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Found 9 papers, 7 papers with code
SummScreen: A Dataset for Abstractive Screenplay Summarization
Since characters are fundamental to TV series, we also propose two entity-centric evaluation metrics.
Unsupervised Label Refinement Improves Dataless Text Classification
This reliance causes dataless classifiers to be highly sensitive to the choice of label descriptions and hinders the broader application of dataless classification in practice.
Ranked #3 on
Zero-Shot Text Classification
on AG News
Mining Knowledge for Natural Language Inference from Wikipedia Categories
Accurate lexical entailment (LE) and natural language inference (NLI) often require large quantities of costly annotations.
NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources
We describe NatCat, a large-scale resource for text classification constructed from three data sources: Wikipedia, Stack Exchange, and Reddit.
How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions
We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one.
EntEval: A Holistic Evaluation Benchmark for Entity Representations
Rich entity representations are useful for a wide class of problems involving entities.
Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations
Prior work on pretrained sentence embeddings and benchmarks focus on the capabilities of stand-alone sentences.
PoMo: Generating Entity-Specific Post-Modifiers in Context
Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity.
Broad Context Language Modeling as Reading Comprehension
Progress in text understanding has been driven by large datasets that test particular capabilities, like recent datasets for reading comprehension (Hermann et al., 2015).
Ranked #32 on
Language Modelling
on LAMBADA
