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Found 30 papers, 10 papers with code
Modeling Mathematical Notation Semantics in Academic Papers
Natural language models often fall short when understanding and generating mathematical notation.
Automatically Generating Cause-and-Effect Questions from Passages
Automated question generation has the potential to greatly aid in education applications, such as online study aids to check understanding of readings.
Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in Automatic Related Work Composition
Numerous AI-assisted scholarly applications have been developed to aid different stages of the research process.
Beyond the Chat: Executable and Verifiable Text-Editing with LLMs
Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing.
Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction
We also introduce a new definition extraction method that masks mathematical symbols, creates a copy of each sentence for each symbol, specifies a target symbol, and predicts its corresponding definition spans using slot filling.
Pragmatically Appropriate Diversity for Dialogue Evaluation
To remedy this, we propose the notion of Pragmatically Appropriate Diversity, defined as the extent to which a conversation creates and constrains the creation of multiple diverse responses.
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Scholarly publications are key to the transfer of knowledge from scholars to others.
Semantic Diversity in Dialogue with Natural Language Inference
Second, we demonstrate how to iteratively improve the semantic diversity of a sampled set of responses via a new generation procedure called Diversity Threshold Generation, which results in an average 137% increase in NLI Diversity compared to standard generation procedures.
Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing
When seeking information not covered in patient-friendly documents, like medical pamphlets, healthcare consumers may turn to the research literature.
NewsPod: Automatic and Interactive News Podcasts
News podcasts are a popular medium to stay informed and dive deep into news topics.
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level).
Can Transformer Models Measure Coherence In Text: Re-Thinking the Shuffle Test
The Shuffle Test is the most common task to evaluate whether NLP models can measure coherence in text.
Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text
This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity.
Can Transformer Models Measure Coherence In Text? Re-Thinking the Shuffle Test
The Shuffle Test is the most common task to evaluate whether NLP models can measure coherence in text.
What's The Latest? A Question-driven News Chatbot
This work describes an automatic news chatbot that draws content from a diverse set of news articles and creates conversations with a user about the news.
News Headline Grouping as a Challenging NLU Task
Recent progress in Natural Language Understanding (NLU) has seen the latest models outperform human performance on many standard tasks.
The Summary Loop: Learning to Write Abstractive Summaries Without Examples
This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint.
Ranked #51 on
Abstractive Text Summarization
on CNN / Daily Mail
Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions
Based on this analysis, we develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark.
Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols
We introduce ScholarPhi, an augmented reading interface with four novel features: (1) tooltips that surface position-sensitive definitions from elsewhere in a paper, (2) a filter over the paper that "declutters" it to reveal how the term or symbol is used across the paper, (3) automatic equation diagrams that expose multiple definitions in parallel, and (4) an automatically generated glossary of important terms and symbols.
CIMA: A Large Open Access Dialogue Dataset for Tutoring
To remedy this, we propose a novel asynchronous method for collecting tutoring dialogue via crowdworkers that is both amenable to the needs of deep learning algorithms and reflective of pedagogical concerns.
More Diverse Dialogue Datasets via Diversity-Informed Data Collection
Automated generation of conversational dialogue using modern neural architectures has made notable advances.
SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions.
Towards augmenting crisis counselor training by improving message retrieval
A fundamental challenge when training counselors is presenting novices with the opportunity to practice counseling distressed individuals without exacerbating a situation.
Multiple Choice Question Generation Utilizing An Ontology
An in-depth analysis of the teachers{'} comments yields useful insights for any researcher working on automated question generation for educational applications.
Detecting Figures and Part Labels in Patents: Competition-Based Development of Image Processing Algorithms
We report the findings of a month-long online competition in which participants developed algorithms for augmenting the digital version of patent documents published by the United States Patent and Trademark Office (USPTO).
