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Layer or Representation Space: What makes BERT-based Evaluation Metrics Robust?
Doan Nam Long Vu, Nafise Sadat Moosavi, Steffen Eger
The evaluation of recent embedding-based evaluation metrics for text generation is primarily based on measuring their correlation with human evaluations on standard benchmarks. How…
Transformers with Learnable Activation Functions
Haishuo Fang, Ji-Ung Lee, Nafise Sadat Moosavi +1
Activation functions can have a significant impact on reducing the topological complexity of input data and therefore improve the performance of the model. Selecting a suitable act…
Scoring Coreference Chains with Split-Antecedent Anaphors
Silviu Paun, Juntao Yu, Nafise Sadat Moosavi +1
Anaphoric reference is an aspect of language interpretation covering a variety of types of interpretation beyond the simple case of identity reference to entities introduced via no…
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization
Prasetya Ajie Utama, Joshua Bambrick, Nafise Sadat Moosavi +1
Neural abstractive summarization models are prone to generate summaries which are factually inconsistent with their source documents. Previous work has introduced the task of recog…
Adaptable Adapters
Nafise Sadat Moosavi, Quentin Delfosse, Kristian Kersting +1
State-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters provide a parameter-efficient alternative for the full finetuning in which we can…
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models
Dominic Petrak, Nafise Sadat Moosavi, Iryna Gurevych
State-of-the-art pretrained language models tend to perform below their capabilities when applied out-of-the-box on tasks that require understanding and working with numbers. Recen…