no code implementations • NAACL (TeachingNLP) 2021 • Brielen Madureira
This report describes the course Evaluation of NLP Systems, taught for Computational Linguistics undergraduate students during the winter semester 20/21 at the University of Potsdam, Germany.
1 code implementation • 20 Feb 2024 • Brielen Madureira, Patrick Kahardipraja, David Schlangen
Incremental models that process sentences one token at a time will sometimes encounter points where more than one interpretation is possible.
1 code implementation • 30 Jan 2024 • Brielen Madureira, David Schlangen
Clarification requests are a mechanism to help solve communication problems, e. g. due to ambiguity or underspecification, in instruction-following interactions.
1 code implementation • 27 Oct 2023 • Brielen Madureira, Pelin Çelikkol, David Schlangen
In NLP, incremental processors produce output in instalments, based on incoming prefixes of the linguistic input.
1 code implementation • 28 Jul 2023 • Brielen Madureira, Patrick Kahardipraja, David Schlangen
Incremental dialogue model components produce a sequence of output prefixes based on incoming input.
no code implementations • 4 Jun 2023 • Brielen Madureira, David Schlangen
Instruction Clarification Requests are a mechanism to solve communication problems, which is very functional in instruction-following interactions.
1 code implementation • 22 May 2023 • Kranti Chalamalasetti, Jana Götze, Sherzod Hakimov, Brielen Madureira, Philipp Sadler, David Schlangen
Recent work has proposed a methodology for the systematic evaluation of "Situated Language Understanding Agents"-agents that operate in rich linguistic and non-linguistic contexts-through testing them in carefully constructed interactive settings.
1 code implementation • 18 May 2023 • Patrick Kahardipraja, Brielen Madureira, David Schlangen
RNNs are fast but monotonic (cannot correct earlier output, which can be necessary in incremental processing).
1 code implementation • 28 Feb 2023 • Brielen Madureira, David Schlangen
In visual instruction-following dialogue games, players can engage in repair mechanisms in face of an ambiguous or underspecified instruction that cannot be fully mapped to actions in the world.
1 code implementation • ACL 2022 • Brielen Madureira, David Schlangen
Our conclusion is that the ability to make the distinction between shared and privately known statements along the dialogue is moderately present in the analysed models, but not always incrementally consistent, which may partially be due to the limited need for grounding interactions in the original task.
1 code implementation • EMNLP 2021 • Patrick Kahardipraja, Brielen Madureira, David Schlangen
In this work, we examine the feasibility of LT for incremental NLU in English.
1 code implementation • EMNLP 2020 • Brielen Madureira, David Schlangen
While humans process language incrementally, the best language encoders currently used in NLP do not.
no code implementations • ICML Workshop LaReL 2020 • Brielen Madureira, David Schlangen
A suitable state representation is a fundamental part of the learning process in Reinforcement Learning.