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1
Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation ...
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2
CLEVE: Contrastive Pre-training for Event Extraction ...
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3
Rethinking Stealthiness of Backdoor Attack against NLP Models ...
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4
Prevent the Language Model from being Overconfident in Neural Machine Translation ...
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5
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion ...
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6
Modeling Bilingual Conversational Characteristics for Neural Chat Translation ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.444 Abstract: Neural chat translation aims to translate bilingual conversational text, which has a broad application in international exchanges and cooperation. Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency. In this paper, we aim to promote the translation quality of conversational text by modeling the above properties. Specifically, we design three latent variational modules to learn the distributions of bilingual conversational characteristics. Through sampling from these learned distributions, the latent variables, tailored for role preference, dialogue coherence, and translation consistency, are incorporated into the NMT model for better translation. We evaluate our approach on the benchmark dataset BConTrasT ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/akjk-4527
https://underline.io/lecture/25883-modeling-bilingual-conversational-characteristics-for-neural-chat-translation
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7
Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition ...
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