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Found 16 papers, 6 papers with code
DUTNLP Machine Translation System for WMT21 Triangular Translation Task
This paper describes DUT-NLP Lab’s submission to the WMT-21 triangular machine translation shared task.
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation
Experimental results on five benchmarks and four cross-domain datasets show the lexicon-based graph convolutional network successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010).
Context-Aware Word Segmentation for Chinese Real-World Discourse
This paper proposes a three-step strategy to improve the performance for discourse CWS.
A Joint Multiple Criteria Model in Transfer Learning for Cross-domain Chinese Word Segmentation
Chinese word segmentation (CWS) is an essential task for Chinese downstream NLP tasks.
Exploring Better Text Image Translation with Multimodal Codebook
Text image translation (TIT) aims to translate the source texts embedded in the image to target translations, which has a wide range of applications and thus has important research value.
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
We also introduce two deliberately designed test sets to verify the necessity of visual information: Ambiguous with the presence of ambiguous words, and Unambiguous in which the text context is self-contained for translation.
Towards Robust k-Nearest-Neighbor Machine Translation
Meanwhile we inject two types of perturbations into the retrieved pairs for robust training.
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models.
Towards User-Driven Neural Machine Translation
Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus.
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation
Due to the great potential in facilitating software development, code generation has attracted increasing attention recently.
Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction
KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction.
