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Found 13 papers, 7 papers with code
Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL-SciSumm, LaySumm and LongSumm
We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm.
Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?
Privacy plays a crucial role in preserving democratic ideals and personal autonomy.
CURIE: An Iterative Querying Approach for Reasoning About Situations
Recently, models have been shown to predict the effects of unexpected situations, e. g., would cloudy skies help or hinder plant growth?
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering
When Question-Answering (QA) systems are deployed in the real world, users query them through a variety of interfaces, such as speaking to voice assistants, typing questions into a search engine, or even translating questions to languages supported by the QA system.
Measuring and Improving Consistency in Pretrained Language Models
In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect to factual knowledge?
On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT
In particular, we demonstrate through a simple consistency probe that the ability to correctly retrieve hypernyms in cloze tasks, as used in prior work, does not correspond to systematic knowledge in BERT.
EIGEN: Event Influence GENeration using Pre-trained Language Models
Reasoning about events and tracking their influences is fundamental to understanding processes.
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?
Although neural models have achieved impressive results on several NLP benchmarks, little is understood about the mechanisms they use to perform language tasks.
Question Answering for Privacy Policies: Combining Computational and Legal Perspectives
The PrivacyQA corpus offers a challenging corpus for question answering, with genuine real-world utility.
EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference
Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle.
Stress Test Evaluation for Natural Language Inference
Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner.
Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models
In this work, we focus on improving learning for such hierarchical models and demonstrate our method on the task of speaker trait prediction.
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations
We investigate the pertinence of methods from algebraic topology for text data analysis.
