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Found 11 papers, 8 papers with code
Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets
Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19.
IITKGP at W-NUT 2020 Shared Task-1: Domain specific BERT representation for Named Entity Recognition of lab protocol
Supervised models trained to predict properties from representations have been achieving high accuracy on a variety of tasks. For in-stance, the BERT family seems to work exceptionally well on the downstream task from NER tagging to the range of other linguistictasks.
Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale
Rapid advancements in GPU computational power has outpaced memory capacity and bandwidth growth, creating bottlenecks in Large Language Model (LLM) inference.
LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression
Low Rank Decomposition of matrix - splitting a large matrix into a product of two smaller matrix offers a means for compression that reduces the parameters of a model without sparsification, and hence delivering more speedup on modern hardware.
Efficient Encoders for Streaming Sequence Tagging
A naive application of state-of-the-art bidirectional encoders for streaming sequence tagging would require encoding each token from scratch for each new token in an incremental streaming input (like transcribed speech).
What do tokens know about their characters and how do they know it?
Pre-trained language models (PLMs) that use subword tokenization schemes can succeed at a variety of language tasks that require character-level information, despite lacking explicit access to the character composition of tokens.
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other.
KGP at SemEval-2021 Task 8: Leveraging Multi-Staged Language Models for Extracting Measurements, their Attributes and Relations
SemEval-2021 Task 8: MeasEval aims at improving the machine understanding of measurements in scientific texts through a set of entity and semantic relation extraction sub-tasks on identifying quantity spans along with various attributes and relationships.
tWT--WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets
The stance detection task aims at detecting the stance of a tweet or a text for a target.
Domain specific BERT representation for Named Entity Recognition of lab protocol
Supervised models trained to predict properties from representations have been achieving high accuracy on a variety of tasks.
Ranked #2 on
Named Entity Recognition (NER)
on WNUT 2020
Leveraging Event Specific and Chunk Span features to Extract COVID Events from tweets
Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19.
