Results for "Mirella Lapata"

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Neural Semantic Role Labeling with Dependency Path EmbeddingsMay 24 2016Jul 18 2016This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques. Our approach is motivated by the observation that complex syntactic structures and related phenomena, such as nested subordinations and ... More
Weakly-supervised Neural Semantic Parsing with a Generative RankerAug 23 2018Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent. The task is challenging due to the large search space and spuriousness of logical forms. In this paper we introduce a neural parser-ranker ... More
Hierarchical Transformers for Multi-Document SummarizationMay 30 2019In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. We represent cross-document relationships ... More
Neural Summarization by Extracting Sentences and WordsMar 23 2016Jul 01 2016Traditional approaches to extractive summarization rely heavily on human-engineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a general framework for single-document ... More
Sentence Centrality Revisited for Unsupervised SummarizationJun 08 2019Single document summarization has enjoyed renewed interests in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. In this paper we develop an unsupervised approach arguing that it is unrealistic ... More
Language to Logical Form with Neural AttentionJan 06 2016Jun 06 2016Semantic parsing aims at mapping natural language to machine interpretable meaning representations. Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domain- or representation-specific. ... More
Categorization in the Wild: Generalizing Cognitive Models to Naturalistic Data across LanguagesFeb 23 2019Categories such as animal or furniture are acquired at an early age and play an important role in processing, organizing, and communicating world knowledge. Categories exist across cultures: they allow to efficiently represent the complexity of the world, ... More
Bootstrapping Generators from Noisy DataApr 17 2018Apr 18 2018A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where ... More
Whodunnit? Crime Drama as a Case for Natural Language UnderstandingOct 31 2017In this paper we argue that crime drama exemplified in television programs such as CSI:Crime Scene Investigation is an ideal testbed for approximating real-world natural language understanding and the complex inferences associated with it. We propose ... More
Ranking Sentences for Extractive Summarization with Reinforcement LearningFeb 23 2018Apr 16 2018Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training ... More
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme SummarizationAug 27 2018We introduce extreme summarization, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question "What ... More
Dependency Parsing as Head SelectionJun 03 2016Dec 22 2016Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call ... More
Generating Summaries with Topic Templates and Structured Convolutional DecodersJun 11 2019Existing neural generation approaches create multi-sentence text as a single sequence. In this paper we propose a structured convolutional decoder that is guided by the content structure of target summaries. We compare our model with existing sequential ... More
Dependency Parsing as Head SelectionJun 03 2016Jun 20 2016Conventional dependency parsers rely on a statistical model and a transition system or graph algorithm to enforce tree-structured outputs during training and inference. In this work we formalize dependency parsing as the problem of selecting the head ... More
Building a Neural Semantic Parser from a Domain OntologyDec 25 2018Semantic parsing is the task of converting natural language utterances into machine interpretable meaning representations which can be executed against a real-world environment such as a database. Scaling semantic parsing to arbitrary domains faces two ... More
Long Short-Term Memory-Networks for Machine ReadingJan 25 2016Sep 20 2016In this paper we address the question of how to render sequence-level networks better at handling structured input. We propose a machine reading simulator which processes text incrementally from left to right and performs shallow reasoning with memory ... More
Dependency Parsing as Head SelectionJun 03 2016Dec 02 2016Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call ... More
A Generative Parser with a Discriminative Recognition AlgorithmAug 01 2017Aug 17 2017Generative models defining joint distributions over parse trees and sentences are useful for parsing and language modeling, but impose restrictions on the scope of features and are often outperformed by discriminative models. We propose a framework for ... More
Confidence Modeling for Neural Semantic ParsingMay 11 2018In this work we focus on confidence modeling for neural semantic parsers which are built upon sequence-to-sequence models. We outline three major causes of uncertainty, and design various metrics to quantify these factors. These metrics are then used ... More
Autofolding for Source Code SummarizationMar 18 2014Feb 06 2016Developers spend much of their time reading and browsing source code, raising new opportunities for summarization methods. Indeed, modern code editors provide code folding, which allows one to selectively hide blocks of code. However this is impractical ... More
