596 citations · 1.1k across the 37 of their papers we have counts for
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Knowledge-Augmented Language Models for Cause-Effect Relation Classification
Pedram Hosseini, David A. Broniatowski, Mona Diab
Previous studies have shown the efficacy of knowledge augmentation methods in pretrained language models. However, these methods behave differently across domains and downstream ta…
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs
Peter Hase, Mona Diab, Asli Celikyilmaz +5
Do language models have beliefs about the world? Dennett (1995) famously argues that even thermostats have beliefs, on the view that a belief is simply an informational state decou…
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization
Alexander R. Fabbri, Xiaojian Wu, Srini Iyer +2
Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions. Each question thr…
Discrete Cosine Transform as Universal Sentence Encoder
Nada Almarwani, Mona Diab
Modern sentence encoders are used to generate dense vector representations that capture the underlying linguistic characteristics for a sequence of words, including phrases, senten…
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala +6
The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are lin…
Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation
Adithya Renduchintala, Denise Diaz, Kenneth Heafield +2
Is bias amplified when neural machine translation (NMT) models are optimized for speed and evaluated on generic test sets using BLEU? We investigate architectures and techniques co…
Predicting Directionality in Causal Relations in Text
Pedram Hosseini, David A. Broniatowski, Mona Diab
In this work, we test the performance of two bidirectional transformer-based language models, BERT and SpanBERT, on predicting directionality in causal pairs in the textual content…