2 citations · 2 across the 2 of their papers we have counts for
7 papers
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data
Sanjeev Kumar Karn, Francine Chen, Yan-Ying Chen +2
Interleaved texts, where posts belonging to different threads occur in a sequence, commonly occur in online chat posts, so that it can be time-consuming to quickly obtain an overvi…
Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA
Nina Poerner, Ulli Waltinger, Hinrich Schütze
Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text. While successful, this approach is expensive in ter…
AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings
Florian Buettner, John Piorkowski, Ian McCulloh +1
To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems…
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity
Nina Poerner, Ulli Waltinger, Hinrich Schütze
We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings. We apply, extend and evalua…
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT
Nina Poerner, Ulli Waltinger, Hinrich Schütze
We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 201…
A Hierarchical Decoder with Three-level Hierarchical Attention to Generate Abstractive Summaries of Interleaved Texts
Sanjeev Kumar Karn, Francine Chen, Yan-Ying Chen +2
Interleaved texts, where posts belonging to different threads occur in one sequence, are a common occurrence, e.g., online chat conversations. To quickly obtain an overview of such…