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20182022
most citedRanking vs. Classifying: Measuring Knowledge Base Completion Quality

3 citations · 6 across the 6 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

cs.CV2021★ 2 cited

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…

cs.CL2021

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…

cs.SE2021

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…

cs.AI2021★ 3 cited

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…

cs.CL2021

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…

cs.CL2021

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…