nils-herrmann/clean_scirepeval huggingface dataset:
A Discriminative Matching Approach to Word Alignment Ben

We present a discriminative, largemargin approach to feature-based matching for word alignment. In this framework, pairs of word tokens receive a matching score, which is based on features of that pair, including measures of association between the words, distortion between their positions, similarity of the orthographic form, and so on. Even with only 100 labeled training examples and simple features which incorporate counts from a large unlabeled corpus, we achieve AER performance close to IBM Model 4, in much less time. Including Model 4 predictions as features, we achieve a relative AER reduction of 22% in over intersected Model 4 alignments.
PageRank using Power Extrapolation

We present a novel technique for speeding up the computation of PageRank, a hyperlink-based estimate of the “importance” o f Web pages, based on the ideas presented in . The original PageRank algorit hm uses the Power Method to compute successive iterates that converge to the p rincipal eigenvector of the Markov matrix representing the Web link graph. The algorithm presented here, called Power Extrapolation, accelerates the c onv rgence of the Power Method by subtracting off the error along several nonprinci pal eigenvectors from the current iterate of the Power Method, making use of known n onprincipal eigenvalues of the Web hyperlink matrix. Empirically, we show tha t using Power Extrapolation speeds up PageRank computation by 30% on a Web gr aph of 80 million nodes in realistic scenarios over the standard power me thod, in a way that is simple to understand and implement.
The Unsupervised Learning of Linguistic Structure

From a machine learning perspective,mostwork in natural languageprocessing can be seenas annotating observed linguistic inputs with unobservedstructure, for example detecting the topic of a text document or choosing a grammatical analysis for a sentence. The rules and patterns which drive such processing are typically either manually crafted by experts or derived automatically from human-annotated, labeled examples. On the other hand, language acquisition in humans is primarily unsupervised: children can learn languagerapidly, accurately, and robustly with lit tle or no explicit supervisory information. While a computerand achild areundeniably vastly different in both situationand context, thefactsof child language acquisition suggestthat there may be sufficient information in raw languagedata to allow the induction of hiddenlinguistic structures.The problemof unsupervisedlearning of languageis important not only in the abstract – thelogical questionof theacquisitionof natural language– but alsoonpractical grounds– labeled training data is expensiveand exists for only a relatively small number of langauges,while unlabeleddata is cheap, and more readily available. This thesisexaminesthe unsupervisedlearning of linguistic structure, primarily focusing on learning tree-st ructuredgrammatical analyses,in which treenodesdominate contiguousspansof wordscalled constituents. Constituents are the linguistically coherent units in a sentence, such as noun phrasesor verb phrases.An aim of a grammar induction systemis to figure out, givenjust the sentencesin a corpus, what treestructurescorrespond to them. It is an openproblemwhetherentirely unsupervisedmethodscan producelinguistically accurateparses of sentences. Due to the difficulty of the grammar induction task, the vast majority of statistical parsing work has focusedon supervisedapproaches,where one usesa treebank of fully parsedsentencesto induce a model which thenparsesunseensentences(Collins1997,Charniak 2000). In early work for this thesis(Klein and Manning 2001a, Klein and Manning 2001b, Klein and Manning 2002), we have shown a theoretically concise,robust method of inducing natural languagegrammar structures.This method not only parsesmuchbetter than therandom-parseunsupervisedparsing baseline,but it is also thefirst unsupervisedparsing systemto score higherthan the standard right-branching baselineon a per-consituent F-score on the ATIS parsing task (seefigure 2 and figure3). The restof the thesiswill explore extensionsof this initial work. First, we will thoroughly investigate the causesfor failure of previous systems. Second, although the above experiments usedparts-of-speech sequencesas input (rather than specific words), the full thesiswill integrate automatic word clustering into theacquisitionmodel. Finally, wewill examinethepotential for using broad syntatic constraint to allow the learning of semantic dependencies.Theseextensionsare describedbelow in more detail.
Evaluating strategies for similarity search on the web

Finding pages on the Web that are similar to a query page (Related Pages) is an important component of modern search engines. A variety of strategies have been proposed for answering Related Pages queries, but comparative evaluation by user studies is expensive, especially when large strategy spaces must be searched (e.g., when tuning parameters). We present a technique for automatically evaluating strategies using Web hierarchies, such as Open Directory, in place of user feedback. We apply this evaluation methodology to a mix of document representation strategies, including the use of text, anchor-text, and links. We discuss the relative advantages and disadvantages of the various approaches examined. Finally, we describe how to efficiently construct a similarity index out of our chosen strategies, and provide sample results from our index.
Similarity Search on the Web: Evaluation and Scalability Considerations

