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Found 23 papers, 11 papers with code
Collecting Verified COVID-19 Question Answer Pairs
We release a dataset of over 2, 100 COVID19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites.
An Immersive Computational Text Analysis Course for Non-Computer Science Students at Barnard College
We provide an overview of a new Computational Text Analysis course that will be taught at Barnard College over a six week period in May and June 2021.
On Gender Biases in Offensive Language Classification Models
We explore whether neural Natural Language Processing models trained to identify offensive language in tweets contain gender biases.
Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference
We test whether replacing crowdsource workers with LLMs to write Natural Language Inference (NLI) hypotheses similarly results in annotation artifacts.
Evaluating Paraphrastic Robustness in Textual Entailment Models
We present PaRTE, a collection of 1, 126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate whether models are robust to paraphrasing.
Discovering changes in birthing narratives during COVID-19
We investigate whether, and if so how, birthing narratives written by new parents on Reddit changed during COVID-19.
Figurative Language in Recognizing Textual Entailment
We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language.
Fine-Tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets
We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Mining for Health Applications (SMM4H) shared tasks.
Temporal Reasoning in Natural Language Inference
We introduce five new natural language inference (NLI) datasets focused on temporal reasoning.
A Survey on Recognizing Textual Entailment as an NLP Evaluation
Recognizing Textual Entailment (RTE) was proposed as a unified evaluation framework to compare semantic understanding of different NLP systems.
Probing Neural Language Models for Human Tacit Assumptions
Our prompts are based on human responses in a psychological study of conceptual associations.
Uncertain Natural Language Inference
We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments.
On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases.
Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise.
What do you learn from context? Probing for sentence structure in contextualized word representations
The jiant toolkit for general-purpose text understanding models
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Our results show that pretraining on language modeling performs the best on average across our probing tasks, supporting its widespread use for pretraining state-of-the-art NLP models, and CCG supertagging and NLI pretraining perform comparably.
Hypothesis Only Baselines in Natural Language Inference
We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI).
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference
We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena.
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning.
CADET: Computer Assisted Discovery Extraction and Translation
It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users.
Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles
We study how different frame annotations complement one another when learning continuous lexical semantics.
Efficient, Compositional, Order-sensitive n-gram Embeddings
We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive.
Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis
The popular skip-gram model induces word embeddings by exploiting the signal from word-context coocurrence.
