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Found 15 papers, 8 papers with code
Modeling Persuasive Discourse to Adaptively Support Students’ Argumentative Writing
We introduce an argumentation annotation approach to model the structure of argumentative discourse in student-written business model pitches.
Shallow Discourse Parsing for Open Information Extraction and Text Simplification
We present a discourse-aware text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences.
Towards LLM-based Autograding for Short Textual Answers
Thus, in this manuscript we provide an evaluation of a large language model for the purpose of autograding, while also highlighting how LLMs can support educators in validating their grading procedures.
Discourse-Aware Text Simplification: From Complex Sentences to Linked Propositions
In that way, we generate a semantic hierarchy of minimal propositions that leads to a novel representation of complex assertions that puts a semantic layer on top of the simplified sentences.
Supporting Cognitive and Emotional Empathic Writing of Students
We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components.
Context-Preserving Text Simplification
We present a context-preserving text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences.
A Corpus for Argumentative Writing Support in German
In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language.
DisSim: A Discourse-Aware Syntactic Text Simplification Framework for English and German
We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications.
DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor English and German
We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications.
MinWikiSplit: A Sentence Splitting Corpus with Minimal Propositions
We compiled a new sentence splitting corpus that is composed of 203K pairs of aligned complex source and simplified target sentences.
Transforming Complex Sentences into a Semantic Hierarchy
We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE).
Graphene: A Context-Preserving Open Information Extraction System
In that way, we preserve the context of the relational tuples extracted from a source sentence, generating a novel lightweight semantic representation for Open IE that enhances the expressiveness of the extracted propositions.
Graphene: Semantically-Linked Propositions in Open Information Extraction
We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification.
A Survey on Open Information Extraction
We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction.
A Sentence Simplification System for Improving Relation Extraction
In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems.
