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Found 17 papers, 13 papers with code
What's under the hood: Investigating Automatic Metrics on Meeting Summarization
Meeting summarization has become a critical task considering the increase in online interactions.
MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions
MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models.
Citation Amnesia: NLP and Other Academic Fields Are in a Citation Age Recession
This study examines the tendency to cite older work across 20 fields of study over 43 years (1980--2023).
Text-Guided Image Clustering
We, therefore, propose Text-Guided Image Clustering, i. e., generating text using image captioning and visual question-answering (VQA) models and subsequently clustering the generated text.
We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields
We analyzed ~77k NLP papers, ~3. 1m citations from NLP papers to other papers, and ~1. 8m citations from other papers to NLP papers.
Paraphrase Types for Generation and Detection
Current approaches in paraphrase generation and detection heavily rely on a single general similarity score, ignoring the intricate linguistic properties of language.
The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research
Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development.
Paraphrase Detection: Human vs. Machine Content
Additionally, we identify four datasets as the most diverse and challenging for paraphrase detection.
A Cohesive Distillation Architecture for Neural Language Models
We developed two methods to test our hypothesis that efficient architectures can gain knowledge from LMs and extract valuable information from lexical sources.
Analyzing Multi-Task Learning for Abstractive Text Summarization
Further, we find that choice and combinations of task families influence downstream performance more than the training scheme, supporting the use of task families for abstractive text summarization.
CS-Insights: A System for Analyzing Computer Science Research
This paper presents CS-Insights, an interactive web application to analyze computer science publications from DBLP through multiple perspectives.
How Large Language Models are Transforming Machine-Paraphrased Plagiarism
The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work.
D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research
We present an initial analysis focused on the volume of computer science research (e. g., number of papers, authors, research activity), trends in topics of interest, and citation patterns.
Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection
We expect that evaluating a broad spectrum of datasets and models will benefit future research in developing misinformation detection systems.
Incorporating Word Sense Disambiguation in Neural Language Models
We present two supervised (pre-)training methods to incorporate gloss definitions from lexical resources into neural language models (LMs).
Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection
The rise of language models such as BERT allows for high-quality text paraphrasing.
Identifying Machine-Paraphrased Plagiarism
Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity.
