1 |
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization ...
|
|
|
|
Abstract:
We present ClidSum, a benchmark dataset for building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents from two subsets (i.e., SAMSum and MediaSum) and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART-50 (a multi-lingual BART) via further pre-training. The multiple objectives used in the further pre-training stage help the pre-trained model capture the structural characteristics as well as important content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we ...
|
|
Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://arxiv.org/abs/2202.05599 https://dx.doi.org/10.48550/arxiv.2202.05599
|
|
BASE
|
|
Hide details
|
|
5 |
EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Modeling Bilingual Conversational Characteristics for Neural Chat Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Sequence-Level Training for Non-Autoregressive Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Competence-based Curriculum Learning for Multilingual Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|