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Found 27 papers, 8 papers with code
“Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays
Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task.
“What makes a question inquisitive?” A Study on Type-Controlled Inquisitive Question Generation
We propose a type-controlled framework for inquisitive question generation.
Controlled Language Generation for Language Learning Items
This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items.
AGReE: A system for generating Automated Grammar Reading Exercises
We describe the AGReE system, which takes user-submitted passages as input and automatically generates grammar practice exercises that can be completed while reading.
FLUTE: Figurative Language Understanding through Textual Explanations
Figurative language understanding has been recently framed as a recognizing textual entailment (RTE) task (a. k. a.
"What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation
We propose a type-controlled framework for inquisitive question generation.
Figurative Language in Recognizing Textual Entailment
We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language.
``Laughing at you or with you'': The Role of Sarcasm in Shaping the Disagreement Space
We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e. g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures).
"Sharks are not the threat humans are": Argument Component Segmentation in School Student Essays
Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task.
"Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space
We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e. g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures).
R\^3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence.
An Exploratory Study of Argumentative Writing by Young Students: A Transformer-based Approach
We present a computational exploration of argument critique writing by young students.
Exploring Recurrent, Memory and Attention Based Architectures for Scoring Interactional Aspects of Human-Machine Text Dialog
An important step towards enabling English language learners to improve their conversational speaking proficiency involves automated scoring of multiple aspects of interactional competence and subsequent targeted feedback.
A Report on the 2020 Sarcasm Detection Shared Task
Detecting sarcasm and verbal irony is critical for understanding people's actual sentiments and beliefs.
$R^3$: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence.
Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic Incongruity
Human communication often involves the use of verbal irony or sarcasm, where the speakers usually mean the opposite of what they say.
Sarcasm Analysis using Conversation Context
To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn.
ChangeMyView Through Concessions: Do Concessions Increase Persuasion?
Drawing from a theoretically-informed typology of concessions, we conduct an annotation task to label a set of polysemous lexical markers as introducing an argumentative concession or not and we observe their distribution in threads that achieved and did not achieve persuasion.
"With 1 follower I must be AWESOME :P". Exploring the role of irony markers in irony recognition
Conversations in social media often contain the use of irony or sarcasm, when the users say the opposite of what they really mean.
The Role of Conversation Context for Sarcasm Detection in Online Interactions
To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the sarcastic response.
