Papers by Tilman Beck
Classification and Clustering of Arguments with Contextualized Word Embeddings (P19-1)
Copied to clipboard
| Challenge: | Existing methods for argument mining focus on analyzing local argumentation structures, but information-seeking approaches need to be able to deal with heterogeneous sources and topics. |
| Approach: | They propose to use contextualized word embeddings to classify and cluster topic-dependent arguments using a UKP Sentential Argument Mining Corpus and IBM Debater - Evidence Sentences datasets. |
| Outcome: | The proposed method improves state-of-the-art on argument classification and clustering tasks and across multiple datasets. |
Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon (2024.eacl-long)
Copied to clipboard
| Challenge: | Prior work extended multilingual models to other languages due to the unavailability of labeled and unlabeled training data. |
| Approach: | They use multilingual lexicons to enhance multilingual models capabilities in low-resource languages . they focus on zero-shot sentiment analysis tasks across 34 languages based on a single sentence . |
| Outcome: | The proposed model improves zero-shot performance across 34 languages without using any sentence-level sentiment data. |
AdapterDrop: On the Efficiency of Adapters in Transformers (2021.emnlp-main)
Copied to clipboard
Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, Iryna Gurevych
| Challenge: | Recent approaches to transformer models are expensive to fine-tune, slow for inference, and have large storage requirements. |
| Approach: | They propose a method to remove adapters from transformer layers during training and inference . they show that AdapterDrop can dynamically reduce computational overhead . |
| Outcome: | The proposed approach reduces computational overhead while maintaining performance over multiple tasks with minimal loss of performance. |
AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters (2022.acl-demo)
Copied to clipboard
Tilman Beck, Bela Bohlender, Christina Viehmann, Vincent Hane, Yanik Adamson, Jaber Khuri, Jonas Brossmann, Jonas Pfeiffer, Iryna Gurevych
| Challenge: | AdapterHub Playground is an open-access tool for researchers to use pretrained language models without writing a single line of code. |
| Approach: | They propose a tool which allows researchers to leverage pretrained models without writing a single line of code for a variety of NLP tasks. |
| Outcome: | The proposed model can be used for prediction, training and analysis of textual data without writing a single line of code. |
Composing Structure-Aware Batches for Pairwise Sentence Classification (2022.findings-acl)
Copied to clipboard
| Challenge: | Identifying the relation between two sentences requires datasets with pairwise annotations. |
| Approach: | They propose three batch composition strategies to incorporate such information and measure their performance over 14 heterogeneous pairwise sentence classification tasks. |
| Outcome: | The proposed methods show that the pre-trained language model can benefit from having such structural information in a low-resource setting. |
The challenges of temporal alignment on Twitter during crises (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models consider data spanning years to decades, but shorter time spans are critical for crisis data. |
| Approach: | They propose to use domain adaptation techniques to cope with performance degradation by leveraging domain adaptation. |
| Outcome: | The proposed models outperform baseline models under conditions of natural and human-induced disasters while highlighting the limitations of current models. |
Investigating label suggestions for opinion mining in German Covid-19 social media (2021.acl-long)
Copied to clipboard
| Challenge: | Existing difficulties in data annotation are due to prolonged data gathering processes or opinion surveys being subject to reactivity. |
| Approach: | They propose to use label suggestions to improve annotation efficiency in german Covid-19 data by providing annotators with pre-recorded annotations. |
| Outcome: | The proposed model improves inter-annotator agreement and annotation quality in a controlled study with social science students. |
Robust Integration of Contextual Information for Cross-Target Stance Detection (2023.starsem-1)
Copied to clipboard
| Challenge: | Existing stance detection models do not take into account relevant contextual information which allows for inferring the stance correctly. |
| Approach: | They propose an approach to integrate contextual information as text into pretrained language models by prompting large language models. |
| Outcome: | The proposed approach outperforms baselines on a large and diverse stance detection benchmark in a cross-target setup, i.e. for targets unseen during training. |
Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing studies on sociodemographic prompting have not explored the effectiveness of this technique. |
| Approach: | They propose to use sociodemographic prompting to steer models towards answers that humans with specific sociodemography would give. |
| Outcome: | The proposed technique can improve zero-shot learning by focusing on human sociodemographic profiles. |