the context sometimes implies that the distribution over predicted words should be multi-modal in embedded space. However, the context’s single-vector representation provably fails to capture such a distribution. To address this limitation, we propose to represent context with multiple vector embeddings, which we term facets. This is distinct from previous work on multi-sense vocabulary embeddings, which employs multiple vectors for the dictionary entries, not the context.
In this dissertation, we first present the theoretical limitations of the single context embedding in LMs and how the theoretical analyses suggest new alternative softmax layers that encode a context as multiple embeddings. The proposed alternatives achieve better perplexity than the mixture of softmax (MoS), especially given an ambiguous context, without adding significant computational cost to LMs. Our approaches also let GPT-2 learn to properly copy the entities from the context, which increases the coherence of the generated text without requiring any labels.
In addition to predicting the next word, we also use multiple CLS embeddings to improve state-of-the-art pretraining methods for BERT on natural language understanding (NLU) benchmarks without introducing significant extra parameters or computations, especially when the training datasets are small. Furthermore, we show that our multi-facet embeddings improve the sequential recommendation, scientific paper embeddings, measurement of sentence similarity, distantly supervised relation extraction, unsupervised text pattern entailment detection, and cold-start citation recommendation. Finally, we use the multiple vector embeddings to predict the future topics of a context, and build on the basis, we propose a novel interactive language generation framework
How research programs come apart: the example of supersymmetry and the disunity of physics
According to Peter Galison, the coordination of different ``subcultures''
within a scientific field happens through local exchanges within ``trading
zones''. In his view, the workability of such trading zones is not guaranteed,
and science is not necessarily driven towards further integration. In this
paper, we develop and apply quantitative methods (using semantic, authorship,
and citation data from scientific literature), inspired by Galison's framework,
to the case of the disunity of high-energy physics. We give prominence to
supersymmetry, a concept that has given rise to several major but distinct
research programs in the field, such as the formulation of a consistent theory
of quantum gravity or the search for new particles. We show that ``theory'' and
`phenomenology'' in high-energy physics should be regarded as distinct
theoretical subcultures, between which supersymmetry has helped sustain
scientific ``trades''. However, as we demonstrate using a topic model, the
phenomenological component of supersymmetry research has lost traction and the
ability of supersymmetry to tie these subcultures together is now compromised.
Our work supports that even fields with an initially strong sentiment of unity
may eventually generate diverging research programs and demonstrates the
fruitfulness of the notion of trading zones for informing quantitative
approaches to scientific pluralism
Comparing the E-Z Reader Model to Other Models of Eye Movement Control in Reading
The E-Z Reader model provides a theoretical framework for understanding how word identification, visual processing, attention, and oculomotor control jointly determine when and where the eyes move during reading. Thus, in contrast to other reading models reviewed in this article, E-Z Reader can simultaneously account for many of the known effects of linguistic, visual, and oculomotor factors on eye movement control during reading. Furthermore, the core principles of the model have been generalized to other task domains (e.g., equation solving, visual search), and are broadly consistent with what is known about the architecture of the neural systems that support reading
Enhancing Word Representation Learning with Linguistic Knowledge
Representation learning, the process whereby representations are modelled from data, has recently become a central part of Natural Language Processing (NLP). Among the most widely used learned representations are word embeddings trained on large corpora of unannotated text, where the learned embeddings are treated as general representations that can be used across multiple NLP tasks. Despite their empirical successes, word embeddings learned entirely from data can only capture patterns of language usage from the particular linguistic domain of the training data. Linguistic knowledge, which does not vary among linguistic domains, can potentially be used to address this limitation. The vast sources of linguistic knowledge that are readily available nowadays can help train more general word embeddings (i.e. less affected by distance between linguistic domains) by providing them with such information as semantic relations, syntactic structure, word morphology, etc.
In this research, I investigate the different ways in which word embedding models capture and encode words’ semantic and contextual information. To this end, I propose two approaches to integrate linguistic knowledge into the statistical learning of word embeddings. The first approach is based on augmenting the training data for a well-known Skip-gram word embedding model, where synonym information is extracted from a lexical knowledge base and incorporated into the training data in the form of additional training examples. This data augmentation approach seeks to enforce synonym relations in the learned embeddings. The second approach exploits structural information in text by transforming every sentence in the data into its corresponding dependency parse trees and training an autoencoder to recover the original sentence. While learning a mapping from a dependency parse tree to its originating sentence, this novel Structure-to-Sequence (Struct2Seq) model produces word embeddings that contain information about a word’s structural context. Given that the combination of knowledge and statistical methods can often be unpredictable, a central focus of this thesis is on understanding the effects of incorporating linguistic knowledge into word representation learning. Through the use of intrinsic (geometric characteristics) and extrinsic (performance on downstream tasks) evaluation metrics, I aim to measure the specific influence that the injected knowledge can have on different aspects of the informational composition of word embeddings
Computational approaches to semantic change (Volume 6)
Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans
SEGMENTATION, RECOGNITION, AND ALIGNMENT OF COLLABORATIVE GROUP MOTION
Modeling and recognition of human motion in videos has broad applications in behavioral biometrics, content-based visual data analysis, security and surveillance, as well as designing interactive environments. Significant progress has been made in the past two decades by way of new models, methods, and implementations. In this dissertation, we focus our attention on a relatively less investigated sub-area called collaborative group motion analysis. Collaborative group motions are those that typically involve multiple objects, wherein the motion patterns of individual objects may vary significantly in both space and time, but the collective motion pattern of the ensemble allows characterization in terms of geometry and statistics. Therefore, the motions or activities of an individual object constitute local information. A framework to synthesize all local information into a holistic view, and to explicitly characterize interactions among objects, involves large scale global reasoning, and is of significant complexity. In this dissertation, we first review relevant previous contributions on human motion/activity modeling and recognition, and then propose several approaches to answer a sequence of traditional vision questions including 1) which of the motion elements among all are the ones relevant to a group motion pattern of interest (Segmentation); 2) what is the underlying motion pattern (Recognition); and 3) how two motion ensembles are similar and how we can 'optimally' transform one to match the other (Alignment). Our primary practical scenario is American football play, where the corresponding problems are 1) who are offensive players; 2) what are the offensive strategy they are using; and 3) whether two plays are using the same strategy and how we can remove the spatio-temporal misalignment between them due to internal or external factors. The proposed approaches discard traditional modeling paradigm but explore either concise descriptors, hierarchies, stochastic mechanism, or compact generative model to achieve both effectiveness and efficiency.
In particular, the intrinsic geometry of the spaces of the involved features/descriptors/quantities is exploited and statistical tools are established on these nonlinear manifolds. These initial attempts have identified new challenging problems in complex motion analysis, as well as in more general tasks in video dynamics. The insights gained from nonlinear geometric modeling and analysis in this dissertation may hopefully be useful toward a broader class of computer vision applications
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
BNAIC 2008:Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference
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