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09/06/2023
Gender-specific Machine Translation with Large Language Models
Decoder-only Large Language Models (LLMs) have demonstrated potential in...
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07/03/2023
Improving Language Plasticity via Pretraining with Active Forgetting
Pretrained language models (PLMs) are today the primary model for natura...
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05/23/2023
Non-parametric, Nearest-neighbor-assisted Fine-tuning for Neural Machine Translation
Non-parametric, k-nearest-neighbor algorithms have recently made inroads...
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05/22/2023
G3Detector: General GPT-Generated Text Detector
The burgeoning progress in the field of Large Language Models (LLMs) her...
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04/19/2023
MasakhaNEWS: News Topic Classification for African languages
African languages are severely under-represented in NLP research due to ...
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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...
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10/30/2022
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks
Access to external knowledge is essential for many natural language proc...
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10/13/2022
Query Expansion Using Contextual Clue Sampling with Language Models
Query expansion is an effective approach for mitigating vocabulary misma...
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07/20/2022
ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective
Factorisation-based Models (FMs), such as DistMult, have enjoyed endurin...
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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...
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03/24/2022
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
Natural language processing models often exploit spurious correlations b...
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12/16/2021
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants
In Dynamic Adversarial Data Collection (DADC), human annotators are task...
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10/06/2021
Spike-inspired Rank Coding for Fast and Accurate Recurrent Neural Networks
Biological spiking neural networks (SNNs) can temporally encode informat...
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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...
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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...
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09/02/2021
Challenges in Generalization in Open Domain Question Answering
Recent work on Open Domain Question Answering has shown that there is a ...
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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...
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05/28/2021
Controllable Abstractive Dialogue Summarization with Sketch Supervision
In this paper, we aim to improve abstractive dialogue summarization qual...
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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...
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04/18/2021
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation
Despite the availability of very large datasets and pretrained models, s...
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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...
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01/01/2021
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020. The competition...
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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...
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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...
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07/13/2020
Learning Reasoning Strategies in End-to-End Differentiable Proving
Attempts to render deep learning models interpretable, data-efficient, a...
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04/29/2020
AxCell: Automatic Extraction of Results from Machine Learning Papers
Tracking progress in machine learning has become increasingly difficult ...
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04/13/2020
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples
While recent efforts have shown that neural text processing models are v...
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04/04/2020
Prerequisites for Explainable Machine Reading Comprehension: A Position Paper
Machine reading comprehension (MRC) has received considerable attention ...
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02/15/2020
Undersensitivity in Neural Reading Comprehension
Current reading comprehension models generalise well to in-distribution ...
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02/02/2020
Beat the AI: Investigating Adversarial Human Annotations for Reading Comprehension
Innovations in annotation methodology have been a propellant for Reading...
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11/21/2019
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets
Existing analysis work in machine reading comprehension (MRC) is largely...
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10/10/2019
RC-QED: Evaluating Natural Language Derivations in Multi-Hop Reading Comprehension
Recent studies revealed that reading comprehension (RC) systems learn to...
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01/31/2019
Towards Machine-assisted Meta-Studies: The Hubble Constant
We present an approach for automatic extraction of measured values from ...
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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...
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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...
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09/26/2018
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
Grammatical error correction, like other machine learning tasks, greatly...
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06/20/2018
Jack the Reader - A Machine Reading Framework
Many Machine Reading and Natural Language Understanding tasks require re...
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05/17/2018
Extrapolation in NLP
We argue that extrapolation to examples outside the training space will ...
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10/17/2017
Constructing Datasets for Multi-hop Reading Comprehension Across Documents
Most Reading Comprehension methods limit themselves to queries which can...
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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...
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10/24/2016
Learning to Reason With Adaptive Computation
Multi-hop inference is necessary for machine learning systems to success...
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06/04/2016
Neural Architectures for Fine-grained Entity Type Classification
In this work, we investigate several neural network architectures for fi...
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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...
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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...
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12/19/2014