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TinyBERT: Distilling BERT for Natural Language Understanding
Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationallyExpand
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HHMM-based Chinese Lexical Analyzer ICTCLAS
This document presents the results from Inst. of Computing Tech., CAS in the ACL SIGHAN-sponsored First International Chinese Word Segmentation Bake-off. The authors introduce the unified HHMM-basedExpand
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TinyBERT: Distilling BERT for Natural Language Understanding
Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationallyExpand
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ERNIE: Enhanced Language Representation with Informative Entities
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve theExpand
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Exploiting Cross-Sentence Context for Neural Machine Translation
In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate theExpand
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基於《知網》的辭彙語義相似度計算 (Word Similarity Computing Based on How-net)
  • Qun Liu, Sujian Li
  • Computer Science
  • Int. J. Comput. Linguistics Chin. Lang. Process.
  • 2002
Word similarity is broadly used in many applications, such as information retrieval, information extraction, text classification, word sense disambiguation, example -based machine translation, etc.Expand
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Maximum Entropy Based Phrase Reordering Model for Statistical Machine Translation
We propose a novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). TheExpand
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Findings of the 2017 Conference on Machine Translation (WMT17)
This paper presents the results of the WMT17 shared tasks, which included three machine translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and run-timeExpand
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Tree-to-String Alignment Template for Statistical Machine Translation
We present a novel translation model based on tree-to-string alignment template (TAT) which describes the alignment between a source parse tree and a target string. A TAT is capable of generatingExpand
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Doubly-Attentive Decoder for Multi-modal Neural Machine Translation
We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neuralExpand
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