Yu Lu


2021

pdf bib
RevCore: Review-Augmented Conversational Recommendation
Yu Lu | Junwei Bao | Yan Song | Zichen Ma | Shuguang Cui | Youzheng Wu | Xiaodong He
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

pdf bib
Attention Calibration for Transformer in Neural Machine Translation
Yu Lu | Jiali Zeng | Jiajun Zhang | Shuangzhi Wu | Mu Li
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Attention mechanisms have achieved substantial improvements in neural machine translation by dynamically selecting relevant inputs for different predictions. However, recent studies have questioned the attention mechanisms’ capability for discovering decisive inputs. In this paper, we propose to calibrate the attention weights by introducing a mask perturbation model that automatically evaluates each input’s contribution to the model outputs. We increase the attention weights assigned to the indispensable tokens, whose removal leads to a dramatic performance decrease. The extensive experiments on the Transformer-based translation have demonstrated the effectiveness of our model. We further find that the calibrated attention weights are more uniform at lower layers to collect multiple information while more concentrated on the specific inputs at higher layers. Detailed analyses also show a great need for calibration in the attention weights with high entropy where the model is unconfident about its decision.

2020

pdf bib
CASIA’s System for IWSLT 2020 Open Domain Translation
Qian Wang | Yuchen Liu | Cong Ma | Yu Lu | Yining Wang | Long Zhou | Yang Zhao | Jiajun Zhang | Chengqing Zong
Proceedings of the 17th International Conference on Spoken Language Translation

This paper describes the CASIA’s system for the IWSLT 2020 open domain translation task. This year we participate in both Chinese→Japanese and Japanese→Chinese translation tasks. Our system is neural machine translation system based on Transformer model. We augment the training data with knowledge distillation and back translation to improve the translation performance. Domain data classification and weighted domain model ensemble are introduced to generate the final translation result. We compare and analyze the performance on development data with different model settings and different data processing techniques.