8 citations · 15 across the 11 of their papers we have counts for
13 papers
Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?
Marina Sedinkina, Martin Schmitt, Hinrich Schütze
The practical success of much of NLP depends on the availability of training data. However, in real-world scenarios, training data is often scarce, not least because many applicati…
Scene Graph Generation for Better Image Captioning?
Maximilian Mozes, Martin Schmitt, Vladimir Golkov +2
We investigate the incorporation of visual relationships into the task of supervised image caption generation by proposing a model that leverages detected objects and auto-generate…
Continuous Entailment Patterns for Lexical Inference in Context
Martin Schmitt, Hinrich Schütze
Combining a pretrained language model (PLM) with textual patterns has been shown to help in both zero- and few-shot settings. For zero-shot performance, it makes sense to design pa…
Semi-Automated Labeling of Requirement Datasets for Relation Extraction
Jeremias Bohn, Jannik Fischbach, Martin Schmitt +2
Creating datasets manually by human annotators is a laborious task that can lead to biased and inhomogeneous labels. We propose a flexible, semi-automatic framework for labeling da…
Ranking vs. Classifying: Measuring Knowledge Base Completion Quality
Marina Speranskaya, Martin Schmitt, Benjamin Roth
Knowledge base completion (KBC) methods aim at inferring missing facts from the information present in a knowledge base (KB) by estimating the likelihood of candidate facts. In the…
Position Information in Transformers: An Overview
Philipp Dufter, Martin Schmitt, Hinrich Schütze
Transformers are arguably the main workhorse in recent Natural Language Processing research. By definition a Transformer is invariant with respect to reordering of the input. Howev…
Language Models for Lexical Inference in Context
Martin Schmitt, Hinrich Schütze
Lexical inference in context (LIiC) is the task of recognizing textual entailment between two very similar sentences, i.e., sentences that only differ in one expression. It can the…
Improving Scene Graph Classification by Exploiting Knowledge from Texts
Sahand Sharifzadeh, Sina Moayed Baharlou, Martin Schmitt +2
Training scene graph classification models requires a large amount of annotated image data. Meanwhile, scene graphs represent relational knowledge that can be modeled with symbolic…
Investigating Pretrained Language Models for Graph-to-Text Generation
Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze +1
Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper, we investigate two recently proposed pretrained language models (PLMs) and analyze the…
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
Martin Schmitt, Leonardo F. R. Ribeiro, Philipp Dufter +2
We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation. With our novel graph self-attention, the encoding of a node relies on all…
SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference
Martin Schmitt, Hinrich Schütze
We present SherLIiC, a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) ~960k unlabel…
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing
Martin Schmitt, Sahand Sharifzadeh, Volker Tresp +1
Knowledge graphs (KGs) can vary greatly from one domain to another. Therefore supervised approaches to both graph-to-text generation and text-to-graph knowledge extraction (semanti…
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…