Search Results for author: Hinrich Sch{\"u}tze

Found 61 papers, 8 papers with code

Identifying Elements Essential for BERT's Multilinguality

no code implementations EMNLP 2020 Philipp Dufter, Hinrich Sch{\"u}tze

We aim to identify architectural properties of BERT and linguistic properties of languages that are necessary for BERT to become multilingual.

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

1 code implementation EACL 2021 Timo Schick, Hinrich Sch{\"u}tze

Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with {``}task descriptions{''} in natural language (e. g., Radford et al., 2019).

Few-Shot Text Classification Language Modelling +3

EmbLexChange at SemEval-2020 Task 1: Unsupervised Embedding-based Detection of Lexical Semantic Changes

1 code implementation SEMEVAL 2020 Ehsaneddin Asgari, Christoph Ringlstetter, Hinrich Sch{\"u}tze

This paper describes EmbLexChange, a system introduced by the {``}Life-Language{''} team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes.

Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention

1 code implementation COLING 2020 Philipp Dufter, Martin Schmitt, Hinrich Sch{\"u}tze

Self-Attention Networks (SANs) are an integral part of successful neural architectures such as Transformer (Vaswani et al., 2017), and thus of pretrained language models such as BERT (Devlin et al., 2019) or GPT-3 (Brown et al., 2020).

Language Modelling Part-Of-Speech Tagging +1

Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations

1 code implementation COLING 2020 Sheng Liang, Philipp Dufter, Hinrich Sch{\"u}tze

Pretrained language models (PLMs) learn stereotypes held by humans and reflected in text from their training corpora, including gender bias.

Language Modelling Sentence

Predicting the Growth of Morphological Families from Social and Linguistic Factors

no code implementations ACL 2020 Valentin Hofmann, Janet Pierrehumbert, Hinrich Sch{\"u}tze

We present the first study that examines the evolution of morphological families, i. e., sets of morphologically related words such as {``}trump{''}, {``}antitrumpism{''}, and {``}detrumpify{''}, in social media.

A Graph Auto-encoder Model of Derivational Morphology

no code implementations ACL 2020 Valentin Hofmann, Hinrich Sch{\"u}tze, Janet Pierrehumbert

The auto-encoder models MWF in English surprisingly well by combining syntactic and semantic information with associative information from the mental lexicon.

LMU Bilingual Dictionary Induction System with Word Surface Similarity Scores for BUCC 2020

no code implementations LREC 2020 Silvia Severini, Viktor Hangya, Alex Fraser, er, Hinrich Sch{\"u}tze

We participate in both the open and closed tracks of the shared task and we show improved results of our method compared to simple vector similarity based approaches.

Machine Translation Translation +2

ThaiLMCut: Unsupervised Pretraining for Thai Word Segmentation

1 code implementation LREC 2020 Suteera Seeha, Ivan Bilan, Liliana Mamani Sanchez, Johannes Huber, Michael Matuschek, Hinrich Sch{\"u}tze

We propose ThaiLMCut, a semi-supervised approach for Thai word segmentation which utilizes a bi-directional character language model (LM) as a way to leverage useful linguistic knowledge from unlabeled data.

Language Modelling Segmentation +1

Towards Summarization for Social Media - Results of the TL;DR Challenge

no code implementations WS 2019 Shahbaz Syed, Michael V{\"o}lske, Nedim Lipka, Benno Stein, Hinrich Sch{\"u}tze, Martin Potthast

In this paper, we report on the results of the TL;DR challenge, discussing an extensive manual evaluation of the expected properties of a good summary based on analyzing the comments provided by human annotators.

A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction

no code implementations ACL 2019 Mengjie Zhao, Hinrich Sch{\"u}tze

We present a new method for sentiment lexicon induction that is designed to be applicable to the entire range of typological diversity of the world{'}s languages.

Domain Adaptation

Task Proposal: The TL;DR Challenge

no code implementations WS 2018 Shahbaz Syed, Michael V{\"o}lske, Martin Potthast, Nedim Lipka, Benno Stein, Hinrich Sch{\"u}tze

The TL;DR challenge fosters research in abstractive summarization of informal text, the largest and fastest-growing source of textual data on the web, which has been overlooked by summarization research so far.

Abstractive Text Summarization Information Retrieval +1

Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable

1 code implementation ACL 2018 Viktor Hangya, Fabienne Braune, Alex Fraser, er, Hinrich Sch{\"u}tze

Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language.

Bilingual Lexicon Induction Classification +7

AutoExtend: Combining Word Embeddings with Semantic Resources

no code implementations CL 2017 Sascha Rothe, Hinrich Sch{\"u}tze

We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource.

Learning Word Embeddings Sentiment Analysis +1

End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification

no code implementations EACL 2017 Sanjeev Karn, Ulli Waltinger, Hinrich Sch{\"u}tze

We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoder-decoder that generates paths in the type hierarchy and can be trained end-to-end.

Classification General Classification +1

Nonsymbolic Text Representation

no code implementations EACL 2017 Hinrich Sch{\"u}tze

We introduce the first generic text representation model that is completely nonsymbolic, i. e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text.

Denoising Entity Typing +2

Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction

1 code implementation COLING 2016 Pankaj Gupta, Hinrich Sch{\"u}tze, Bernt Andrassy

This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies.

Classification Entity Extraction using GAN +7

Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank

no code implementations LREC 2012 Christian Scheible, Hinrich Sch{\"u}tze

We present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers.

Document Classification General Classification +1

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