Pontus Stenetorp

research

∙ 09/06/2023

Gender-specific Machine Translation with Large Language Models

Decoder-only Large Language Models (LLMs) have demonstrated potential in...

0 Eduardo Sanchez, et al. ∙

research

∙ 07/03/2023

Improving Language Plasticity via Pretraining with Active Forgetting

Pretrained language models (PLMs) are today the primary model for natura...

0 Yihong Chen, et al. ∙

research

∙ 05/23/2023

Non-parametric, Nearest-neighbor-assisted Fine-tuning for Neural Machine Translation

Non-parametric, k-nearest-neighbor algorithms have recently made inroads...

0 Jiayi Wang, et al. ∙

research

∙ 05/22/2023

G3Detector: General GPT-Generated Text Detector

The burgeoning progress in the field of Large Language Models (LLMs) her...

0 Haolan Zhan, et al. ∙

research

∙ 04/19/2023

MasakhaNEWS: News Topic Classification for African languages

African languages are severely under-represented in NLP research due to ...

6 David Ifeoluwa Adelani, et al. ∙

research

∙ 01/27/2023

Graph Attention with Hierarchies for Multi-hop Question Answering

Multi-hop QA (Question Answering) is the task of finding the answer to a...

0 Yunjie He, et al. ∙

research

∙ 10/30/2022

An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks

Access to external knowledge is essential for many natural language proc...

0 Yuxiang WU, et al. ∙

research

∙ 10/13/2022

Query Expansion Using Contextual Clue Sampling with Language Models

Query expansion is an effective approach for mitigating vocabulary misma...

0 Linqing Liu, et al. ∙

research

∙ 07/20/2022

ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective

Factorisation-based Models (FMs), such as DistMult, have enjoyed endurin...

4 Yihong Chen, et al. ∙

research

∙ 04/10/2022

MedDistant19: A Challenging Benchmark for Distantly Supervised Biomedical Relation Extraction

Relation Extraction in the biomedical domain is challenging due to the l...

10 Saadullah Amin, et al. ∙

research

∙ 03/24/2022

Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets

Natural language processing models often exploit spurious correlations b...

0 Yuxiang WU, et al. ∙

research

∙ 12/16/2021

Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants

In Dynamic Adversarial Data Collection (DADC), human annotators are task...

0 Max Bartolo, et al. ∙

research

∙ 10/06/2021

Spike-inspired Rank Coding for Fast and Accurate Recurrent Neural Networks

Biological spiking neural networks (SNNs) can temporally encode informat...

0 Alan Jeffares, et al. ∙

research

∙ 10/06/2021

Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

Learning good representations on multi-relational graphs is essential to...

6 Yihong Chen, et al. ∙

research

∙ 09/09/2021

Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification

Research shows that natural language processing models are generally con...

0 Maximilian Mozes, et al. ∙

research

∙ 09/02/2021

Challenges in Generalization in Open Domain Question Answering

Recent work on Open Domain Question Answering has shown that there is a ...

0 Linqing Liu, et al. ∙

research

∙ 07/05/2021

Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints

Adaptive Computation (AC) has been shown to be effective in improving th...

0 Yuxiang WU, et al. ∙

research

∙ 05/28/2021

Controllable Abstractive Dialogue Summarization with Sketch Supervision

In this paper, we aim to improve abstractive dialogue summarization qual...

0 Chien-Sheng Wu, et al. ∙

research

∙ 04/18/2021

Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

When primed with only a handful of training samples, very large pretrain...

8 Yao Lu, et al. ∙

research

∙ 04/18/2021

Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

Despite the availability of very large datasets and pretrained models, s...

0 Max Bartolo, et al. ∙

research

∙ 02/13/2021

PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them

Open-domain Question Answering models which directly leverage question-a...

0 Patrick Lewis, et al. ∙

research

∙ 01/01/2021

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

We review the EfficientQA competition from NeurIPS 2020. The competition...

15 Sewon Min, et al. ∙

research

∙ 11/10/2020

Don't Read Too Much into It: Adaptive Computation for Open-Domain Question Answering

Most approaches to Open-Domain Question Answering consist of a light-wei...

