3 citations · 7 across the 20 of their papers we have counts for
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The Course of News Events: A Comparison of Bottom-Up and Top-Down Approaches for Collecting Text-Based Data about Disasters
Brielen Madureira, Andreas Niekler, Mariana Madruga de Brito
News articles are an important source of information on disaster impacts and adaptation. A key methodological challenge in socio-environmental studies is how to select a representa…
How Loud Rumbles Hit Newsstands: A Data Analysis of Coverage and Spatial Bias in German News about Landslides Around the World
Brielen Madureira, Andreas Niekler, Marc Keuschnigg +1
Landslides often hit newsstands due to their destructive and potentially fatal effects. News are a valuable source of information for creating or enriching disaster databases and f…
Assessing socio-economic climate impacts from text data
Mariana Madruga de Brito, Brielen Madureira, Taís Maria Nunes Carvalho +15
Recent advances in natural language processing (NLP) and large language models (LLMs) have enabled the systematic use of large-scale textual data from news, social media, and repor…
The Newsworthiness of Brazilian Distress: A Peak Analysis on Time Series of International Media Attention to Disasters in Brazil
Brielen Madureira, Andreas Niekler, Marc Keuschnigg +1
Media coverage influences disaster response, yet the drivers of international media attention to local events remain unevenly understood. Brazil offers a compelling case: some of i…
Retrieving Floods without Floodlights: Topic Models as Binary Classifiers for Extreme Climate Events in German News
Brielen Madureira, Mariana Madruga de Brito, Andreas Niekler
In studies of media coverage of extreme climate events, NLP methods have become indispensable for identifying relevant texts in large news databases. Still, enough annotated data t…
Geolocating News about Extreme Climate Events: A Comparative Analysis of Off-the-Shelf Tools for Toponym Identification in German
Brielen Madureira, Mariana Madruga de Brito, Andreas Niekler
Determining the geolocation of extreme climate events and disasters in texts is a common problem in climate impact and adaptation research. Named-entity recognition (NER) tools are…
clembench-2024: A Challenging, Dynamic, Complementary, Multilingual Benchmark and Underlying Flexible Framework for LLMs as Multi-Action Agents
Anne Beyer, Kranti Chalamalasetti, Sherzod Hakimov +3
It has been established in recent work that Large Language Models (LLMs) can be prompted to "self-play" conversational games that probe certain capabilities (general instruction fo…
It Couldn't Help But Overhear: On the Limits of Modelling Meta-Communicative Grounding Acts with Supervised Learning
Brielen Madureira, David Schlangen
Active participation in a conversation is key to building common ground, since understanding is jointly tailored by producers and recipients. Overhearers are deprived of the privil…
When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality
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. Causal models are forced to output one…
Taking Action Towards Graceful Interaction: The Effects of Performing Actions on Modelling Policies for Instruction Clarification Requests
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. Despite their impor…
Revising with a Backward Glance: Regressions and Skips during Reading as Cognitive Signals for Revision Policies in Incremental Processing
Brielen Madureira, Pelin Çelikkol, David Schlangen
In NLP, incremental processors produce output in instalments, based on incoming prefixes of the linguistic input. Some tokens trigger revisions, causing edits to the output hypothe…
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…
"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…
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…
Clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents
Kranti Chalamalasetti, Jana Götze, Sherzod Hakimov +3
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-…
Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Dataset
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…
Can Visual Dialogue Models Do Scorekeeping? Exploring How Dialogue Representations Incrementally Encode Shared Knowledge
Brielen Madureira, David Schlangen
Cognitively plausible visual dialogue models should keep a mental scoreboard of shared established facts in the dialogue context. We propose a theory-based evaluation method for in…
Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU
Patrick Kahardipraja, Brielen Madureira, David Schlangen
Incremental processing allows interactive systems to respond based on partial inputs, which is a desirable property e.g. in dialogue agents. The currently popular Transformer archi…
Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLU
Brielen Madureira, David Schlangen
While humans process language incrementally, the best language encoders currently used in NLP do not. Both bidirectional LSTMs and Transformers assume that the sequence that is to…
An Overview of Natural Language State Representation for Reinforcement Learning
Brielen Madureira, David Schlangen
A suitable state representation is a fundamental part of the learning process in Reinforcement Learning. In various tasks, the state can either be described by natural language or…