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07/25/2023
Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm
In this paper, we conduct a holistic exploration of the Universal Decomp...
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05/24/2023
Revisiting Token Dropping Strategy in Efficient BERT Pretraining
Token dropping is a recently-proposed strategy to speed up the pretraini...
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05/03/2023
TempoSum: Evaluating the Temporal Generalization of Abstractive Summarization
Recent pre-trained language models (PLMs) achieve promising results in e...
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03/24/2023
Towards Making the Most of ChatGPT for Machine Translation
ChatGPT shows remarkable capabilities for machine translation (MT). Seve...
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12/08/2022
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation
Transfer learning is a simple and powerful method that can be used to bo...
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12/02/2022
Improving Simultaneous Machine Translation with Monolingual Data
Simultaneous machine translation (SiMT) is usually done via sequence-lev...
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11/23/2022
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling
Existing research generally treats Chinese character as a minimum unit f...
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11/03/2022
Revisiting Grammatical Error Correction Evaluation and Beyond
Pretraining-based (PT-based) automatic evaluation metrics (e.g., BERTSco...
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04/16/2022
BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representation
Data augmentations (DA) are the cores to achieving robust sequence-to-se...
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11/07/2021
Variance-Aware Machine Translation Test Sets
We release 70 small and discriminative test sets for machine translation...
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10/05/2021
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation
Pre-training (PT) and back-translation (BT) are two simple and powerful ...
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07/30/2021
Difficulty-Aware Machine Translation Evaluation
The high-quality translation results produced by machine translation (MT...
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07/17/2021
On the Copying Behaviors of Pre-Training for Neural Machine Translation
Previous studies have shown that initializing neural machine translation...
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06/10/2021
Progressive Multi-Granularity Training for Non-Autoregressive Translation
Non-autoregressive translation (NAT) significantly accelerates the infer...
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06/02/2021
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation
Knowledge distillation (KD) is commonly used to construct synthetic data...
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03/03/2021
Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation
Meta-learning has been sufficiently validated to be beneficial for low-r...
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12/29/2020
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
Encoder layer fusion (EncoderFusion) is a technique to fuse all the enco...
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12/29/2020
Understanding and Improving Lexical Choice in Non-Autoregressive Translation
Knowledge distillation (KD) is essential for training non-autoregressive...
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08/03/2020
AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting
Scene text spotting aims to detect and recognize the entire word or sent...
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06/03/2020
Norm-Based Curriculum Learning for Neural Machine Translation
A neural machine translation (NMT) system is expensive to train, especia...
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05/07/2020
Scene Text Image Super-Resolution in the Wild
Low-resolution text images are often seen in natural scenes such as docu...
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09/29/2019
PolarMask: Single Shot Instance Segmentation with Polar Representation
In this paper, we introduce an anchor-box free and single shot instance ...
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06/07/2019
Shared-Private Bilingual Word Embeddings for Neural Machine Translation
Word embedding is central to neural machine translation (NMT), which has...
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03/28/2019
Pyramid Mask Text Detector
Scene text detection, an essential step of scene text recognition system...
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02/28/2019
Context-aware Dynamic Block
Although deeper and larger neural networks have achieved better performa...
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01/05/2018