Search Results for author:
Found 21 papers, 7 papers with code
Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification
Emotion Classification is the task of automatically associating a text with a human emotion.
TextMachina: Seamless Generation of Machine-Generated Text Datasets
Recent advancements in Large Language Models (LLMs) have led to high-quality Machine-Generated Text (MGT), giving rise to countless new use cases and applications.
Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains
This paper presents the overview of the AuTexTification shared task as part of the IberLEF 2023 Workshop in Iberian Languages Evaluation Forum, within the framework of the SEPLN 2023 conference.
Programming by Example and Text-to-Code Translation for Conversational Code Generation
We propose Modular Programs for Text-guided Hierarchical Synthesis (MPaTHS), a method for integrating Programming by Example and text-to-code systems which offers an accessible natural language interface for synthesizing general programs.
Zero and Few-shot Learning for Author Profiling
We find that entailment-based models out-perform supervised text classifiers based on roberta-XLM and that we can reach 80% of the accuracy of previous approaches using less than 50\% of the training data on average.
Unsupervised Ranking and Aggregation of Label Descriptions for Zero-Shot Classifiers
Zero-shot text classifiers based on label descriptions embed an input text and a set of labels into the same space: measures such as cosine similarity can then be used to select the most similar label description to the input text as the predicted label.
Active Few-Shot Learning with FASL
Recent advances in natural language processing (NLP) have led to strong text classification models for many tasks.
Few-Shot Learning with Siamese Networks and Label Tuning
We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification.
What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts
According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people{'}s everyday life and career.
Five Psycholinguistic Characteristics for Better Interaction with Users
When two people pay attention to each other and are interested in what the other has to say or write, they almost instantly adapt their writing/speaking style to match the other.
Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning
Experimental results show that Aspect On dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model.
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations
In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot.
Ranked #2 on
Emotion Recognition in Conversation
on EC
UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering
In this work we describe the system built for the three English subtasks of the SemEval 2016 Task 3 by the Department of Computer Science of the University of Houston (UH) and the Pattern Recognition and Human Language Technology (PRHLT) research center - Universitat Polit`ecnica de Val`encia: UH-PRHLT.
Semantically-informed distance and similarity measures for paraphrase plagiarism identification
Paraphrase plagiarism identification represents a very complex task given that plagiarized texts are intentionally modified through several rewording techniques.
A Resource-Light Method for Cross-Lingual Semantic Textual Similarity
In contrast, we propose an unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages.
Cross-Lingual Information Retrieval
Cross-Lingual Semantic Textual Similarity
+9
Sentence Alignment Methods for Improving Text Simplification Systems
We provide several methods for sentence-alignment of texts with different complexity levels.
A Low Dimensionality Representation for Language Variety Identification
We compare our LDR method with common state-of-the-art representations and show an increase in accuracy of ~35%.
Single and Cross-domain Polarity Classification using String Kernels
That setting usually implies the use of a domain adaptation method.
