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Haipeng Sun

7 accepted papers

2025

Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning

COLING 2025main

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. These issues hinder the system’s ability to fully comprehend critical information and lead to inaccurat…

2023

Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog

ACL 2023findings

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 performance is significantly affected by the quality of the belief state and action state. We first explore what dialog conte…

2023

MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking

ACL 2023findings

Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizes all history information, the dialogue state in the previous turn is typically adopted as the input for predicting the current state…

Cited by 9SourcePDFScholar
2022

BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog

NAACL 2022findings

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 both previously generated inaccurate dialog states and responses, especially in low-resource scenarios. To alleviate th…

2022

OPERA: Operation-Pivoted Discrete Reasoning over Text

NAACL 2022long

Machine reading comprehension (MRC) that requires discrete reasoning involving symbolic operations, e.g., addition, sorting, and counting, is a challenging task. According to this nature, semantic parsing-based methods predict interpretable but complex logical forms. However, logical form generation…

2021

Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios

NAACL 2021long

Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks. However, in real-world scenarios, massive monolingual corpora do not exist for some extremely low-resource languages such as Estonian, and UN…

Cited by 12SourcePDFScholar
2020

Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training

COLING 2020main

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 collection of required large training text sentences while with only a slightly worse performance than supervised neural machine…