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Found 48 papers, 18 papers with code
K-SNACS: Annotating Korean Adposition Semantics
While many languages use adpositions to encode semantic relationships between content words in a sentence (e. g., agentivity or temporality), the details of how adpositions work vary widely across languages with respect to both form and meaning.
Putting Context in SNACS: A 5-Way Classification of Adpositional Pragmatic Markers
The SNACS framework provides a network of semantic labels called supersenses for annotating adpositional semantics in corpora.
Sprucing up Supersenses: Untangling the Semantic Clusters of Accompaniment and Purpose
We reevaluate an existing adpositional annotation scheme with respect to two thorny semantic domains: accompaniment and purpose.
Tulu 3: Pushing Frontiers in Open Language Model Post-Training
Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag behind proprietary ones.
Diverging Preferences: When do Annotators Disagree and do Models Know?
We find that the majority of disagreements are in opposition with standard reward modeling approaches, which are designed with the assumption that annotator disagreement is noise.
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance
The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models.
CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting
As the utilization of large language models (LLMs) has proliferated world-wide, it is crucial for them to have adequate knowledge and fair representation for diverse global cultures.
Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty
As natural language becomes the default interface for human-AI interaction, there is a need for LMs to appropriately communicate uncertainties in downstream applications.
SPLAIN: Augmenting CybersecurityWarnings with Reasons and Data
Effective cyber threat recognition and prevention demand comprehensible forecasting systems, as prior approaches commonly offer limited and, ultimately, unconvincing information.
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
To instead investigate the ability to model unusual, unexpected, and unlikely situations, we explore the task of uncommonsense abductive reasoning.
The Generative AI Paradox: "What It Can Create, It May Not Understand"
Specifically, we propose and test the Generative AI Paradox hypothesis: generative models, having been trained directly to reproduce expert-like outputs, acquire generative capabilities that are not contingent upon -- and can therefore exceed -- their ability to understand those same types of outputs.
"You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representation
We compare models' analysis of this semantic structure across two settings: 1) direct production of AMR parses based on zero- and few-shot prompts, and 2) indirect partial reconstruction of AMR via metalinguistic natural language queries (e. g., "Identify the primary event of this sentence, and the predicate corresponding to that event.").
Computer Vision Datasets and Models Exhibit Cultural and Linguistic Diversity in Perception
Computer vision often treats human perception as homogeneous: an implicit assumption that visual stimuli are perceived similarly by everyone.
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements
To study the contextual dynamics of offensiveness, we train models to generate COBRA explanations, with and without access to the context.
PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning
We present PlaSma, a novel two-pronged approach to endow small language models with procedural knowledge and (constrained) language planning capabilities.
Faith and Fate: Limits of Transformers on Compositionality
We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures.
Causal schema induction for knowledge discovery
In this work, we investigate how to apply schema induction models to the task of knowledge discovery for enhanced search of English-language news texts.
ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations
Context is everything, even in commonsense moral reasoning.
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation
Here, we investigate an alternative that a priori seems impossible: can smaller language models (e. g., GPT-2) win over models that are orders of magnitude larger and better (e. g., GPT-3), if powered with novel commonsense distillation algorithms?
ComFact: A Benchmark for Linking Contextual Commonsense Knowledge
Understanding rich narratives, such as dialogues and stories, often requires natural language processing systems to access relevant knowledge from commonsense knowledge graphs.
Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest
Large neural networks can now generate jokes, but do they really "understand" humor?
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).
The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning
We present Sherlock, an annotated corpus of 103K images for testing machine capacity for abductive reasoning beyond literal image contents.
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
We apply this to the ATOMIC resource, and share our new symbolic knowledge graph and commonsense models.
Can Machines Learn Morality? The Delphi Experiment
As AI systems become increasingly powerful and pervasive, there are growing concerns about machines' morality or a lack thereof.
Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation
Understanding manipulated media, from automatically generated {`}deepfakes{'} to manually edited ones, raises novel research challenges.
On-the-Fly Attention Modulation for Neural Generation
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: the generated text is repetitive, generic, self-contradictory, and often lacks commonsense.
Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences
In social settings, much of human behavior is governed by unspoken rules of conduct.
Edited Media Understanding: Reasoning About Implications of Manipulated Images
The difference between this example, and harmful edits that spread disinformation, is one of intent.
Social Chemistry 101: Learning to Reason about Social and Moral Norms
We present Social Chemistry, a new conceptual formalism to study people's everyday social norms and moral judgments over a rich spectrum of real life situations described in natural language.
Thinking Like a Skeptic: Defeasible Inference in Natural Language
Defeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is a penguin).
COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs
Next, we show that ATOMIC 2020 is better suited for training knowledge models that can generate accurate, representative knowledge for new, unseen entities and events.
Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean
In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar.
Preparing SNACS for Subjects and Objects
Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens.
Coordinate Structures in Universal Dependencies for Head-final Languages
This paper discusses the representation of coordinate structures in the Universal Dependencies framework for two head-final languages, Japanese and Korean.
Comprehensive Supersense Disambiguation of English Prepositions and Possessives
Semantic relations are often signaled with prepositional or possessive marking--but extreme polysemy bedevils their analysis and automatic interpretation.
Ranked #4 on
Natural Language Understanding
on STREUSLE
(Role F1 (Preps) metric)
Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions
We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4, 250 preposition tokens in a 55, 000 word corpus of English.
Adposition and Case Supersenses v2.6: Guidelines for English
This document offers a detailed linguistic description of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al., 2018), an inventory of 52 semantic labels ("supersenses") that characterize the use of adpositions and case markers at a somewhat coarse level of granularity, as demonstrated in the STREUSLE corpus (https://github. com/nert-nlp/streusle/ ; version 4. 5 tracks guidelines version 2. 6).
Coping with Construals in Broad-Coverage Semantic Annotation of Adpositions
We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4, 250 preposition tokens in a 55, 000 word corpus of English.
A corpus of preposition supersenses in English web reviews
We present the first corpus annotated with preposition supersenses, unlexicalized categories for semantic functions that can be marked by English prepositions (Schneider et al., 2015).
PropBank: Semantics of New Predicate Types
This research focuses on expanding PropBank, a corpus annotated with predicate argument structures, with new predicate types; namely, noun, adjective and complex predicates, such as Light Verb Constructions.
Criteria for Identifying and Annotating Caused Motion Constructions in Corpus Data
While natural language processing performance has been improved through the recognition that there is a relationship between the semantics of the verb and the syntactic context in which the verb is realized, sentences where the verb does not conform to the expected syntax-semantic patterning behavior remain problematic.
