91 citations · 104 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks
Martin Schmitt, Simon Steinheber, Konrad Schreiber +1
In this work, we propose a new model for aspect-based sentiment analysis. In contrast to previous approaches, we jointly model the detection of aspects and the classification of th…
Position-aware Self-attention with Relative Positional Encodings for Slot Filling
Ivan Bilan, Benjamin Roth
This paper describes how to apply self-attention with relative positional encodings to the task of relation extraction. We propose to use the self-attention encoder layer together…
Joint Bootstrapping Machines for High Confidence Relation Extraction
Pankaj Gupta, Benjamin Roth, Hinrich Schütze
Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge…
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…