1 citations · 3 across the 20 of their papers we have counts for
7 papers · 2 filters
MALAMUTE: A Multilingual, Highly-granular, Template-free, Education-based Probing Dataset
Sagi Shaier, George Arthur Baker, Chiranthan Sridhar +2
Language models (LMs) have excelled in various broad domains. However, to ensure their safe and effective integration into real-world educational settings, they must demonstrate pr…
From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation
Ali Marashian, Enora Rice, Luke Gessler +2
Many of the world's languages have insufficient data to train high-performing general neural machine translation (NMT) models, let alone domain-specific models, and often the only…
The Trade-off between Performance, Efficiency, and Fairness in Adapter Modules for Text Classification
Minh Duc Bui, Katharina von der Wense
Current natural language processing (NLP) research tends to focus on only one or, less frequently, two dimensions - e.g., performance, privacy, fairness, or efficiency - at a time,…
Knowledge Distillation vs. Pretraining from Scratch under a Fixed (Computation) Budget
Minh Duc Bui, Fabian David Schmidt, Goran Glavaš +1
Compared to standard language model (LM) pretraining (i.e., from scratch), Knowledge Distillation (KD) entails an additional forward pass through a teacher model that is typically…
TAMS: Translation-Assisted Morphological Segmentation
Enora Rice, Ali Marashian, Luke Gessler +2
Canonical morphological segmentation is the process of analyzing words into the standard (aka underlying) forms of their constituent morphemes. This is a core task in language docu…
Comparing Template-based and Template-free Language Model Probing
Sagi Shaier, Kevin Bennett, Lawrence E Hunter +1
The differences between cloze-task language model (LM) probing with 1) expert-made templates and 2) naturally-occurring text have often been overlooked. Here, we evaluate 16 differ…
Desiderata for the Context Use of Question Answering Systems
Sagi Shaier, Lawrence E Hunter, Katharina von der Wense
Prior work has uncovered a set of common problems in state-of-the-art context-based question answering (QA) systems: a lack of attention to the context when the latter conflicts wi…