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Found 38 papers, 19 papers with code
proScript: Partially Ordered Scripts Generation
Scripts – prototypical event sequences describing everyday activities – have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has emerged as a promising direction to amend this.
Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions
However, they are not universally true -- while sparrows and penguins are both birds, only sparrows can fly and penguins cannot.
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
We present ACCoRD, an end-to-end system tackling the novel task of generating sets of descriptions of scientific concepts.
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.
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification
Constructing benchmarks that test the abilities of modern natural language understanding models is difficult - pre-trained language models exploit artifacts in benchmarks to achieve human parity, but still fail on adversarial examples and make errors that demonstrate a lack of common sense.
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.
Delphi: Towards Machine Ethics and Norms
We identify four underlying challenges towards machine ethics and norms: (1) an understanding of moral precepts and social norms; (2) the ability to perceive real-world situations visually or by reading natural language descriptions; (3) commonsense reasoning to anticipate the outcome of alternative actions in different contexts; (4) most importantly, the ability to make ethical judgments given the interplay between competing values and their grounding in different contexts (e. g., the right to freedom of expression vs. preventing the spread of fake news).
``I'm Not Mad'': Commonsense Implications of Negation and Contradiction
In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions.
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
Despite recent advances in natural language generation, it remains challenging to control attributes of generated text.
proScript: Partially Ordered Scripts Generation via Pre-trained Language Models
Scripts - standardized event sequences describing typical everyday activities - have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information.
"I'm Not Mad": Commonsense Implications of Negation and Contradiction
In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions.
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
First, we propose a new multitask benchmark, RAINBOW, to promote research on commonsense models that generalize well over multiple tasks and datasets.
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.
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).
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints
While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples.
Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models
In this paper, we present Reflective Decoding, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks.
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs
Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights.
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.
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning
Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future.
Paragraph-level Commonsense Transformers with Recurrent Memory
Human understanding of narrative texts requires making commonsense inferences beyond what is stated explicitly in the text.
Generative Data Augmentation for Commonsense Reasoning
Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance.
Ranked #1 on
Question Answering
on CODAH
VisualCOMET: Reasoning about the Dynamic Context of a Still Image
In addition, we provide person-grounding (i. e., co-reference links) between people appearing in the image and people mentioned in the textual commonsense descriptions, allowing for tighter integration between images and text.
Unsupervised Commonsense Question Answering with Self-Talk
Natural language understanding involves reading between the lines with implicit background knowledge.
Adversarial Filters of Dataset Biases
Large neural models have demonstrated human-level performance on language and vision benchmarks, while their performance degrades considerably on adversarial or out-of-distribution samples.
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning.
Ranked #1 on
Text Generation
on CommonGen
(CIDEr metric)
Commonsense Knowledge Base Completion with Structural and Semantic Context
Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1. 5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency.
Counterfactual Story Reasoning and Generation
Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes.
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
In this paper, we introduce Cosmos QA, a large-scale dataset of 35, 600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions.
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations.
Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer Learning
Claims are a fundamental unit of scientific discourse.
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.
Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context
Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies.
Construction of the Literature Graph in Semantic Scholar
We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery.
Content-Based Citation Recommendation
We present a content-based method for recommending citations in an academic paper draft.
Semi-supervised sequence tagging with bidirectional language models
Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.
Ranked #44 on
Named Entity Recognition
on CoNLL 2003 (English)
