Hinrich Schütze

research

∙ 11/26/2021

True Few-Shot Learning with Prompts – A Real-World Perspective

Prompt-based approaches are strong at few-shot learning. However, Perez ...

Timo Schick, et al. ∙

research

∙ 09/29/2021

BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief

Although pretrained language models (PTLMs) contain significant amounts ...

Nora Kassner, et al. ∙

research

∙ 09/28/2021

Active Learning for Argument Mining: A Practical Approach

Despite considerable recent progress, the creation of well-balanced and ...

Nikolai Solmsdorf, et al. ∙

research

∙ 09/23/2021

Scene Graph Generation for Better Image Captioning?

We investigate the incorporation of visual relationships into the task o...

Maximilian Mozes, et al. ∙

research

∙ 09/20/2021

BERT Cannot Align Characters

In previous work, it has been shown that BERT can adequately align cross...

Antonis Maronikolakis, et al. ∙

research

∙ 09/16/2021

Locating Language-Specific Information in Contextualized Embeddings

Multilingual pretrained language models (MPLMs) exhibit multilinguality ...

Sheng Liang, et al. ∙

research

∙ 09/13/2021

Graph Algorithms for Multiparallel Word Alignment

With the advent of end-to-end deep learning approaches in machine transl...

Ayyoob Imani, et al. ∙

research

∙ 09/13/2021

Wine is Not v i n. – On the Compatibility of Tokenizations Across Languages

The size of the vocabulary is a central design choice in large pretraine...

Antonis Maronikolakis, et al. ∙

research

∙ 09/08/2021

Continuous Entailment Patterns for Lexical Inference in Context

Combining a pretrained language model (PLM) with textual patterns has be...

Martin Schmitt, et al. ∙

research

∙ 09/08/2021

Discrete and Soft Prompting for Multilingual Models

It has been shown for English that discrete and soft prompting perform s...

Mengjie Zhao, et al. ∙

research

∙ 09/05/2021

Semi-Automated Labeling of Requirement Datasets for Relation Extraction

Creating datasets manually by human annotators is a laborious task that ...

Jeremias Bohn, et al. ∙

research

∙ 07/14/2021

ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus

With more than 7000 languages worldwide, multilingual natural language p...

Ayyoob Imani, et al. ∙

research

∙ 07/02/2021

Data Centric Domain Adaptation for Historical Text with OCR Errors

We propose new methods for in-domain and cross-domain Named Entity Recog...

Luisa März, et al. ∙

research

∙ 04/18/2021

Modeling Ideological Agenda Setting and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity

The increasing polarization of online political discourse calls for comp...

Valentin Hofmann, et al. ∙

research

∙ 04/17/2021

Multi-source Neural Topic Modeling in Multi-view Embedding Spaces

Though word embeddings and topics are complementary representations, sev...

Pankaj Gupta, et al. ∙

research

∙ 04/16/2021

Enriching a Model's Notion of Belief using a Persistent Memory

Although pretrained language models (PTLMs) have been shown to contain s...

Nora Kassner, et al. ∙

research

∙ 04/15/2021

Generating Datasets with Pretrained Language Models

To obtain high-quality sentence embeddings from pretrained language mode...

Timo Schick, et al. ∙

research

∙ 04/14/2021

Static Embeddings as Efficient Knowledge Bases?

Recent research investigates factual knowledge stored in large pretraine...

Philipp Dufter, et al. ∙

research

∙ 02/28/2021

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

When trained on large, unfiltered crawls from the internet, language mod...

Timo Schick, et al. ∙

research

∙ 02/22/2021

Position Information in Transformers: An Overview

Transformers are arguably the main workhorse in recent Natural Language ...

Philipp Dufter, et al. ∙

research

∙ 02/10/2021

Language Models for Lexical Inference in Context

Lexical inference in context (LIiC) is the task of recognizing textual e...

Martin Schmitt, et al. ∙

research

∙ 02/09/2021

Improving Visual Reasoning by Exploiting The Knowledge in Texts

This paper presents a new framework for training image-based classifiers...

Sahand Sharifzadeh, et al. ∙

research

∙ 02/06/2021

Does He Wink or Does He Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models

Recent progress in pretraining language models on large corpora has resu...

Lutfi Kerem Senel, et al. ∙

research

∙ 02/01/2021

Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models

Recently, it has been found that monolingual English language models can...

Nora Kassner, et al. ∙

research

∙ 01/02/2021

Superbizarre Is Not Superb: Improving BERT's Interpretations of Complex Words with Derivational Morphology

How does the input segmentation of pretrained language models (PLMs) aff...

Valentin Hofmann, et al. ∙

research

∙ 12/31/2020

A Closer Look at Few-Shot Crosslingual Transfer: Variance, Benchmarks and Baselines

We present a focused study of few-shot crosslingual transfer, a recently...

