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Found 11 papers, 6 papers with code
Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction
The goal of open information extraction (OIE) is to extract facts from natural language text, and to represent them as structured triples of the form {\textless}subject, predicate, object{\textgreater}.
Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns
Bias-measuring datasets play a critical role in detecting biased behavior of language models and in evaluating progress of bias mitigation methods.
The Defeat of the Winograd Schema Challenge
The Winograd Schema Challenge - a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge - was proposed by Hector Levesque in 2011.
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup
A potential solution is the few-shot out-of-domain transfer of NLEs from a parent task with many NLEs to a child task.
Knowledge Base Completion Meets Transfer Learning
The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases.
The Gap on GAP: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets
For example, if the feminine subset of a gender-bias-measuring coreference resolution dataset contains sentences with a longer average distance between the pronoun and the correct candidate, an RNN-based model may perform worse on this subset due to long-term dependencies.
Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution
Overall, four categories of training and evaluation objectives have been introduced.
A Review of Winograd Schema Challenge Datasets and Approaches
The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test.
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
We use a language-model-based approach for pronoun resolution in combination with our WikiCREM dataset.
A Surprisingly Robust Trick for the Winograd Schema Challenge
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning.
A Surprisingly Robust Trick for Winograd Schema Challenge
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning.
