scholarly journals ¬Learning Island-insensitivity from the input: A corpus analysis of child- and youth-directed text in Norwegian

2021 ◽  
Author(s):  
Dave Kush ◽  
Charlotte Sant ◽  
Sunniva Briså Strætkvern
Keyword(s):  
Relative Clauses ◽  
Direct Evidence ◽  
Corpus Analysis ◽  
Target Language ◽  
Learning Models ◽  
Long Distance ◽  
Corpus Study ◽  
Fine Grained ◽  
Embedded Questions ◽  
Reading Material

Norwegian allows filler-gap dependencies into relative clauses (RCs) and embedded questions (EQs) – domains that are usually considered islands. We conducted a corpus study on youth-directed reading material to assess what direct evidence Norwegian children receive for filler-gap dependencies into islands. Results suggest that the input contains examples of Filler-gap dependencies into both RCs and EQs, but such examples are significantly less frequent than long-distance filler-gap dependencies into non-island clauses. Moreover, evidence for island violations is characterized by the absence of forms that are, in principle, acceptable in the target grammar. Thus, although they encounter dependencies into islands, children must generalize beyond the fine-grained distributional characteristics of the input to acquire the full pattern of island-insensitivity in their target language. We conclude by considering how different learning models would fare on acquiring the target generalizations.

Experimental Evidence on Island Effects in Spanish Relative Clauses

Probus ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Laura Stigliano ◽  
Ming Xiang
Keyword(s):  
Experimental Evidence ◽  
Relative Clauses ◽  
Linguistic Theory ◽  
Experimental Paradigm ◽  
Long Distance ◽  
Fine Grained ◽  
Wh Questions ◽  
Judgment Study ◽  
Definition Of ◽  
Acceptability Judgment

Abstract Research on islands has been central to linguistic theory for more than 50 years. Its importance relies on the theoretical consequences islands posit for movement and long distance dependencies. In this paper we aim to explore the contrast between a variety of islands in Spanish relative clauses to reveal whether there is any gradience in the strength of the island effects. In order to tease apart fine-grained contrasts we run an acceptability judgment study based on the factorial definition of island, an experimental paradigm that aims to isolate the various factors that can affect the acceptability of a sentence involving island violations. Overall, we found that the five constructions tested (embedded wh-questions, whether-clauses, adjuncts, complex NPs and relative clauses) show island effects in Spanish and that there are limited differences in the size of these effects, which points to a more categorical view of islands.

Experimental Evidence on Island Effects in Spanish Relative Clauses

Probus ◽  
2021 ◽  
Vol 33 (2) ◽  
pp. 271-296
Author(s):  
Laura Stigliano ◽  
Ming Xiang
Keyword(s):  
Experimental Evidence ◽  
Relative Clauses ◽  
Linguistic Theory ◽  
Experimental Paradigm ◽  
Long Distance ◽  
Fine Grained ◽  
Wh Questions ◽  
Judgment Study ◽  
Definition Of ◽  
Acceptability Judgment

Abstract Research on islands has been central to linguistic theory for more than 50 years. Its importance relies on the theoretical consequences islands posit for movement and long distance dependencies. In this paper we aim to explore the contrast between a variety of islands in Spanish relative clauses to reveal whether there is any gradience in the strength of the island effects. In order to tease apart fine-grained contrasts we run an acceptability judgment study based on the factorial definition of island, an experimental paradigm that aims to isolate the various factors that can affect the acceptability of a sentence involving island violations. Overall, we found that the five constructions tested (embedded wh-questions, whether-clauses, adjuncts, complex NPs and relative clauses) show island effects in Spanish and that there are limited differences in the size of these effects, which points to a more categorical view of islands.

Rethinking child difficulty: The effect of NP type on children's processing of relative clauses in Hebrew

2009 ◽  
Vol 37 (1) ◽  
pp. 27-57 ◽  
Author(s):  
INBAL ARNON
Keyword(s):  
Relative Clause ◽  
Relative Clauses ◽  
Corpus Study ◽  
Fine Grained ◽  
Distributional Information ◽  
Poor Comprehension ◽  
Object Relative

ABSTRACTChildren find object relative clauses difficult. They show poor comprehension that lags behind production into their fifth year. This finding has shaped models of relative clause acquisition, with appeals to processing heuristics or syntactic preferences to explain why object relatives are more difficult than subject relatives. Two studies here suggest that children (age 4 ; 6) do not find all object relatives difficult: a corpus study shows that children most often hear and produce object relatives with pronominal subjects. But they are most often tested on ones with lexical-NP subjects (e.g. The nurse thatthe girlis drawing). When tested on object relatives with pronominal subjects (e.g. The nurse thatIam drawing), similar to those they actually hear and produce, Hebrew speakers aged 4 ; 6 show good comprehension (85% accuracy) that matches their production ability. This suggests a different path of relative clause acquisition, one that is sensitive to fine-grained distributional information.