Mengjie Zhao, et al. ∙

research

∙ 12/22/2020

Few-Shot Text Generation with Pattern-Exploiting Training

Providing pretrained language models with simple task descriptions or pr...

Timo Schick, et al. ∙

research

∙ 12/21/2020

Subword Sampling for Low Resource Word Alignment

Annotation projection is an important area in NLP that can greatly contr...

Ehsaneddin Asgari, et al. ∙

research

∙ 10/26/2020

Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification

A recent approach for few-shot text classification is to convert textual...

Timo Schick, et al. ∙

research

∙ 10/23/2020

Dynamic Contextualized Word Embeddings

Static word embeddings that represent words by a single vector cannot ca...

Valentin Hofmann, et al. ∙

research

∙ 09/28/2020

Transformers Are Better Than Humans at Identifying Generated Text

Fake information spread via the internet and social media influences pub...

Antonis Maronikolakis, et al. ∙

research

∙ 09/15/2020

It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners

When scaled to hundreds of billions of parameters, pretrained language m...

Timo Schick, et al. ∙

research

∙ 07/16/2020

Investigating Pretrained Language Models for Graph-to-Text Generation

Graph-to-text generation, a subtask of data-to-text generation, aims to ...

Leonardo F. R. Ribeiro, et al. ∙

research

∙ 06/25/2020

Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes

In this paper, we automatically create sentiment dictionaries for predic...

Marina Sedinkina, et al. ∙

research

∙ 06/19/2020

Neural Topic Modeling with Continual Lifelong Learning

Lifelong learning has recently attracted attention in building machine l...

Pankaj Gupta, et al. ∙

research

∙ 06/18/2020

Explainable and Discourse Topic-aware Neural Language Understanding

Marrying topic models and language models exposes language understanding...

Yatin Chaudhary, et al. ∙

research

∙ 06/18/2020

Pre-trained Language Models as Symbolic Reasoners over Knowledge?

How can pre-trained language models (PLMs) learn factual knowledge from ...

Nora Kassner, et al. ∙

research

∙ 06/16/2020

Modeling Graph Structure via Relative Position for Better Text Generation from Knowledge Graphs

We present a novel encoder-decoder architecture for graph-to-text genera...

Martin Schmitt, et al. ∙

research

∙ 05/16/2020

Unsupervised Embedding-based Detection of Lexical Semantic Changes

This paper describes EmbLexChange, a system introduced by the "Life-Lang...

Ehsaneddin Asgari, et al. ∙

research

∙ 05/02/2020

BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA

Khandelwal et al. (2020) show that a k-nearest-neighbor (kNN) component ...

Nora Kassner, et al. ∙

research

∙ 05/02/2020

Generating Derivational Morphology with BERT

Can BERT generate derivationally complex words? We present the first stu...

Valentin Hofmann, et al. ∙

research

∙ 05/01/2020

Identifying Necessary Elements for BERT's Multilinguality

It has been shown that multilingual BERT (mBERT) yields high quality mul...

Philipp Dufter, et al. ∙

research

∙ 04/26/2020

Masking as an Efficient Alternative to Finetuning for Pretrained Language Models

We present an efficient method of utilizing pretrained language models, ...

Mengjie Zhao, et al. ∙

research

∙ 04/25/2020

Quantifying the Contextualization of Word Representations with Semantic Class Probing

Pretrained language models have achieved a new state of the art on many ...

Mengjie Zhao, et al. ∙

research

∙ 04/18/2020

SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings

Word alignments are useful for tasks like statistical and neural machine...

Masoud Jalili Sabet, et al. ∙

research

∙ 04/07/2020

Inexpensive Domain Adaptation of Pretrained Language Models: A Case Study on Biomedical Named Entity Recognition

Domain adaptation of Pretrained Language Models (PTLMs) is typically ach...

Nina Poerner, et al. ∙

research

∙ 01/21/2020

Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference

Some NLP tasks can be solved in a fully unsupervised fashion by providin...

Timo Schick, et al. ∙

research

∙ 01/07/2020

Multipurpose Intelligent Process Automation via Conversational Assistant

Intelligent Process Automation (IPA) is an emerging technology with a pr...

Alena Moiseeva, et al. ∙

research

∙ 12/12/2019

Extending Machine Language Models toward Human-Level Language Understanding

Language is central to human intelligence. We review recent breakthrough...

James L. McClelland, et al. ∙

research

∙ 11/12/2019

Morphological Segmentation Inside-Out

Morphological segmentation has traditionally been modeled with non-hiera...

Ryan Cotterell, et al. ∙

Hinrich Schütze

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