Dominik Stammbach


Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data
Dominik Stammbach | Maria Antoniak | Elliott Ash
Proceedings of the 4th Workshop of Narrative Understanding (WNU2022)

This paper shows how to use large-scale pretrained language models to extract character roles from narrative texts without domain-specific training data. Queried with a zero-shot question-answering prompt, GPT-3 can identify the hero, villain, and victim in diverse domains: newspaper articles, movie plot summaries, and political speeches.

DocSCAN: Unsupervised Text Classification via Learning from Neighbors
Dominik Stammbach | Elliott Ash
Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)


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Evidence Selection as a Token-Level Prediction Task
Dominik Stammbach
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)

In Automated Claim Verification, we retrieve evidence from a knowledge base to determine the veracity of a claim. Intuitively, the retrieval of the correct evidence plays a crucial role in this process. Often, evidence selection is tackled as a pairwise sentence classification task, i.e., we train a model to predict for each sentence individually whether it is evidence for a claim. In this work, we fine-tune document level transformers to extract all evidence from a Wikipedia document at once. We show that this approach performs better than a comparable model classifying sentences individually on all relevant evidence selection metrics in FEVER. Our complete pipeline building on this evidence selection procedure produces a new state-of-the-art result on FEVER, a popular claim verification benchmark.


DOMLIN at SemEval-2019 Task 8: Automated Fact Checking exploiting Ratings in Community Question Answering Forums
Dominik Stammbach | Stalin Varanasi | Guenter Neumann
Proceedings of the 13th International Workshop on Semantic Evaluation

In the following, we describe our system developed for the Semeval2019 Task 8. We fine-tuned a BERT checkpoint on the qatar living forum dump and used this checkpoint to train a number of models. Our hand-in for subtask A consists of a fine-tuned classifier from this BERT checkpoint. For subtask B, we first have a classifier deciding whether a comment is factual or non-factual. If it is factual, we retrieve intra-forum evidence and using this evidence, have a classifier deciding the comment’s veracity. We trained this classifier on ratings which we crawled from

Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task
Dominik Stammbach | Guenter Neumann
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

This paper contains our system description for the second Fact Extraction and VERification (FEVER) challenge. We propose a two-staged sentence selection strategy to account for examples in the dataset where evidence is not only conditioned on the claim, but also on previously retrieved evidence. We use a publicly available document retrieval module and have fine-tuned BERT checkpoints for sentence se- lection and as the entailment classifier. We report a FEVER score of 68.46% on the blind testset.