Search Results for author: Raphael Schumann

Found 9 papers, 6 papers with code

Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMap

1 code implementation30 Aug 2023 Michael Staniek, Raphael Schumann, Maike Züfle, Stefan Riezler

We present Text-to-OverpassQL, a task designed to facilitate a natural language interface for querying geodata from OpenStreetMap (OSM).

VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View

1 code implementation12 Jul 2023 Raphael Schumann, Wanrong Zhu, Weixi Feng, Tsu-Jui Fu, Stefan Riezler, William Yang Wang

In this work, we propose VELMA, an embodied LLM agent that uses a verbalization of the trajectory and of visual environment observations as contextual prompt for the next action.

Decision Making Natural Language Understanding +1

Backward Compatibility During Data Updates by Weight Interpolation

no code implementations25 Jan 2023 Raphael Schumann, Elman Mansimov, Yi-An Lai, Nikolaos Pappas, Xibin Gao, Yi Zhang

This method interpolates between the weights of the old and new model and we show in extensive experiments that it reduces negative flips without sacrificing the improved accuracy of the new model.


Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem

no code implementations ACL 2021 Raphael Schumann, Stefan Riezler

Car-focused navigation services are based on turns and distances of named streets, whereas navigation instructions naturally used by humans are centered around physical objects called landmarks.

Natural Language Landmark Navigation Instructions Generation

Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification

1 code implementation CONLL 2019 Raphael Schumann, Ines Rehbein

Active learning (AL) is a technique for reducing manual annotation effort during the annotation of training data for machine learning classifiers.

Active Learning General Classification +3

Unsupervised Abstractive Sentence Summarization using Length Controlled Variational Autoencoder

1 code implementation14 Sep 2018 Raphael Schumann

In this work we present an unsupervised approach to summarize sentences in abstractive way using Variational Autoencoder (VAE).

Sentence Unsupervised Sentence Summarization

Scalable Wide and Deep Learning for Computer Assisted Coding

no code implementations NAACL 2018 Marilisa Amoia, Frank Diehl, Jesus Gimenez, Joel Pinto, Raphael Schumann, Fabian Stemmer, Paul Vozila, Yi Zhang

In recent years the use of electronic medical records has accelerated resulting in large volumes of medical data when a patient visits a healthcare facility.

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