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Found 20 papers, 14 papers with code
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning
A LERP is designed as a vector of probabilistic logical functions on the entity's neighboring sub-graph.
Logical Reasoning over Natural Language as Knowledge Representation: A Survey
This paper provides a comprehensive overview on a new paradigm of logical reasoning, which uses natural language as knowledge representation~(and pretrained language models as reasoners), including philosophical definition and categorization of logical reasoning, advantages of the new paradigm, benchmarks and methods, challenges of the new paradigm, desirable tasks & methods in the future, and relation to related NLP fields.
Language Models as Inductive Reasoners
To this end, we propose a new task, which is to induce natural language rules from natural language facts, and create a dataset termed DEER containing 1. 2k rule-fact pairs for the task, where rules and facts are written in natural language.
Retrieval-Augmented Generative Question Answering for Event Argument Extraction
We propose a retrieval-augmented generative QA model (R-GQA) for event argument extraction.
Zero-Shot Classification by Logical Reasoning on Natural Language Explanations
To this end, we propose the framework CLORE (Classification by LOgical Reasoning on Explanations).
Dynamic Global Memory for Document-level Argument Extraction
Extracting informative arguments of events from news articles is a challenging problem in information extraction, which requires a global contextual understanding of each document.
Automatic Error Analysis for Document-level Information Extraction
Document-level information extraction (IE) tasks have recently begun to be revisited in earnest using the end-to-end neural network techniques that have been successful on their sentence-level IE counterparts.
QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining
To address this problem, we introduce QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model.
Template Filling with Generative Transformers
Template filling is generally tackled by a pipeline of two separate supervised systems {--} one for role-filler extraction and another for template/event recognition.
Few-shot Intent Classification and Slot Filling with Retrieved Examples
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain.
Leveraging Structured Metadata for Improving Question Answering on the Web
We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking.
Improving Event Duration Prediction via Time-aware Pre-training
End-to-end models in NLP rarely encode external world knowledge about length of time.
GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction
We revisit the classic problem of document-level role-filler entity extraction (REE) for template filling.
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding
Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions.
Event Extraction by Answering (Almost) Natural Questions
The problem of event extraction requires detecting the event trigger and extracting its corresponding arguments.
Be Consistent! Improving Procedural Text Comprehension using Label Consistency
Our goal is procedural text comprehension, namely tracking how the properties of entities (e. g., their location) change with time given a procedural text (e. g., a paragraph about photosynthesis, a recipe).
Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia
We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence.
Identifying Where to Focus in Reading Comprehension for Neural Question Generation
A first step in the task of automatically generating questions for testing reading comprehension is to identify \textit{question-worthy} sentences, i. e. sentences in a text passage that humans find it worthwhile to ask questions about.
Learning to Ask: Neural Question Generation for Reading Comprehension
We study automatic question generation for sentences from text passages in reading comprehension.
