b'Shay B. Cohen'

research

∙ 06/17/2022

A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning

Inscrutable AI systems are difficult to trust, especially if they operat...

Balint Gyevnar, et al. ∙

research

∙ 05/25/2022

Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents by Sampling Summary Views

We argue that disentangling content selection from the budget used to co...

Marcio Fonseca, et al. ∙

research

∙ 05/20/2022

On the Trade-off between Redundancy and Local Coherence in Summarization

Extractive summarization systems are known to produce poorly coherent an...

Ronald Cardenas, et al. ∙

research

∙ 03/15/2022

Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information

We describe a simple and effective method (Spectral Attribute removaL; S...

Shun Shao, et al. ∙

research

∙ 10/05/2021

Co-training an Unsupervised Constituency Parser with Weak Supervision

We introduce a method for unsupervised parsing that relies on bootstrapp...

Nickil Maveli, et al. ∙

research

∙ 04/16/2021

Unsupervised Extractive Summarization by Human Memory Simulation

Summarization systems face the core challenge of identifying and selecti...

Ronald Cardenas, et al. ∙

research

∙ 02/03/2021

Learning to Match Mathematical Statements with Proofs

We introduce a novel task consisting in assigning a proof to a given mat...

Maximin Coavoux, et al. ∙

research

∙ 01/17/2021

Narration Generation for Cartoon Videos

Research on text generation from multimodal inputs has largely focused o...

Nikos Papasarantopoulos, et al. ∙

research

∙ 10/23/2020

A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing

Abstract Meaning Representations (AMR) are a broad-coverage semantic for...

Chunchuan Lyu, et al. ∙

research

∙ 10/09/2020

Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation

AMR-to-text generation is used to transduce Abstract Meaning Representat...

Yan Zhang, et al. ∙

research

∙ 09/28/2020

Reducing Quantity Hallucinations in Abstractive Summarization

It is well-known that abstractive summaries are subject to hallucination...

Zheng Zhao, et al. ∙

research

∙ 08/17/2020

Learning Two-Layer Residual Networks with Nonparametric Function Estimation by Convex Programming

We focus on learning a two-layer residual neural network with preactivat...

Zhunxuan Wang, et al. ∙

research

∙ 04/23/2020

Learning Dialog Policies from Weak Demonstrations

Deep reinforcement learning is a promising approach to training a dialog...

Gabriel Gordon-Hall, et al. ∙

research

∙ 09/07/2019

Semantic Role Labeling with Iterative Structure Refinement

Modern state-of-the-art Semantic Role Labeling (SRL) methods rely on exp...

Chunchuan Lyu, et al. ∙

research

∙ 07/19/2019

What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks

We introduce 'extreme summarization', a new single-document summarizatio...

Shashi Narayan, et al. ∙

research

∙ 04/21/2019

Obfuscation for Privacy-preserving Syntactic Parsing

The goal of homomorphic encryption is to encrypt data such that another ...

Zhifeng Hu, et al. ∙

research

∙ 04/03/2019

Jointly Extracting and Compressing Documents with Summary State Representations

We present a new neural model for text summarization that first extracts...

Afonso Mendes, et al. ∙

research

∙ 04/03/2019

Jointly Extracting and Compressing Documentswith Summary State Representations

We present a new neural model for text summarization that first extracts...

Afonso Mendes, et al. ∙

research

∙ 04/01/2019

Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle

We introduce a novel transition system for discontinuous constituency pa...

Maximin Coavoux, et al. ∙

research

∙ 03/27/2019

Structural Neural Encoders for AMR-to-text Generation

AMR-to-text generation is a problem recently introduced to the NLP commu...

Marco Damonte, et al. ∙

research

∙ 02/24/2019

Unlexicalized Transition-based Discontinuous Constituency Parsing

Lexicalized parsing models are based on the assumptions that (i) constit...

Maximin Coavoux, et al. ∙

research

∙ 09/03/2018

Multilingual Clustering of Streaming News

Clustering news across languages enables efficient media monitoring by a...

Sebastião Miranda, et al. ∙

research

∙ 08/28/2018

Privacy-preserving Neural Representations of Text

This article deals with adversarial attacks towards deep learning system...

Maximin Coavoux, et al. ∙

research

∙ 08/27/2018

Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

We introduce extreme summarization, a new single-document summarization ...

Shashi Narayan, et al. ∙

research

∙ 02/23/2018

Ranking Sentences for Extractive Summarization with Reinforcement Learning

Single document summarization is the task of producing a shorter version...

Shashi Narayan, et al. ∙

research

∙ 10/31/2017

Whodunnit? Crime Drama as a Case for Natural Language Understanding

In this paper we argue that crime drama exemplified in television progra...

Lea Frermann, et al. ∙

research

∙ 07/21/2017

Split and Rephrase

We propose a new sentence simplification task (Split-and-Rephrase) where...

Shashi Narayan, et al. ∙

research

∙ 04/14/2017

Cross-lingual Abstract Meaning Representation Parsing

Abstract Meaning Representation (AMR) annotation efforts have mostly foc...

Marco Damonte, et al. ∙

research

∙ 04/14/2017

Neural Extractive Summarization with Side Information

Most extractive summarization methods focus on the main body of the docu...

Shashi Narayan, et al. ∙

research

∙ 08/22/2016

An Incremental Parser for Abstract Meaning Representation

Meaning Representation (AMR) is a semantic representation for natural la...

Marco Damonte, et al. ∙

research

∙ 08/09/2016

Canonical Correlation Inference for Mapping Abstract Scenes to Text

We describe a technique for structured prediction, based on canonical co...

Nikos Papasarantopoulos, et al. ∙

research

∙ 06/07/2016

Optimizing Spectral Learning for Parsing

We describe a search algorithm for optimizing the number of latent state...

Shashi Narayan, et al. ∙

research

∙ 01/22/2016

Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing

One of the limitations of semantic parsing approaches to open-domain que...

Shashi Narayan, et al. ∙

research

∙ 09/03/2015

Encoding Prior Knowledge with Eigenword Embeddings

Canonical correlation analysis (CCA) is a method for reducing the dimens...

Dominique Osborne, et al. ∙

research

∙ 04/30/2015

Parsing Linear Context-Free Rewriting Systems with Fast Matrix Multiplication

We describe a matrix multiplication recognition algorithm for a subset o...

Shay B. Cohen, et al. ∙

research

∙ 06/28/2012

Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing

We present a novel technique to remove spurious ambiguity from transitio...

Shay B. Cohen, et al. ∙

Shay B. Cohen

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