Search Results for author:
Found 19 papers, 5 papers with code
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts
In NLP tasks like machine translation and pre-trained language modeling, there is a significant performance gap between supernet and training from scratch for the same model architecture, necessitating retraining post optimal architecture identification.
Binary and Ternary Natural Language Generation
For machine translation, we achieved BLEU scores of 21. 7 and 17. 6 on the WMT16 En-Ro benchmark, compared with a full precision mBART model score of 26. 8.
BiT: Robustly Binarized Multi-distilled Transformer
Modern pre-trained transformers have rapidly advanced the state-of-the-art in machine learning, but have also grown in parameters and computational complexity, making them increasingly difficult to deploy in resource-constrained environments.
Current Challenges and Future Directions in Podcast Information Access
Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and creators.
TREC 2020 Podcasts Track Overview
The Podcast Track is new at the Text Retrieval Conference (TREC) in 2020.
Detecting Extraneous Content in Podcasts
Podcast episodes often contain material extraneous to the main content, such as advertisements, interleaved within the audio and the written descriptions.
100,000 Podcasts: A Spoken English Document Corpus
Paired with the audio files, they are also a resource for speech processing and the study of paralinguistic, sociolinguistic, and acoustic aspects of the domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Unsupervised Neologism Normalization Using Embedding Space Mapping
This paper presents an approach for detecting and normalizing neologisms in social media content.
On the Complexity of Opinions and Online Discussions
Sports, and the Newsroom app suggest that unidimensional opinion models may often be unable to accurately represent online discussions, provide insights into human judgements and opinions, and show that our framework is able to circumvent language nuances such as sarcasm or humor by relying on human judgements instead of textual analysis.
Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches
In Multilabel Learning (MLL) each training instance is associated with a set of labels and the task is to learn a function that maps an unseen instance to its corresponding label set.
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work.
Finding Good Conversations Online: The Yahoo News Annotated Comments Corpus
This work presents a dataset and annotation scheme for the new task of identifying {``}good{''} conversations that occur online, which we call ERICs: Engaging, Respectful, and/or Informative Conversations.
Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest
The New Yorker publishes a weekly captionless cartoon.
