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20192022
most citedKnowledge Distillation for Multilingual Unsupervised Neural Machine Translation

6 citations · 9 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CL20221 cited

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…

cs.CL20222 cited

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…

cs.CL2022

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…

cs.CL2021

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…

cs.CL20206 cited

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…

cs.CL2020

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…