7 citations · 22 across the 10 of their papers we have counts for
10 papers
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking
Haoning Zhang, Junwei Bao, Haipeng Sun +4
Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizing all histor…
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking
Haoning Zhang, Junwei Bao, Haipeng Sun +3
Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with limited labeled data. Existing few-shot methods mainly transfer knowledge learned from…
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog
Haipeng Sun, Junwei Bao, Youzheng Wu +1
Traditional end-to-end task-oriented dialog systems first convert dialog context into belief state and action state before generating the system response. The system response perfo…
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog
Haipeng Sun, Junwei Bao, Youzheng Wu +1
A typical end-to-end task-oriented dialog system transfers context into dialog state, and upon which generates a response, which usually faces the problem of error propagation from…
OPERA:Operation-Pivoted Discrete Reasoning over Text
Yongwei Zhou, Junwei Bao, Chaoqun Duan +7
Machine reading comprehension (MRC) that requires discrete reasoning involving symbolic operations, e.g., addition, sorting, and counting, is a challenging task. According to this…
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
Yongwei Zhou, Junwei Bao, Haipeng Sun +5
Reasoning machine reading comprehension (R-MRC) aims to answer complex questions that require discrete reasoning based on text. To support discrete reasoning, evidence, typically t…
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation
Haipeng Sun, Rui Wang, Kehai Chen +3
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs. However, it can only translate between a single language pair an…
Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios
Haipeng Sun, Rui Wang, Kehai Chen +3
Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks. However, in real-worl…
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training
Haipeng Sun, Rui Wang, Kehai Chen +4
Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community. The main advantage of the UNMT lies in its easy collectio…
Revisiting Simple Domain Adaptation Methods in Unsupervised Neural Machine Translation
Haipeng Sun, Rui Wang, Kehai Chen +4
Domain adaptation has been well-studied in supervised neural machine translation (SNMT). However, it has not been well-studied for unsupervised neural machine translation (UNMT), a…