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Found 8 papers, 0 papers with code
DTAFA: Decoupled Training Architecture for Efficient FAQ Retrieval
Automated Frequently Asked Question (FAQ) retrieval provides an effective procedure to provide prompt responses to natural language based queries, providing an efficient platform for large-scale service-providing companies for presenting readily available information pertaining to customers’ questions.
Multi-Stage Framework with Refinement Based Point Set Registration for Unsupervised Bi-Lingual Word Alignment
Cross-lingual alignment of word embeddings are important in knowledge transfer across languages, for improving machine translation and other multi-lingual applications.
Cross-lingual Sentence Embedding using Multi-Task Learning
We demonstrate the efficacy of an unsupervised as well as a weakly supervised variant of our framework on STS, BUCC and Tatoeba benchmark tasks.
Aligned Weight Regularizers for Pruning Pretrained Neural Networks
While various avenues of research have been explored for iterative pruning, little is known what effect pruning has on zero-shot test performance and its potential implications on the choice of pruning criteria.
Deep Neural Compression Via Concurrent Pruning and Self-Distillation
Pruning aims to reduce the number of parameters while maintaining performance close to the original network.
Self-Distilled Pruning Of Neural Networks
Pruning aims to reduce the number of parameters while maintaining performance close to the original network.
Sequence-to-Sequence Learning on Keywords for Efficient FAQ Retrieval
Frequently-Asked-Question (FAQ) retrieval provides an effective procedure for responding to user's natural language based queries.
Unsupervised Word Translation Pairing using Refinement based Point Set Registration
Cross-lingual alignment of word embeddings play an important role in knowledge transfer across languages, for improving machine translation and other multi-lingual applications.
