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Found 58 papers, 22 papers with code
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-weight retriever that selects a set of candidate passages, and a computationally expensive reader that examines the passages to identify the correct answer.
Non-parametric, Nearest-neighbor-assisted Fine-tuning for Neural Machine Translation
We explore whether such non-parametric models can improve machine translation models at the fine-tuning stage by incorporating statistics from the kNN predictions to inform the gradient updates for a baseline translation model.
G3Detector: General GPT-Generated Text Detector
The burgeoning progress in the field of Large Language Models (LLMs) heralds significant benefits due to their unparalleled capacities.
MasakhaNEWS: News Topic Classification for African languages
Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API).
Graph Attention with Hierarchies for Multi-hop Question Answering
Multi-hop QA (Question Answering) is the task of finding the answer to a question across multiple documents.
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks
Experiments on various knowledge-intensive tasks such as question answering and dialogue datasets show that, simply augmenting parametric models (T5-base) using our method produces more accurate results (e. g., 25. 8 -> 44. 3 EM on NQ) while retaining a high throughput (e. g., 1000 queries/s on NQ).
Ranked #4 on
Question Answering
on KILT: ELI5
Query Expansion Using Contextual Clue Sampling with Language Models
To balance these two considerations, we propose a combination of an effective filtering strategy and fusion of the retrieved documents based on the generation probability of each context.
What the DAAM: Interpreting Stable Diffusion Using Cross Attention
Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses.
ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective
Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural Networks (GNNs).
MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction
Relation extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs, needing domain experts.
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
We propose to tackle this problem by generating a debiased version of a dataset, which can then be used to train a debiased, off-the-shelf model, by simply replacing its training data.
Ranked #1 on
Natural Language Inference
on HANS
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants
We collect training datasets in twenty experimental settings and perform a detailed analysis of this approach for the task of extractive question answering (QA) for both standard and adversarial data collection.
Spike-inspired Rank Coding for Fast and Accurate Recurrent Neural Networks
We demonstrate these in two toy problems of sequence classification, and in a temporally-encoded MNIST dataset where our RC model achieves 99. 19% accuracy after the first input time-step, outperforming the state of the art in temporal coding with SNNs, as well as in spoken-word classification of Google Speech Commands, outperforming non-RC-trained early inference with LSTMs.
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC).
Ranked #1 on
Link Prediction
on CoDEx Small
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification
Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e. g., the preservation of semantics and grammaticality).
Challenges in Generalization in Open Domain Question Answering
Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions.
Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints
Adaptive Computation (AC) has been shown to be effective in improving the efficiency of Open-Domain Question Answering (ODQA) systems.
Controllable Abstractive Dialogue Summarization with Sketch Supervision
In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control.
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation
We further conduct a novel human-in-the-loop evaluation to show that our models are considerably more robust to new human-written adversarial examples: crowdworkers can fool our model only 8. 8% of the time on average, compared to 17. 6% for a model trained without synthetic data.
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, pretrained language models such as GPT-3 have shown competitive results when compared to fully-supervised, fine-tuned, large, pretrained language models.
Dynabench: Rethinking Benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking.
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
We introduce a new QA-pair retriever, RePAQ, to complement PAQ.
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020.
CorDial: Coarse-to-fine Abstractive Dialogue Summarization with Controllable Granularity
2) A simple strategy to control the granularity of the final summary.
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-weight retriever that selects a set of candidate passages, and a computationally expensive reader that examines the passages to identify the correct answer.
Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets
We also find that 30% of test-set questions have a near-duplicate paraphrase in their corresponding training sets.
Learning Reasoning Strategies in End-to-End Differentiable Proving
Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theorem Provers (NTPs).
Ranked #1 on
Relational Reasoning
on CLUTRR (k=3)
AxCell: Automatic Extraction of Results from Machine Learning Papers
Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers.
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples
Recent efforts have shown that neural text processing models are vulnerable to adversarial examples, but the nature of these examples is poorly understood.
Benchmarking Machine Reading Comprehension: A Psychological Perspective
Machine reading comprehension (MRC) has received considerable attention as a benchmark for natural language understanding.
Undersensitivity in Neural Reading Comprehension
Current reading comprehension models generalise well to in-distribution test sets, yet perform poorly on adversarially selected inputs.
Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
We find that training on adversarially collected samples leads to strong generalisation to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop.
Ranked #1 on
Reading Comprehension
on AdversarialQA
(using extra training data)
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets
Existing analysis work in machine reading comprehension (MRC) is largely concerned with evaluating the capabilities of systems.
R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason
Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets.
Towards Machine-assisted Meta-Studies: The Hubble Constant
We present an approach for automatic extraction of measured values from the astrophysical literature, using the Hubble constant for our pilot study.
On the Importance of Strong Baselines in Bayesian Deep Learning
Like all sub-fields of machine learning Bayesian Deep Learning is driven by empirical validation of its theoretical proposals.
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data
Electronic health records (EHR) are increasingly being used for constructing disease risk prediction models.
UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)
In this paper we describe our 2nd place FEVER shared-task system that achieved a FEVER score of 62. 52{\%} on the provisional test set (without additional human evaluation), and 65. 41{\%} on the development set.
Jack the Reader -- A Machine Reading Framework
For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions.
Jack the Reader - A Machine Reading Framework
For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions.
Extrapolation in NLP
We argue that extrapolation to examples outside the training space will often be easier for models that capture global structures, rather than just maximise their local fit to the training data.
Constructing Datasets for Multi-hop Reading Comprehension Across Documents
We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods.
Ranked #8 on
Question Answering
on WikiHop
Convolutional 2D Knowledge Graph Embeddings
In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets.
Ranked #1 on
Link Prediction
on WN18
Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies
In this paper we present a novel Neural Network algorithm for conducting semi-supervised learning for sequence labeling tasks arranged in a linguistically motivated hierarchy.
Learning to Reason With Adaptive Computation
Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading.
Neural Architectures for Fine-grained Entity Type Classification
In this work, we investigate several neural network architectures for fine-grained entity type classification.
An Attentive Neural Architecture for Fine-grained Entity Type Classification
In this work we propose a novel attention-based neural network model for the task of fine-grained entity type classification that unlike previously proposed models recursively composes representations of entity mention contexts.
Task-Oriented Learning of Word Embeddings for Semantic Relation Classification
We present a novel learning method for word embeddings designed for relation classification.
Leveraging Monolingual Data for Crosslingual Compositional Word Representations
In this work, we present a novel neural network based architecture for inducing compositional crosslingual word representations.