Allowing users to find pages on the web similar to a particular query page is a crucial component of modern search engines. A variety of techniques and approaches exist to support "Related Pages" queries. In this paper we discuss shortcomings of previous approaches and present a unifying approach that puts special emphasis on the use of text, both within anchors and surrounding anchors. In the central contribution of our paper, we present a novel technique for automating the evaluation process, allowing us to tune our parameters to maximize the quality of the results. Finally, we show how to scale our approach to millions of web pages, using the established Locality-Sensitive-Hashing technique.
Working memory and perception

The ability to teach a robot new skills and tasks without explicit programming is an important goal in robotics. Such capability tends to imply the ability to learn from experience, much like many biological creatures. Evidence suggests that working memory plays a pivotal role in this learning process, in part by focusing attention on the most relevant data. We describe ongoing research to study the utility of computational neuroscience models of working memory within robotic systems. A system comprised of working memory, short term memory, long term memory, spatial reasoning and perception modules is proposed. The paper focuses on the perceptual module and its interaction with the working memory. Results are given to show the current progress.
Prefrontal cortex and flexible cognitive control: rules without symbols.

Human cognitive control is uniquely flexible and has been shown to depend on prefrontal cortex (PFC). But exactly how the biological mechanisms of the PFC support flexible cognitive control remains a profound mystery. Existing theoretical models have posited powerful task-specific PFC representations, but not how these develop. We show how this can occur when a set of PFC-specific neural mechanisms interact with breadth of experience to self organize abstract rule-like PFC representations that support flexible generalization in novel tasks. The same model is shown to apply to benchmark PFC tasks (Stroop and Wisconsin card sorting), accurately simulating the behavior of neurologically intact and frontally damaged people.
NAV: A tool for producing presentation quality animations of graphical cognitive model dynamics

Computational models of cognition often exhibit complex dynamics that are difficult to discern without the use of visualization tools. Current tools often provide insight only to the modeling expert, however, and they provide limited functionality for communicating model dynamics to the nonexpert, as is needed during scientific presentations and in educational settings. We present NAV, the Node Activity Visualizer, an easy-to-use and portable software tool that interactively transforms the output of cognitive modeling simulators into presentation quality animations of model performance. nt]mis|The members of the Vanderbilt University Computational Cognitive Neuroscience Laboratory for their helpful comments.
Modular behavior control for a cognitive robot

We propose a method for a cognitive robot behavior control in which a small number of behaviors are loaded into a workspace, called working memory, where they are combined to generate actions during a task execution. We use the existing components in our cognitive robot architecture, such as the long-term memory, the short-term memory, with the addition of a working memory system and a control mechanism called the central executive agent to create a modular control system. This control method is used to drive the behaviors of our humanoid robot ISAC
A biologically inspired working memory framework for robots

The human brain includes a capacity-limited memory system devoted to the short-term retention of task-relevant information. This system is called working memory. Some computational neuroscience accounts of working memory have explained it in terms of interactions between the prefrontal cortex and the mesolimbic dopamine system. Inspired by these models, we have constructed a software toolkit for creating working memory components for robot control systems, based on the proposed mechanisms used by the brain. We report our design for this toolkit, as well as the results of a feasibility study, involving a robotic version of the delayed saccade task, and we discuss future plans to test this framework in the context of more complex tasks.
Impaired Cognitive Flexibility and Intact Cognitive Control in Autism: A Computational Cognitive Neuroscience Approach

In people with autism, the ability to enact a behavior in the presence of competing responses appears intact, while the ability to fluently adapt cognitive control in the face of changing task contingencies is impaired. In this paper, the Cross-Task Generalization model (Rougier et al., 2005), which offers a formal account of the effect of dopamine on frontal cortex function, is used to capture performance of both normally functioning individuals and people with autism on a classic test of cognitive control, the Stroop task (Stroop, 1935), and one of cognitive flexibility, the Wisconsin Card Sort Test (Berg, 1948). By weakening the effect of the dopamine’s signal on frontal cortex, the model fits quantitative and qualitative results of autistic performance on these tasks and demonstrates the potential usefulness of this approach in autism research.
Neurocomputational Mechanisms for Generalization During the Sequential Learning of Multiple Tasks

