1 |
What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Cross-Lingual Cross-Domain Nested Named Entity Evaluation on English Web Texts ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
DaN+: Danish Nested Named Entities and Lexical Normalization ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
SemEval-2021 Task 12: Learning with Disagreements
|
|
|
|
Abstract:
Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.
|
|
URL: http://repository.essex.ac.uk/31851/1/2021.semeval-1.41.pdf http://repository.essex.ac.uk/31851/ https://doi.org/10.18653/v1/2021.semeval-1.41
|
|
BASE
|
|
Hide details
|
|
19 |
Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|