Learning an Executable Neural Semantic ParserNov 14 2017Aug 12 2018This paper describes a neural semantic parser that maps natural language utterances onto logical forms which can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured ... More
Neural Extractive Summarization with Side InformationApr 14 2017Sep 10 2017Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted. However, the gist of the document may lie in side information, such as the title and image captions which are often available for newswire ... More
Learning Structured Natural Language Representations for Semantic ParsingApr 27 2017Jun 14 2017We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic ... More
Neural Latent Extractive Document SummarizationAug 22 2018Aug 28 2018Extractive summarization models require sentence-level labels, which are usually created heuristically (e.g., with rule-based methods) given that most summarization datasets only have document-summary pairs. Since these labels might be suboptimal, we ... More
Universal Semantic ParsingFeb 10 2017Aug 28 2017Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. ... More
Autofolding for Source Code SummarizationMar 18 2014Mar 06 2017Developers spend much of their time reading and browsing source code, raising new opportunities for summarization methods. Indeed, modern code editors provide code folding, which allows one to selectively hide blocks of code. However this is impractical ... More
Bootstrapping Generators from Noisy DataApr 17 2018Mar 18 2019A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where ... More
Sentence Compression as Tree TransductionJan 15 2014This paper presents a tree-to-tree transduction method for sentence compression. Our model is based on synchronous tree substitution grammar, a formalism that allows local distortion of the tree topology and can thus naturally capture structural mismatches. ... More
Unsupervised Visual Sense Disambiguation for Verbs using Multimodal EmbeddingsMar 30 2016We introduce a new task, visual sense disambiguation for verbs: given an image and a verb, assign the correct sense of the verb, i.e., the one that describes the action depicted in the image. Just as textual word sense disambiguation is useful for a wide ... More
Top-down Tree Long Short-Term Memory NetworksOct 31 2015Apr 03 2016Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have been successfully applied to a variety of sequence modeling tasks. In this paper we develop Tree Long Short-Term Memory (TreeLSTM), ... More
Learning to Paraphrase for Question AnsweringAug 20 2017Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous ... More
Text Generation from Knowledge Graphs with Graph TransformersApr 04 2019Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce. In this work, we address the problem ... More
A Note on Generic ProjectionsOct 10 2002Let $X \subseteq {\bf P}^N ={\bf P}^{2n}_K$ be a subvariety of dimension $n$ and $P \in {\bf P}^N$ a generic point. If the tangent variety Tan$ X$ is equal to ${\bf P}^N$ then for generic points $x$, $y$ of $X$ the projective tangent spaces $t_xX$ and ... More
The speed of Arnold diffusionFeb 06 2013A detailed numerical study is presented of the slow diffusion (Arnold diffusion) taking place around resonance crossings in nearly integrable Hamiltonian systems of three degrees of freedom in the so-called `Nekhoroshev regime'. The aim is to construct ... More
Chaotic Spiral GalaxiesAug 30 2011We study the role of asymptotic curves in supporting the spiral structure of a N-body model simulating a barred spiral galaxy. Chaotic orbits with initial conditions on the unstable asymptotic curves of the main unstable periodic orbits follow the shape ... More
Analytical forms of chaotic spiral armsMar 30 2016We develop an analytical theory of chaotic spiral arms in galaxies. This is based on the Moser theory of invariant manifolds around unstable periodic orbits. We apply this theory to the chaotic spiral arms, that start from the neighborhood of the Lagrangian ... More
Periodic Orbits and Escapes in Dynamical SystemsMar 05 2012We study the periodic orbits and the escapes in two different dynamical systems, namely (1) a classical system of two coupled oscillators, and (2) the Manko-Novikov metric (1992) which is a perturbation of the Kerr metric (a general relativistic system). ... More
A randomized most powerful test to detect a cheater's action. Applicaton to identification of listeriosis in LombardyApr 01 2014Nov 08 2014This article presents a new randomized non-parametric test based on a sample of independent but not identically distributed variables; this test detects if a cheater replaces one of the distributions of the sample with a convex-dominating one. The presented ... More
A generalization of Kantorovich operators for convex compact subsetsMay 22 2016In this paper we introduce and study a new sequence of positive linear operators acting on function spaces defined on a convex compact subset. Their construction depends on a given Markov operator, a positive real number and a sequence of probability ... More
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A ReviewDec 20 2018The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation ... More
A quick guide for student-driven community genome annotationMay 09 2018Oct 16 2018High quality gene models are necessary to expand the molecular and genetic tools available for a target organism, but these are available for only a handful of model organisms that have undergone extensive curation and experimental validation over the ... More