2 |
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Monotonic Simultaneous Translation with Chunk-wise Reordering and Refinement ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
DEEP: DEnoising Entity Pre-training for Neural Machine Translation ...
|
|
|
|
Abstract:
It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. Earlier named entity translation methods mainly focus on phonetic transliteration, which ignores the sentence context for translation and is limited in domain and language coverage. To address this limitation, we propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences. Besides, we investigate a multi-task learning strategy that finetunes a pre-trained neural machine translation model on both entity-augmented monolingual data and parallel data to further improve entity translation. Experimental results on three language pairs demonstrate that \method results in significant improvements over strong denoising auto-encoding baselines, with a gain of up to 1.3 BLEU and up to 9.2 entity accuracy points for English-Russian translation. ... : 13 pages ...
|
|
Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://dx.doi.org/10.48550/arxiv.2111.07393 https://arxiv.org/abs/2111.07393
|
|
BASE
|
|
Hide details
|
|
8 |
VisualSem: A High-quality Knowledge Graph for Vision and Language ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Learning to Learn Morphological Inflection for Resource-Poor Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
AdapterHub: A Framework for Adapting Transformers
|
|
Pfeiffer, Jonas; Ruckle, Andreas; Poth, Clifton. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP 2020), 2020
|
|
BASE
|
|
Show details
|
|
14 |
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Emergent Linguistic Phenomena in Multi-Agent Communication Games ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Insertion-based Decoding with Automatically Inferred Generation Order
|
|
|
|
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 661-676 (2019) (2019)
|
|
BASE
|
|
Show details
|
|
18 |
Meta-Learning for Low-Resource Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
From Characters to Understanding Natural Language (C2NLU): Robust End-to-End Deep Learning for NLP (Dagstuhl Seminar 17042)
|
|
|
|
BASE
|
|
Show details
|
|
|
|