Traditional artificial neural network models of learning suffer from catastrophic interference. They are commonly trained to perform only one specific task, and when trained on a new task, they forget the original task completely. It has been shown that the foundational neurocomputational principles embodied by the Leabra cognitive modeling framework are sufficient to overcome this limitation significantly. Leabra is consistent with known biological properties of the neural networks of the brain. In particular, this framework includes both fast lateral inhibition and a local synaptic plasticity model that incorporates both correlational and error-based dynamics. It has been shown that the use of sparse internal representation significantly reduces the problem of catastrophic interference during sequential learning of multiple motor skills. We have also provided prelimilary evidence that Leabra is able to generalize the sub-sequences of motor skills, when doing so is appropriate. In this paper, we provide a detailed analysis of the extent of generalization possible with Leabra during sequential learning of multiple tasks. For comparison, we measure the generalization shown by a backpropagation of error learning algorithm based artificial neural network.
Reinforcement Learning of Dimensional Attention for Categorization

The ability to selectively focus attention on stimulus dimensions appears to play an important role in human category learning. This insight is embodied by learned dimensional attention weights in the ALCOVE model (Kruschke, 1992). The success of this psychological model suggests its use as a foundation for efforts to understand the neural basis of category learning. One obstacle to such an effort is ALCOVE’s use of the biologically implausible backpropagation of error algorithm to adapt dimensional attention weights. This obstacle may be overcome by replacing this attention mechanism with one grounded in the reinforcement learning processes of the brain’s dopamine system. In this paper, such a biologically-based mechanism for dimensional attention is proposed, and the fit of this mechanism to human performance is shown to be comparable to that of ALCOVE.
A Biologically Inspired Adaptive Working Memory for Robots

In this paper, we discuss the motivation, approach, and status of a new NSF ITR project in which an adaptive working memory is investigated for robot control and learning. There is much evidence for the existence of such a memory structure in primates. Such memory is closely tied to the learning and execution of tasks, as it contributes to decision-making capabilities by focusing on essential task information and discarding distractions. We will integrate the adaptive working memory structure into a robot to explore the issues of task learning in a physical embodiment. This leads to a complex but realistic system involving perceptual systems, actuators, reasoning, and short-term and long-term memory structures. In the paper, we discuss also planned experiments intended to evaluate the utility of the adaptive working memory.
Explaining purportedly irrational behavior by modeling skepticism in task parameters: an example examining confidence in forced-choice tasks.

Many purported demonstrations of irrational behavior rely on the assumption that participants believe key task parameters that are merely asserted by experimenters. For example, previous researchers have found that participants who first reported confidence in items presented in a yes-no format did not change confidence to the degree prescribed by the normative model when those same items were later presented in a forced-choice format. A crucial assumption, however, was that participants fully believed the assertion that the forced-choice items were mutually exclusive and exhaustive. In this article, the authors derive and test a new normative model in which it is not assumed that participants fully believe the assertion. Two visual identification experiments show that the new normative model provides a compelling account of participants' confidence reports.
A Multi-Agent Approach to Self-Reflection for Cognitive Robotics

We hypothesize that there are three key cognitive abilities a robot must have in order to effectively interact with humans. First, it must be able to reason about the status of its own internal processing. This might be called “self-awareness”. Second, the robot must be able to reason about the states of the humans with which it interacts. This might be called an “awareness of other”. The third critical cognitive ability is that which allows the robot to reason about its own abilities, cognitive processes, and knowledge. This might be called “self-reflection”. In this paper, we discuss one way to address the special challenges that self-reflection poses to multi-agent cognitive architectures.
Prefrontal cortex and dynamic categorization tasks: representational organization and neuromodulatory control.

We present a computational model of the intradimensional/ extradimensional (ID/ED) task (a variant of the Wisconsin card sorting task) that simulates the performance of intact and frontally lesioned monkeys on three different kinds of rule changes (Dias et al., 1997, ). Although Dias et al. interpret the lesion data as supporting a model in which prefrontal cortex is organized into different processing functions, our model suggests an alternative account based on representational content. A key aspect of the model is that prefrontal cortex representations are organized according to different levels of abstraction, with orbital areas encoding more specific featural information and dorsolateral areas encoding more abstract dimensional information. This representational scheme of the model is integrated with two additional key elements: (i) activation-based working memory representations controlled by a dynamic gating mechanism that simulates the hypothesized phasic actions of dopaminergic neuromodulation in prefrontal cortex, which acts to stabilize or destabilize frontal representations based on success in the task; and (ii) a weight-based associative learning system simulating posterior cortex and other subcortical areas, where the stimulus-response mappings are encoded. Frontal cortex contributes to the task via top-down activation-based biasing of task-appropriate features and dimensions in this posterior cortex system - this top-down biasing is specifically important for overcoming prepotent associations after a sorting rule reverses. The ability of the model to capture the double-dissociation observed by Dias et al. with orbital versus dorsolateral lesions supports the validity of these principles, many of which have also been useful in accounting for other frontal phenomena.
Relation between confidence in yes-no and forced-choice tasks.

