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Found 16 papers, 9 papers with code
Think about it! Improving defeasible reasoning by first modeling the question scenario.
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
Conditional set generation using Seq2seq models
Conditional set generation learns a mapping from an input sequence of tokens to a set.
Memory-assisted prompt editing to improve GPT-3 after deployment
Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans.
Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback
Our goal is for an LM to continue to improve after deployment, without retraining, using feedback from the user.
Interscript: A dataset for interactive learning of scripts through error feedback
We present a new dataset, Interscript, containing user feedback on a deployed model that generates complex everyday tasks.
Think about it! Improving defeasible reasoning by first modeling the question scenario
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
Could you give me a hint? Generating inference graphs for defeasible reasoning
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
Improving Neural Model Performance through Natural Language Feedback on Their Explanations
A class of explainable NLP models for reasoning tasks support their decisions by generating free-form or structured explanations, but what happens when these supporting structures contain errors?
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?
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Data-to-Text Generation
on WebNLG ru
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
EIGEN: Event Influence GENeration using Pre-trained Language Models
Reasoning about events and tracking their influences is fundamental to understanding processes.
Neural Language Modeling for Contextualized Temporal Graph Generation
We address this challenge by using existing IE/NLP tools to automatically generate a large quantity (89, 000) of system-produced document-graph pairs, and propose a novel formulation of the contextualized graph generation problem as a sequence-to-sequence mapping task.
Politeness Transfer: A Tag and Generate Approach
This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning.
Practical Comparable Data Collection for Low-Resource Languages via Images
We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators.
Occurrence Statistics of Entities, Relations and Types on the Web
This is owing to the severe mismatch in the distributions of such entities on the web and in the relatively diminutive training data.
