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Found 10 papers, 8 papers with code
Vector search with small radiuses
Although convenient to compute, this metric is distantly related to the end-to-end accuracy of a full system that integrates vector search.
Improving Wikipedia Verifiability with AI
Hence, maintaining and improving the quality of Wikipedia references is an important challenge and there is a pressing need for better tools to assist humans in this effort.
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
Contrastive Predictive Coding (CPC), based on predicting future segments of speech based on past segments is emerging as a powerful algorithm for representation learning of speech signal.
Unsupervised pretraining transfers well across languages
Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been extensively investigated in the supervised setting.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Libri-Light: A Benchmark for ASR with Limited or No Supervision
Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER).
Ranked #1 on
Speech Recognition
on Libri-Light test-other
(ABX-within metric)
Reference-less Quality Estimation of Text Simplification Systems
We show that n-gram-based MT metrics such as BLEU and METEOR correlate the most with human judgment of grammaticality and meaning preservation, whereas simplicity is best evaluated by basic length-based metrics.
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
As our agent engages in conversation, it also estimates user satisfaction in its responses.
Training Millions of Personalized Dialogue Agents
Current dialogue systems are not very engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies.
Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading
This paper aims at improving how machines can answer questions directly from text, with the focus of having models that can answer correctly multiple types of questions and from various types of texts, documents or even from large collections of them.
