activity
20152023
most citedMeetUp! A Corpus of Joint Activity Dialogues in a Visual Environment

25 citations · 73 across the 24 of their papers we have counts for

collaborators
Showing 2023 · cs.CLShow all

10 papers · 2 filters

cs.CL2023

Neural Conversation Models and How to Rein Them in: A Survey of Failures and Fixes

Fabian Galetzka, Anne Beyer, David Schlangen

Recent conditional language models are able to continue any kind of text source in an often seemingly fluent way. This fact encouraged research in the area of open-domain conversat…

cs.CL2023

The Road to Quality is Paved with Good Revisions: A Detailed Evaluation Methodology for Revision Policies in Incremental Sequence Labelling

Brielen Madureira, Patrick Kahardipraja, David Schlangen

Incremental dialogue model components produce a sequence of output prefixes based on incoming input. Mistakes can occur due to local ambiguities or to wrong hypotheses, making the…

cs.CL2023

"Are you telling me to put glasses on the dog?'' Content-Grounded Annotation of Instruction Clarification Requests in the CoDraw Dataset

Brielen Madureira, David Schlangen

Instruction Clarification Requests are a mechanism to solve communication problems, which is very functional in instruction-following interactions. Recent work has argued that the…

cs.CL2023

Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples

Philipp Sadler, David Schlangen

NLP tasks are typically defined extensionally through datasets containing example instantiations (e.g., pairs of image i and text t), but motivated intensionally through capabiliti…

cs.CL2023

Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks

Sherzod Hakimov, David Schlangen

Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. While being actively researched, multimodal mod…

cs.CL2023★ 1 cited

TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model

Patrick Kahardipraja, Brielen Madureira, David Schlangen

Language is by its very nature incremental in how it is produced and processed. This property can be exploited by NLP systems to produce fast responses, which has been shown to be…