Search Results for author:
Found 9 papers, 4 papers with code
Sparse Graph Representations for Procedural Instructional Documents
We propose two approaches to model document similarity by representing document pairs as a directed and sparse JCIG that incorporates sequential information.
LEGOBench: Scientific Leaderboard Generation Benchmark
We present four graph-based and two language model-based leaderboard generation task configurations.
Unlocking Model Insights: A Dataset for Automated Model Card Generation
To automate model card generation, we introduce a dataset of 500 question-answer pairs for 25 ML models that cover crucial aspects of the model, such as its training configurations, datasets, biases, architecture details, and training resources.
The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through
Language models are increasingly becoming popular in AI-powered scientific IR systems.
COMPARE: A Taxonomy and Dataset of Comparison Discussions in Peer Reviews
We present COMPARE, a taxonomy and a dataset of comparison discussions in peer reviews of research papers in the domain of experimental deep learning.
TweeNLP: A Twitter Exploration Portal for Natural Language Processing
It supports multiple features such as TweetExplorer to explore tweets by topics, visualize insights from Twitter activity throughout the organization cycle of conferences, discover popular research papers and researchers.
Understanding Attention: In Minds and Machines
Attention is a complex and broad concept, studied across multiple disciplines spanning artificial intelligence, cognitive science, psychology, neuroscience, and related fields.
NLPExplorer: Exploring the Universe of NLP Papers
Also, it provides temporal statistics such as yearwise popularity of topics, datasets, and seminal papers.
Event Representations for Automated Story Generation with Deep Neural Nets
We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence).