1 Yuxiang WU, et al. ∙

research

∙ 08/06/2020

Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets

Ideally Open-Domain Question Answering models should exhibit a number of...

14 Patrick Lewis, et al. ∙

research

∙ 07/13/2020

Learning Reasoning Strategies in End-to-End Differentiable Proving

Attempts to render deep learning models interpretable, data-efficient, a...

42 Pasquale Minervini, et al. ∙

research

∙ 04/29/2020

AxCell: Automatic Extraction of Results from Machine Learning Papers

Tracking progress in machine learning has become increasingly difficult ...

2 Marcin Kardas, et al. ∙

research

∙ 04/13/2020

Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples

While recent efforts have shown that neural text processing models are v...

0 Maximilian Mozes, et al. ∙

research

∙ 04/04/2020

Prerequisites for Explainable Machine Reading Comprehension: A Position Paper

Machine reading comprehension (MRC) has received considerable attention ...

0 Saku Sugawara, et al. ∙

research

∙ 02/15/2020

Undersensitivity in Neural Reading Comprehension

Current reading comprehension models generalise well to in-distribution ...

0 Johannes Welbl, et al. ∙

research

∙ 02/02/2020

Beat the AI: Investigating Adversarial Human Annotations for Reading Comprehension

Innovations in annotation methodology have been a propellant for Reading...

0 Max Bartolo, et al. ∙

research

∙ 11/21/2019

Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets

Existing analysis work in machine reading comprehension (MRC) is largely...

0 Saku Sugawara, et al. ∙

research

∙ 10/10/2019

RC-QED: Evaluating Natural Language Derivations in Multi-Hop Reading Comprehension

Recent studies revealed that reading comprehension (RC) systems learn to...

0 Naoya Inoue, et al. ∙

research

∙ 01/31/2019

Towards Machine-assisted Meta-Studies: The Hubble Constant

We present an approach for automatic extraction of measured values from ...

0 Tom Crossland, et al. ∙

research

∙ 11/23/2018

Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data

Electronic health records (EHR) are increasingly being used for construc...

0 Spiros Denaxas, et al. ∙

research

∙ 11/23/2018

On the Importance of Strong Baselines in Bayesian Deep Learning

Like all sub-fields of machine learning, Bayesian Deep Learning is drive...

10 Jishnu Mukhoti, et al. ∙

research

∙ 09/26/2018

Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection

Grammatical error correction, like other machine learning tasks, greatly...

0 Sudhanshu Kasewa, et al. ∙

research

∙ 06/20/2018

Jack the Reader - A Machine Reading Framework

Many Machine Reading and Natural Language Understanding tasks require re...

0 Dirk Weissenborn, et al. ∙

research

∙ 05/17/2018

Extrapolation in NLP

We argue that extrapolation to examples outside the training space will ...

0 Jeff Mitchell, et al. ∙

research

∙ 10/17/2017

Constructing Datasets for Multi-hop Reading Comprehension Across Documents

Most Reading Comprehension methods limit themselves to queries which can...

0 Johannes Welbl, et al. ∙

research

∙ 12/29/2016

Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies

In this paper we present a novel Neural Network algorithm for conducting...

0 Jonathan Godwin, et al. ∙

research

∙ 10/24/2016

Learning to Reason With Adaptive Computation

Multi-hop inference is necessary for machine learning systems to success...

0 Mark Neumann, et al. ∙

research

∙ 06/04/2016

Neural Architectures for Fine-grained Entity Type Classification

In this work, we investigate several neural network architectures for fi...

0 Sonse Shimaoka, et al. ∙

research

∙ 04/19/2016

An Attentive Neural Architecture for Fine-grained Entity Type Classification

In this work we propose a novel attention-based neural network model for...

0 Sonse Shimaoka, et al. ∙

research

∙ 02/28/2015

Task-Oriented Learning of Word Embeddings for Semantic Relation Classification

We present a novel learning method for word embeddings designed for rela...

0 Kazuma Hashimoto, et al. ∙

research

∙ 12/19/2014

Leveraging Monolingual Data for Crosslingual Compositional Word Representations

In this work, we present a novel neural network based architecture for i...

0 Hubert Soyer, et al. ∙

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