Hierarchy-aware Learning of Sequential Tool Usage via Semi-automatically Constructed Taxonomies

no code implementations • COLING (MWE) 2020 • Nima Nabizadeh, Martin Heckmann, Dorothea Kolossa

When repairing a device, humans employ a series of tools that corresponds to the arrangement of the device components.

MyFixit: An Annotated Dataset, Annotation Tool, and Baseline Methods for Information Extraction from Repair Manuals

no code implementations • LREC 2020 • Nima Nabizadeh, Dorothea Kolossa, Martin Heckmann

In this paper, we, therefore, focus on information extraction (IE) from the instructional text in repair manuals.

Speaker-adapted neural-network-based fusion for multimodal reference resolution

no code implementations • WS 2019 • Diana Kleingarn, Nima Nabizadeh, Martin Heckmann, Dorothea Kolossa

Humans use a variety of approaches to reference objects in the external world, including verbal descriptions, hand and head gestures, eye gaze or any combination of them.

From Explainability to Explanation: Using a Dialogue Setting to Elicit Annotations with Justifications

no code implementations • WS 2019 • Nazia Attari, Martin Heckmann, David Schlangen

Despite recent attempts in the field of explainable AI to go beyond black box prediction models, typically already the training data for supervised machine learning is collected in a manner that treats the annotator as a {``}black box{''}, the internal workings of which remains unobserved.