James Henderson

research

∙ 08/28/2023

GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction

Document-level relation extraction aims to identify relationships betwee...

0 Andrei C. Coman, et al. ∙

research

∙ 05/15/2023

TESS: Text-to-Text Self-Conditioned Simplex Diffusion

Diffusion models have emerged as a powerful paradigm for generation, obt...

10 Rabeeh Karimi Mahabadi, et al. ∙

research

∙ 11/02/2022

RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question

Existing metrics for evaluating the quality of automatically generated q...

8 Alireza Mohammadshahi, et al. ∙

research

∙ 10/26/2022

Privacy Analysis of Samsung's Crowd-Sourced Bluetooth Location Tracking System

We present a detailed privacy analysis of Samsung's Offline Finding (OF)...

0 Tingfeng Yu, et al. ∙

research

∙ 10/20/2022

SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages

In recent years, multilingual machine translation models have achieved p...

23 Alireza Mohammadshahi, et al. ∙

research

∙ 07/27/2022

A Variational AutoEncoder for Transformers with Nonparametric Variational Information Bottleneck

We propose a VAE for Transformers by developing a variational informatio...

12 James Henderson, et al. ∙

research

∙ 05/22/2022

What Do Compressed Multilingual Machine Translation Models Forget?

Recently, very large pre-trained models achieve state-of-the-art results...

25 Alireza Mohammadshahi, et al. ∙

research

∙ 04/03/2022

PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models

Current methods for few-shot fine-tuning of pretrained masked language m...

0 Rabeeh Karimi Mahabadi, et al. ∙

research

∙ 03/30/2022

Graph Refinement for Coreference Resolution

The state-of-the-art models for coreference resolution are based on inde...

0 Lesly Miculicich, et al. ∙

research

∙ 03/07/2022

HyperMixer: An MLP-based Green AI Alternative to Transformers

Transformer-based architectures are the model of choice for natural lang...

0 Florian Mai, et al. ∙

research

∙ 10/13/2021

Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation

Text autoencoders are often used for unsupervised conditional text gener...

0 Florian Mai, et al. ∙

research

∙ 09/02/2021

Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning

Though language model text embeddings have revolutionized NLP research, ...

4 Christos Theodoropoulos, et al. ∙

research

∙ 07/05/2021

The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task

We describe the DCU-EPFL submission to the IWPT 2021 Shared Task on Pars...

0 James Barry, et al. ∙

research

∙ 06/10/2021

Variational Information Bottleneck for Effective Low-Resource Fine-Tuning

While large-scale pretrained language models have obtained impressive re...

0 Rabeeh Karimi Mahabadi, et al. ∙

research

∙ 06/08/2021

Compacter: Efficient Low-Rank Hypercomplex Adapter Layers

Adapting large-scale pretrained language models to downstream tasks via ...

0 Rabeeh Karimi Mahabadi, et al. ∙

research

∙ 06/08/2021

Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks

State-of-the-art parameter-efficient fine-tuning methods rely on introdu...

0 Rabeeh Karimi Mahabadi, et al. ∙

research

∙ 06/04/2021

The Closer You Look, The More You Learn: A Grey-box Approach to Protocol State Machine Learning

In this paper, we propose a new approach to infer state machine models f...

0 Chris McMahon Stone, et al. ∙

research

∙ 04/15/2021

Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling

The goal of semantic role labelling (SRL) is to recognise the predicate-...

0 Alireza Mohammadshahi, et al. ∙

research

∙ 02/01/2021

Inducing Meaningful Units from Character Sequences with Slot Attention

Characters do not convey meaning, but sequences of characters do. We pro...

0 Melika Behjati, et al. ∙

research

∙ 10/16/2020

Multi-Adversarial Learning for Cross-Lingual Word Embeddings

Generative adversarial networks (GANs) have succeeded in inducing cross-...

0 Haozhou Wang, et al. ∙

research

∙ 10/06/2020

Plug and Play Autoencoders for Conditional Text Generation

Text autoencoders are commonly used for conditional generation tasks suc...

20 Florian Mai, et al. ∙

research

∙ 05/13/2020

The Unstoppable Rise of Computational Linguistics in Deep Learning

In this paper, we trace the history of neural networks applied to natura...

0 James Henderson, et al. ∙

research

∙ 03/29/2020

Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement

We propose the Recursive Non-autoregressive Graph-to-graph Transformer a...

0 Alireza Mohammadshahi, et al. ∙

research

∙ 11/08/2019

Graph-to-Graph Transformer for Transition-based Dependency Parsing

Transition-based dependency parsing is a challenging task for conditioni...

0 Alireza Mohammadshahi, et al. ∙

research

∙ 09/13/2019

simple but effective techniques to reduce biases

There have been several studies recently showing that strong natural lan...

0 Rabeeh Karimi Mahabadi, et al. ∙

research

∙ 08/26/2019

Partially-supervised Mention Detection

Learning to detect entity mentions without using syntactic information c...

0 Lesly Miculicich, et al. ∙

research

∙ 05/29/2019

Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains

Neural language modeling (LM) has led to significant improvements in sev...

0 Navid Rekabsaz, et al. ∙

research

∙ 05/14/2019

Deep Residual Output Layers for Neural Language Generation

Many tasks, including language generation, benefit from learning the str...

0 Nikolaos Pappas, et al. ∙

research

∙ 04/20/2019

Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings

Distributed representations of words which map each word to a continuous...

0 Haozhou Wang, et al. ∙

research

∙ 10/05/2018

Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation

This paper demonstrates that word sense disambiguation (WSD) can improve...

0 Xiao Pu, et al. ∙

research

∙ 09/05/2018

Document-Level Neural Machine Translation with Hierarchical Attention Networks

Neural Machine Translation (NMT) can be improved by including document-l...

0 Lesly Miculicich, et al. ∙

research

∙ 08/31/2018

Beyond Weight Tying: Learning Joint Input-Output Embeddings for Neural Machine Translation

Tying the weights of the target word embeddings with the target word cla...

0 Nikolaos Pappas, et al. ∙

research

∙ 06/16/2018

Joint Input-Label Embedding for Neural Text Classification

Neural text classification methods typically treat output classes as cat...

0 Nikolaos Pappas, et al. ∙

research

∙ 10/06/2017

Learning Word Embeddings for Hyponymy with Entailment-Based Distributional Semantics

Lexical entailment, such as hyponymy, is a fundamental issue in the sema...

0 James Henderson, et al. ∙

research

∙ 09/30/2017

Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction

Vector-space models, from word embeddings to neural network parsers, hav...

0 Diana Nicoleta Popa, et al. ∙

research

∙ 07/13/2016

A Vector Space for Distributional Semantics for Entailment

Distributional semantics creates vector-space representations that captu...

0 James Henderson, et al. ∙

research

∙ 03/04/2016

A Bayesian Model of Multilingual Unsupervised Semantic Role Induction

We propose a Bayesian model of unsupervised semantic role induction in m...

0 Nikhil Garg, et al. ∙

research

∙ 04/16/2013

Efficient Computation of Mean Truncated Hitting Times on Very Large Graphs

Previous work has shown the effectiveness of random walk hitting times a...

0 Joel Lang, et al. ∙

James Henderson

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