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Found 22 papers, 7 papers with code
Unsupervised Preference-Aware Language Identification
Recognizing the language of ambiguous texts has become a main challenge in language identification (LID).
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation
Diverse NMT aims at generating multiple diverse yet faithful translations given a source sentence.
GCPG: A General Framework for Controllable Paraphrase Generation
Under GCPG, we reconstruct commonly adopted lexical condition (i. e., Keywords) and syntactical conditions (i. e., Part-Of-Speech sequence, Constituent Tree, Masked Template and Sentential Exemplar) and study the combination of the two types.
RoBLEURT Submission for WMT2021 Metrics Task
After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.
Challenges of Neural Machine Translation for Short Texts
After empirically investigating the rationale behind this, we summarize two challenges in NMT for STs associated with translation error types above, respectively: (1) the imbalanced length distribution in training set intensifies model inference calibration over STs, leading to more over-translation cases on STs; and (2) the lack of contextual information forces NMT to have higher data uncertainty on short sentences, and thus NMT model is troubled by considerable mistranslation errors.
Versatile Defense Against Adversarial Attacks on Image Recognition
When facing the PGD attack and the MI-FGSM attack, versatile defense model even outperforms the attack-specific models trained based on these two attacks.
Efficient All-reduce for Distributed DNN Training in Optical Interconnect System
Our results also show that WRHT can reduce the communication time of all-reduce operation by 92. 42% and 91. 31% compared to two existing all-reduce algorithms running in the electrical interconnect system.
UniTE: Unified Translation Evaluation
Translation quality evaluation plays a crucial role in machine translation.
RoBLEURT Submission for the WMT2021 Metrics Task
After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.
Attention Mechanism with Energy-Friendly Operations
Attention mechanism has become the dominant module in natural language processing models.
Frequency-Aware Contrastive Learning for Neural Machine Translation
Inspired by the observation that low-frequency words form a more compact embedding space, we tackle this challenge from a representation learning perspective.
KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation
Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently.
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval
Interactive and non-interactive model are the two de-facto standard frameworks in vector-based cross-lingual information retrieval (V-CLIR), which embed queries and documents in synchronous and asynchronous fashions, respectively.
Computational Efficiency
Cross-Lingual Information Retrieval
+4
Accelerating Fully Connected Neural Network on Optical Network-on-Chip (ONoC)
Compared with ENoC, simulation results show that under batch sizes of 64 and 128, on average ONoC can achieve 21. 02% and 12. 95% on reducing training time with 47. 85% and 39. 27% on saving energy, respectively.
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation
A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary.
Towards User-Driven Neural Machine Translation
Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus.
Multi-Hop Transformer for Document-Level Machine Translation
Document-level neural machine translation (NMT) has proven to be of profound value for its effectiveness on capturing contextual information.
Domain Transfer based Data Augmentation for Neural Query Translation
Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR).
Constraint Translation Candidates: A Bridge between Neural Query Translation and Cross-lingual Information Retrieval
Query translation (QT) is a key component in cross-lingual information retrieval system (CLIR).
Exploiting Neural Query Translation into Cross Lingual Information Retrieval
As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel training data; and 3) the requisite of low latency.
Self-Paced Learning for Neural Machine Translation
Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans.
Alibaba's Neural Machine Translation Systems for WMT18
We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese.
