The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
Year: 2017
Type: conference-paper
Abstract: This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017).All of the five participating teams beat the bidirectional LSTM (BiLSTM) and continuous bag of words baselines reported in Williams et al..The best single model used stacked BiLSTMs with residual connections to extract sentence features and reached 74.5% accuracy on the genre-matched test set.Surprisingly, the results of the competition were fairly consistent across the genrematched and genre-mismatched test sets, and across subsets of the test data representing a variety of linguistic phenomena, suggesting that all of the submitted systems learned reasonably domainindependent representations for sentence meaning.
DOI: 10.18653/v1/w17-5301
Institutions: New York University, University of Trento
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Tencent
OpenAlex ID: W2737504179