Papers by Fabian Suchanek
TINA: Textual Inference with Negation Augmentation (2022.findings-emnlp)
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| Challenge: | Existing transformer-based models perform poorly on textual entailment when examples contain negations. |
| Approach: | They propose a new definition of textual entailment that captures negation and a principled technique for negated data augmentation that can be combined with unlikelihood loss function. |
| Outcome: | The proposed method significantly improves on textual entailment datasets with negations without sacrificing performance on datasets without negation. |
Reconfidencing LLMs from the Grouping Loss Perspective (2024.findings-emnlp)
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| Challenge: | Existing methods to calibrate confidence scores for large language models often overlook biases towards certain groups, such as specific nationalities. |
| Approach: | They propose a method to calibrate confidence scores of Large Language Models by considering different groups, a process they call reconfidencing. |
| Outcome: | The proposed method mitigates biases against minority groups, the authors show . they show that the proposed method is more reliable than existing methods . |
The Locality and Symmetry of Positional Encodings (2023.findings-emnlp)
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| Challenge: | Existing work uses fixed or learned positional encodings to infuse word order information into transformer-based language models. |
| Approach: | They conduct a systematic study of positional encodings in Bidirectional Masked Language Models. |
| Outcome: | The proposed model improves performance across a wide range of natural language understanding tasks. |
Using a Knowledge Base to Automatically Annotate Speech Corpora and to Identify Sociolinguistic Variation (2022.lrec-1)
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| Challenge: | Speech characteristics vary from speaker to speaker due to many factors, including communication context, provenance, age, and social background. |
| Approach: | They propose a method that uses a knowledge base to provide speaker-specific information. |
| Outcome: | The proposed method can be used to enrich existing corpora with speaker-specific information and to correlate with diastratic features. |
MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification (2024.naacl-long)
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| Challenge: | Fallacy classification is a task of broad importance due to advances in deep learning and availability of more data. |
| Approach: | They propose a new annotation scheme tailored for subjective NLP tasks and a method designed to handle subjectivity. |
| Outcome: | The proposed approach integrates existing fallacy classification datasets with new ones. |
Learning High-Quality and General-Purpose Phrase Representations (2024.findings-eacl)
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| Challenge: | Pre-trained language models for phrasal embeddings are unnecessarily complex and require to be pre-tuned on a corpus with context sentences. |
| Approach: | They propose a framework to learn phrase representations in a context-free fashion. |
| Outcome: | The proposed framework generates superior phrase embeddings while requiring a smaller model size. |
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost (2022.acl-long)
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| Challenge: | State-of-the-art NLP systems are brittle when faced with Out-ofVocabulary words . we present a framework that extends word embeddings and makes them robust to OOV . |
| Approach: | They propose a framework that extends existing word embeddings and makes them robust to OOV. |
| Outcome: | The proposed model performs better on original datasets and corrupted variants than previous competitors. |
LogiTorch: A PyTorch-based library for logical reasoning on natural language (2022.emnlp-demos)
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| Challenge: | Logical reasoning on natural language is one of the most challenging tasks for deep learning models. |
| Approach: | They propose a library that includes logical reasoning benchmarks and models . they use a dataset to run the library or fine tune the models with different hyperparameters . |
| Outcome: | This library includes logical reasoning benchmarks, models, utility functions and co-reference resolution. |
A Survey of Meaning Representations – From Theory to Practical Utility (2024.naacl-long)
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| Challenge: | Symbolic meaning representations of natural language text have been studied since at least the 1960s . with the availability of large annotated corpora, the field has recently seen several new developments . |
| Approach: | They propose a framework for expressing meaning in natural language text using annotated corpora and a set of tools for machine learning. |
| Outcome: | The frameworks are based on a set of theoretical and practical problems and their applications. |