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Domain Adaptation for NMT via Filtered Iterative Back-Translation
In comparison, in this work, we observe that a simpler filtering approach based on a domain classifier, applied only to the pseudo-training data can consistently perform better, providing performance gains of 1. 40, 1. 82 and 0. 76 in terms of BLEU score for Medical, Law and IT in one direction, and 1. 28, 1. 60 and 1. 60 in the other direction in low resource scenario over competitive baselines.
Improving NMT via Filtered Back Translation
This is further followed by fine-tuning on the domain-specific corpus.
A Novel Hierarchical BERT Architecture for Sarcasm Detection
Online discussion platforms are often flooded with opinions from users across the world on a variety of topics.
From Monolingual to Multilingual FAQ Assistant using Multilingual Co-training
We observe that the proposed approach provides consistent gains in the performance of BERT for multiple benchmark datasets (e. g. 1. 0{\%} gain on MLDocs, and 1. 2{\%} gain on XNLI over translate-train with BERT), while requiring a single model for multiple languages.
