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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.
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.
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
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).
Multi-agent Learning for Neural Machine Translation
Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, e. g., dual learning, and bidirectional decoding with one agent decoding from left to right and the other decoding in the opposite direction.
