Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment

By Joaquin Quiñonero Candela, Magnini B Dagan I, F. Lauria. 2006

253 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (2018, methodology): “We combine the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al.”
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension (2019, methodology): “Table 2 compares the performance of BART with several recent approaches on the well-studied SQuAD and GLUE tasks (Warstadt et al., 2018; Socher et al., 2013; Dolan & Brockett, 2005; Agirre et al., 2007; Williams et al., 2018; Dagan et al., 2006; Levesque et al., 2011).”
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (2019, methodology): “RTE is included in GLUE, and we use the same data and format as GLUE: We merge data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009).”
Reasoning about Entailment with Neural Attention (2015, background): “This task is important since many natural language processing (NLP) problems, such as information extraction, relation extraction, text summarization or machine translation, rely on it explicitly or implicitly and could benefit from more accurate RTE systems (Dagan et al., 2006).”
A Fast Unified Model for Parsing and Sentence Understanding (2016, methodology): “We evaluate SPINN on the task of natural language inference (NLI, a.k.a. recognizing textual entailment, or RTE; Dagan et al., 2006).”