Papers by Haipeng Sun
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog (2023.findings-acl)
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| Challenge: | Empirical results show dialog context representations are more conducive to multi-turn task-oriented dialog. |
| Approach: | They propose an end-to-end task-oriented dialog system with two contrastive learning strategies to model relationship between dialog context and belief/action state representations. |
| Outcome: | Empirical results show that dialog context representations are more conducive to multi-turn task-oriented dialog. |
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog (2022.findings-naacl)
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| Challenge: | Existing task-oriented dialog systems suffer from error propagation from inaccurate dialog states and responses. |
| Approach: | They propose a back reconstruction approach for end-to-end task-oriented dialog system . they use back reconstruction to reconstruct the original input context from the generated dialog states . |
| Outcome: | Extensive experiments on MultiWOZ 2.0 and CamRest676 show the effectiveness of the proposed model. |
English-Myanmar Supervised and Unsupervised NMT: NICT’s Machine Translation Systems at WAT-2019 (D19-52)
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| Challenge: | NICT participated in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (My) - English task in both translation directions. |
| Approach: | They present the participation of the NICT in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (Burmese) - English task in both translation directions. |
| Outcome: | The proposed systems perform the third in English-to-Myanmar and the second in Myanmar-to English according to BLEU score. |
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training (2020.coling-main)
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| Challenge: | Unsupervised neural machine translation (UNMT) has attracted great interest in the machine translation community. |
| Approach: | They propose to explicitly take noisy data into consideration to improve the robustness of UNMT based systems. |
| Outcome: | The proposed methods significantly improved the robustness of the conventional UNMT systems in noisy scenarios. |
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking (2023.findings-acl)
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| Challenge: | Experimental results show that MoNET outperforms previous DST methods in alleviating state momentum issues and improving the anti-noise ability. |
| Approach: | They propose to use previous state of each turn in training data as input to learn to predict current state. |
| Outcome: | The proposed model outperforms existing methods on multiWOZ datasets and shows that it can update and correct slot values and improve anti-noise ability. |
Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning (2025.coling-main)
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| Challenge: | Existing end-to-end task-oriented dialog systems often encounter challenges arising from implicit information, coreference, and the presence of noisy and irrelevant data within the dialog context. |
| Approach: | They propose a dialog context fusion mechanism for end-to-end task-oriented dialog augmented with three additional tasks: dialog summarization, domain prediction, and slot detection. |
| Outcome: | The proposed method achieves state-of-the-art on the MultiWOZ and CrossWOZ datasets. |
OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)
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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
| Challenge: | Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability. |
| Approach: | They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability. |
| Outcome: | Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA. |
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation (2020.acl-main)
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| Challenge: | Unsupervised neural machine translation (UNMT) can only translate between a single language pair and cannot produce translation results for multiple language pairs at the same time. |
| Approach: | They propose a method to translate between 13 languages using a single encoder and a decoder . they propose two knowledge distillation methods to further enhance multilingual UNMT performance . |
| Outcome: | The proposed method improves translation performance for all languages using multilingual data. |
Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios (2021.naacl-main)
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| Challenge: | Existing methods that use monolingual corpora for translation are not suitable for low-resource languages such as Estonian. |
| Approach: | They propose unsupervised neural machine translation (UNMT) that relies on monolingual corpora to train a robust UNMT system and improve its performance. |
| Outcome: | The proposed methods outperform conventional UNMT systems on several language pairs. |
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking (2022.aacl-short)
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| Challenge: | Existing few-shot dialogue state tracking (DST) methods transfer knowledge from labeled data into DST, but collecting large amount of labeles is laborious. |
| Approach: | They propose a few-shot dialogue state tracking framework that integrates self-training and self-supervised learning methods into the framework. |
| Outcome: | The proposed framework achieves competitive performance in several few-shot scenarios. |
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation (P19-1)
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| Challenge: | Unsupervised bilingual word embedding (UBWE) has helped unsupervised neural machine translation (UNMT) achieve remarkable results in several language pairs. |
| Approach: | They propose two methods that train UNMT with UBWE agreement . they propose to use UBwe to initialize word embedding in UNMT . |
| Outcome: | The proposed methods outperform conventional methods on several language pairs. |