Search Results for author: Nina Poerner

Found 12 papers, 6 papers with code

E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT

1 code implementation Findings of the Association for Computational Linguistics 2020 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., 2016) with BERT's native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors.

Entity Embeddings Entity Linking +3

Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity

no code implementations ACL 2020 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.

Dimensionality Reduction Semantic Textual Similarity +2

Interpretable Question Answering on Knowledge Bases and Text

no code implementations ACL 2019 Alona Sydorova, Nina Poerner, Benjamin Roth

Our results suggest that IP provides better explanations than LIME or attention, according to both automatic and human evaluation.

Question Answering

Interpretable Textual Neuron Representations for NLP

2 code implementations WS 2018 Nina Poerner, Benjamin Roth, Hinrich Schütze

Input optimization methods, such as Google Deep Dream, create interpretable representations of neurons for computer vision DNNs.

Neural Architectures for Open-Type Relation Argument Extraction

no code implementations5 Mar 2018 Benjamin Roth, Costanza Conforti, Nina Poerner, Sanjeev Karn, Hinrich Schütze

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 with title X"), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, e. g. X: the title of a book or a work of art) from the corpus.

Question Answering Relation +2

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