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

scholarly journals Linear capabilities for fully abstract compilation of separation-logic-verified code

2021 ◽  
Vol 31 ◽  
Author(s):  
THOMAS VAN STRYDONCK ◽  
FRANK PIESSENS ◽  
DOMINIQUE DEVRIESE
Keyword(s):  
Spatial Separation ◽  
Separation Logic ◽  
Target Language ◽  
Fine Grained ◽  
Dynamic Contract ◽  
Modular Verification ◽  
Source Program ◽  
Memory Accesses ◽  
Memory Resources ◽  
Efficient Memory

Abstract Separation logic is a powerful program logic for the static modular verification of imperative programs. However, dynamic checking of separation logic contracts on the boundaries between verified and untrusted modules is hard because it requires one to enforce (among other things) that outcalls from a verified to an untrusted module do not access memory resources currently owned by the verified module. This paper proposes an approach to dynamic contract checking by relying on support for capabilities, a well-studied form of unforgeable memory pointers that enables fine-grained, efficient memory access control. More specifically, we rely on a form of capabilities called linear capabilities for which the hardware enforces that they cannot be copied. We formalize our approach as a fully abstract compiler from a statically verified source language to an unverified target language with support for linear capabilities. The key insight behind our compiler is that memory resources described by spatial separation logic predicates can be represented at run time by linear capabilities. The compiler is separation-logic-proof-directed: it uses the separation logic proof of the source program to determine how memory accesses in the source program should be compiled to linear capability accesses in the target program. The full abstraction property of the compiler essentially guarantees that compiled verified modules can interact with untrusted target language modules as if they were compiled from verified code as well. This article is an extended version of one that was presented at ICFP 2019 (Van Strydonck et al., 2019).

scholarly journals Non-Local and Multi-Scale Mechanisms for Image Inpainting

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3281
Author(s):  
Xu He ◽  
Yong Yin
Keyword(s):  
Markov Random Fields ◽  
Receptive Fields ◽  
Image Inpainting ◽  
Long Distance ◽  
Visual Appearance ◽  
Fine Grained ◽  
Multi Scale ◽  
Non Local ◽  
Relationship Of

Recently, deep learning-based techniques have shown great power in image inpainting especially dealing with squared holes. However, they fail to generate plausible results inside the missing regions for irregular and large holes as there is a lack of understanding between missing regions and existing counterparts. To overcome this limitation, we combine two non-local mechanisms including a contextual attention module (CAM) and an implicit diversified Markov random fields (ID-MRF) loss with a multi-scale architecture which uses several dense fusion blocks (DFB) based on the dense combination of dilated convolution to guide the generative network to restore discontinuous and continuous large masked areas. To prevent color discrepancies and grid-like artifacts, we apply the ID-MRF loss to improve the visual appearance by comparing similarities of long-distance feature patches. To further capture the long-term relationship of different regions in large missing regions, we introduce the CAM. Although CAM has the ability to create plausible results via reconstructing refined features, it depends on initial predicted results. Hence, we employ the DFB to obtain larger and more effective receptive fields, which benefits to predict more precise and fine-grained information for CAM. Extensive experiments on two widely-used datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art approaches both in quantity and quality.

scholarly journals Many Languages, One Parser

2016 ◽  
Vol 4 ◽  
pp. 431-444 ◽  
Author(s):  
Waleed Ammar ◽  
George Mulcaire ◽  
Miguel Ballesteros ◽  
Chris Dyer ◽  
Noah A. Smith
Keyword(s):  
Target Language ◽  
Dependency Parsing ◽  
Fine Grained ◽  
Multiple Languages

We train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language information; and (iii) language-specific features (fine-grained POS tags). This input representation enables the parser not only to parse effectively in multiple languages, but also to generalize across languages based on linguistic universals and typological similarities, making it more effective to learn from limited annotations. Our parser’s performance compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small treebank, or no treebank for training.

scholarly journals Generating Natural Counterfactual Visual Explanations

2020 ◽  
Author(s):  
Wenqi Zhao ◽  
Satoshi Oyama ◽  
Masahito Kurihara
Keyword(s):  
Image Classification ◽  
Classification Accuracy ◽  
Classification Task ◽  
Learning Models ◽  
Generative Adversarial Network ◽  
Fine Grained ◽  
Class A ◽  
Adversarial Network ◽  
Class B ◽  
Machine Learning Models

Counterfactual explanations help users to understand the behaviors of machine learning models by changing the inputs for the existing outputs. For an image classification task, an example counterfactual visual explanation explains: "for an example that belongs to class A, what changes do we need to make to the input so that the output is more inclined to class B." Our research considers changing the attribute description text of class A on the basis of the attributes of class B and generating counterfactual images on the basis of the modified text. We can use the prediction results of the model on counterfactual images to find the attributes that have the greatest effect when the model is predicting classes A and B. We applied our method to a fine-grained image classification dataset and used the generative adversarial network to generate natural counterfactual visual explanations. To evaluate these explanations, we used them to assist crowdsourcing workers in an image classification task. We found that, within a specific range, they improved classification accuracy.

scholarly journals From Natural Language to RDF Graphs with Pregroups

2017 ◽  
Author(s):  
Antonin Delpeuch ◽  
Anne Preller
Keyword(s):  
Side Effects ◽  
Semantic Web ◽  
Natural Language ◽  
Relative Clauses ◽  
Semantic Category ◽  
Target Language ◽  
Pregroup Grammars ◽  
Unbounded Dependencies ◽  
Efficient Type ◽  
Rdf Graphs

We define an algorithm translating natural language sentences to the formal syntax of RDF, an existential conjunctive logic widely used on the Semantic Web. Our translationis based on pregroup grammars, an efficient type-logical grammatical framework with atransparent syntax-semantics interface. We introduce a restricted notion of side effects inthe semantic category of finitely generated free semimodules over {0,1} to that end.The translation gives an intensional counterpart to previous extensional models.We establish a one-to-one correspondence between extensional models and RDF models such that satisfaction is preserved. Our translation encompasses the expressivity of the target language and supports complex linguistic constructions like relative clauses and unbounded dependencies.

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