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Found 9 papers, 0 papers with code
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.
Performance of BERT on Persuasion for Good
We consider the task of automatically classifying the persuasion strategy employed by an utterance in a dialog.
Improving NMT via Filtered Back Translation
This is further followed by fine-tuning on the domain-specific corpus.
Acceleron: A Tool to Accelerate Research Ideation
To aid with research ideation, we propose `Acceleron', a research accelerator for different phases of the research life cycle, and which is specially designed to aid the ideation process.
Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting
Cross-domain and cross-compositional generalization of Text-to-SQL semantic parsing is a challenging task.
Neuro-symbolic Zero-Shot Code Cloning with Cross-Language Intermediate Representation
We further fine-tune UnixCoder, the best-performing model for zero-shot cross-programming language code search, for the Code Cloning task with the SBT IRs of C code-pairs, available in the CodeNet dataset.
Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language
In this paper, we focus on transferring the knowledge acquired by the code-to-pseudocode neural model trained on a high resource PL (C++) using parallel code-pseudocode data.
Understanding Advertisements with BERT
This achieves an accuracy of 89. 69{\%}, which is an improvement of 4. 7{\%}.
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.
