Search Results for author:
Found 11 papers, 7 papers with code
Frustratingly Easy Performance Improvements for Low-resource Setups: A Tale on BERT and Segment Embeddings
For low-resource syntactic tasks, we observe impacts of segment embedding and multilingual BERT choice.
Can Humans Identify Domains?
Textual domain is a crucial property within the Natural Language Processing (NLP) community due to its effects on downstream model performance.
Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training
We identify critical learning phases across tasks and time, during which subspaces emerge, share information, and later disentangle to specialize.
Establishing Trustworthiness: Rethinking Tasks and Model Evaluation
Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades.
Evidence > Intuition: Transferability Estimation for Encoder Selection
With the increase in availability of large pre-trained language models (LMs) in Natural Language Processing (NLP), it becomes critical to assess their fit for a specific target task a priori - as fine-tuning the entire space of available LMs is computationally prohibitive and unsustainable.
Sort by Structure: Language Model Ranking as Dependency Probing
Making an informed choice of pre-trained language model (LM) is critical for performance, yet environmentally costly, and as such widely underexplored.
Experimental Standards for Deep Learning in Natural Language Processing Research
The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well.
Probing for Labeled Dependency Trees
Probing has become an important tool for analyzing representations in Natural Language Processing (NLP).
How Universal is Genre in Universal Dependencies?
This work provides the first in-depth analysis of genre in Universal Dependencies (UD).
Genre as Weak Supervision for Cross-lingual Dependency Parsing
Recent work has shown that monolingual masked language models learn to represent data-driven notions of language variation which can be used for domain-targeted training data selection.
