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Found 18 papers, 5 papers with code
Human Rationales as Attribution Priors for Explainable Stance Detection
As NLP systems become better at detecting opinions and beliefs from text, it is important to ensure not only that models are accurate but also that they arrive at their predictions in ways that align with human reasoning.
Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language
Counterspeech, i. e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship.
Towards Countering Essentialism through Social Bias Reasoning
Essentialist beliefs (i. e., believing that members of the same group are fundamentally alike) play a central role in social stereotypes and can lead to harm when left unchallenged.
Legal and Political Stance Detection of SCOTUS Language
As a natural extension of this political stance detection, we propose the more specialized task of legal stance detection with our new dataset SC-stance, which matches written opinions to legal questions.
SafeText: A Benchmark for Exploring Physical Safety in Language Models
Understanding what constitutes safe text is an important issue in natural language processing and can often prevent the deployment of models deemed harmful and unsafe.
Mitigating Covertly Unsafe Text within Natural Language Systems
An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences.
Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions
Generics express generalizations about the world (e. g., birds can fly) that are not universally true (e. g., newborn birds and penguins cannot fly).
Seeded Hierarchical Clustering for Expert-Crafted Taxonomies
Practitioners from many disciplines (e. g., political science) use expert-crafted taxonomies to make sense of large, unlabeled corpora.
Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic
As natural language processing systems become more widespread, it is necessary to address fairness issues in their implementation and deployment to ensure that their negative impacts on society are understood and minimized.
Adversarial Learning for Zero-Shot Stance Detection on Social Media
Stance detection on social media can help to identify and understand slanted news or commentary in everyday life.
Sequential Cross-Document Coreference Resolution
Relating entities and events in text is a key component of natural language understanding.
coreference-resolution
Cross Document Coreference Resolution
+1
Does Putting a Linguist in the Loop Improve NLU Data Collection?
We take natural language inference as a test case and ask whether it is beneficial to put a linguist `in the loop' during data collection to dynamically identify and address gaps in the data by introducing novel constraints on the task.
Event Guided Denoising for Multilingual Relation Learning
General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks.
Event-Guided Denoising for Multilingual Relation Learning
General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks.
Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations
Stance detection is an important component of understanding hidden influences in everyday life.
A Unified Feature Representation for Lexical Connotations
Ideological attitudes and stance are often expressed through subtle meanings of words and phrases.
ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge.
Event2Mind: Commonsense Inference on Events, Intents, and Reactions
We investigate a new commonsense inference task: given an event described in a short free-form text ("X drinks coffee in the morning"), a system reasons about the likely intents ("X wants to stay awake") and reactions ("X feels alert") of the event's participants.
Ranked #1 on
Common Sense Reasoning
on Event2Mind test
