3 citations · 6 across the 5 of their papers we have counts for
8 papers
Data Centric Domain Adaptation for Historical Text with OCR Errors
Luisa März, Stefan Schweter, Nina Poerner +2
We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French. For the cross-domain case, we address domain shift by…
Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA
Nina Poerner, Ulli Waltinger, Hinrich Schütze
Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text. While successful, this approach is expensive in ter…
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity
Nina Poerner, Ulli Waltinger, Hinrich Schütze
We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings. We apply, extend and evalua…
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT
Nina Poerner, Ulli Waltinger, Hinrich Schütze
We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 201…
Interpretable Question Answering on Knowledge Bases and Text
Alona Sydorova, Nina Poerner, Benjamin Roth
Interpretability of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering…
Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective
Nina Poerner, Masoud Jalili Sabet, Benjamin Roth +1
Count-based word alignment methods, such as the IBM models or fast-align, struggle on very small parallel corpora. We therefore present an alternative approach based on cross-lingu…
Interpretable Textual Neuron Representations for NLP
Nina Poerner, Benjamin Roth, Hinrich Schütze
Input optimization methods, such as Google Deep Dream, create interpretable representations of neurons for computer vision DNNs. We propose and evaluate ways of transferring this t…
Neural Architectures for Open-Type Relation Argument Extraction
Benjamin Roth, Costanza Conforti, Nina Poerner +2
In this work, we introduce the task of Open-Type Relation Argument Extraction (ORAE): Given a corpus, a query entity Q and a knowledge base relation (e.g.,"Q authored notable work…