Yes-no and forced-choice tasks are common in psychology, but the empirical relation between reported confidence in the 2 tasks has been unclear. The authors examined this relation with 2 experiments. The general experimental method had participants first report confidence in the truth of each of many general knowledge statements (a yes-no task) then report confidence in them again when the statements were put into pairs where it was known that one statement was true and one was false (a forced-choice task). At issue was how confidence in the statements changed between the yes-no task and the forced-choice task. Two models, including the normative one, were ruled out as descriptive models. A linear model and a multiplicative model remain viable contenders.
Relation between confidence in yes-no and forced-choice tasks.

Yes-no and forced-choice tasks are common in psychology, but the empirical relation between reported confidence in the 2 tasks has been unclear. The authors examined this relation with 2 experiments. The general experimental method had participants first report confidence in the truth of each of many general knowledge statements (a yes--no task) then report confidence in them again when the statements were put into pairs where it was known that one statement was true and one was false (a forced-choice task). At issue was how confidence in the statements changed between the yes-no task and the forced-choice task. Two models, including the normative one, were ruled out as descriptive models. A linear model and a multiplicative model remain viable contenders.
A Connectionist Model of Instructed Learning

Humans typically employ multiple learning strategies when acquiring a skill. In particular, human learners are capable of learning both from feedback on their performance at a task and also from direct verbal instruction, provided by a knowledgeable teacher. These two sources of knowledge, induction over experiences and the reception of instructions, can interact to solve complex learning problems. 
This research presents a general computational model of such instructed learning--of learning involving the integration of induction and instruction. This model is wholly connectionist in nature, being composed of a modular artificial neural network. Thus, the model extends standard connectionist learning beyond induction to incorporate explicit advice taking. A central feature of this model is the representation of explicit instructed knowledge in the dynamic activation space of a recurrent attractor network, while inductively acquired knowledge is encoded in the usual way, in the weight space of network connections. This allows for the rapid modification of behavior in response to instructions while still maintaining the process of slow learning from experience. Also, in the context of examining this model, several insights into the limitations of connectionist attractor networks arise and are investigated in detail. 
The model is validated against human experimental data involving an established interference effect in instructed category learning. When provided with an explicit rule for categorizing visual stimulus items, followed by practice at applying the rule on a particular collection of labeled examples, human learners will sometimes come to violate the given rule as a result of the practice session, even if the practice items are labeled in a manner perfectly consistent with the given rule. For some stimuli, the similarity of the item to previously viewed practice items comes to dominate over instruction following, causing a decrease in rule following accuracy. The proposed model is shown to exhibit this interference effect, and results from new instructed category learning experiments with human learners are reported. Lastly, a Bayesian analysis is conducted of this interference effect, examining the degree to which this effect may be considered a natural result of optimal learning behavior.
A Connectionist Model f Instructed Learning

The focus of this research is on how people blend knowledge gained through explicit instruction with knowledge gained through experience. The product of this work will be a cognitively plausible computational learning model which integrates instructed learning with inductive generalization from examples. The success of this model will require the attainment of both a technical and a scientific goal. The technical goal is the design of a computational mechanism in which induction and instruction are smoothly integrated. The design of such a multistrategy learner might be implemented within a symbolic rule-based framework (Huffman, Miller, & Laird 1993), within a framework strong in inductive generalization, such as connectionism (Noelle & Cottrell 1995), or within a hybrid architecture (Maclin & Shavlik 1994). This work pursues the second of these three general approaches. Following an intuition concerning the primacy of induction (which precedes linguistic rule following both phylogenetically and ontogenetically) and with an eye on future reduction to neurological explanations, the model proposed here is built within a wholly connection& framework. The scientific goal of this research is to account for certain interaction effects between instruction and induction that have been observed in humans, including:
A Co-Regularization Approach to Semi-supervised Learning with Multiple Views

The Co-Training algorithm uses unlabeled examples in multiple views to bootstrap classifiers in each view, typically in a greedy manner, and operating under assumptions of view-independence and compatibility. In this paper, we propose a Co-Regularization framework where classifiers are learnt in each view through forms of multi-view regularization. We propose algorithms within this framework that are based on optimizing measures of agreement and smoothness over labeled and unlabeled examples. These algorithms naturally extend standard regularization methods like Support Vector Machines (SVM) and Regularized Least squares (RLS) for multi-view semi-supervised learning, and inherit their benefits and applicability to high-dimensional classification problems. An empirical investigation is presented that confirms the promise of this approach.