Weihao Zeng


Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization
Lulu Zhao | Fujia Zheng | Weihao Zeng | Keqing He | Weiran Xu | Huixing Jiang | Wei Wu | Yanan Wu
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The most advanced abstractive dialogue summarizers lack generalization ability on new domains and the existing researches for domain adaptation in summarization generally rely on large-scale pre-trainings. To explore the lightweight fine-tuning methods for domain adaptation of dialogue summarization, in this paper, we propose an efficient and generalizable Domain-Oriented Prefix-tuning model, which utilizes a domain word initialized prefix module to alleviate domain entanglement and adopts discrete prompts to guide the model to focus on key contents of dialogues and enhance model generalization. We conduct zero-shot experiments and build domain adaptation benchmarks on two multi-domain dialogue summarization datasets, TODSum and QMSum. Adequate experiments and qualitative analysis prove the effectiveness of our methods.

Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems
Weihao Zeng | Keqing He | Zechen Wang | Dayuan Fu | Guanting Dong | Ruotong Geng | Pei Wang | Jingang Wang | Chaobo Sun | Wei Wu | Weiran Xu
Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)

Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals. However, such systems rely on costly manually labeled dialogs which are not available in practical scenarios. In this paper, we present our models for Track 2 of the SereTOD 2022 challenge, which is the first challenge of building semisupervised and reinforced TOD systems on a large-scale real-world Chinese TOD dataset MobileCS. We build a knowledge-grounded dialog model to formulate dialog history and local KB as input and predict the system response. And we perform semi-supervised pretraining both on the labeled and unlabeled data. Our system achieves the first place both in the automatic evaluation and human interaction, especially with higher BLEU (+7.64) and Success (+13.6%) than the second place.


Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization
Lulu Zhao | Weihao Zeng | Weiran Xu | Jun Guo
Findings of the Association for Computational Linguistics: EMNLP 2021

Abstractive dialogue summarization suffers from a lots of factual errors, which are due to scattered salient elements in the multi-speaker information interaction process. In this work, we design a heterogeneous semantic slot graph with a slot-level mask cross-attention to enhance the slot features for more correct summarization. We also propose a slot-driven beam search algorithm in the decoding process to give priority to generating salient elements in a limited length by “filling-in-the-blanks”. Besides, an adversarial contrastive learning assisting the training process is introduced to alleviate the exposure bias. Experimental performance on different types of factual errors shows the effectiveness of our methods and human evaluation further verifies the results..