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Haifeng Tang

7 accepted papers

2025

A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge

NAACL 2025industry

Designing effective debt collection systems is crucial for improving operational efficiency and reducing costs in the financial industry. However, the challenges of maintaining script diversity, contextual relevance, and coherence make this task particularly difficult. This paper presents a debt col…

Cited by 0SourcePDFScholar
2023

In-sample Curriculum Learning by Sequence Completion for Natural Language Generation

ACL 2023long

Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones. Previous works which either design rules or train models for scoring the difficulty highly rely on task-specific expertise, and cannot generalize. Inspired by…

2023

Incomplete Utterance Rewriting by A Two-Phase Locate-and-Fill Regime

ACL 2023findings

Rewriting incomplete and ambiguous utterances can improve dialogue models’ understanding of the context and help them generate better results. However, the existing end-to-end models will have the problem of too large search space, resulting in poor quality of rewriting results. We propose a 2-phase…

Cited by 4SourcePDFScholar
2023

Reducing Sensitivity on Speaker Names for Text Generation from Dialogues

ACL 2023findings

Changing speaker names consistently throughout a dialogue should not affect its meaning and corresponding outputs for text generation from dialogues. However, pre-trained language models, serving as the backbone for dialogue-processing tasks, have shown to be sensitive to nuances. This may result in…

2022

ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments

ACL 2022long

Existing automatic evaluation systems of chatbots mostly rely on static chat scripts as ground truth, which is hard to obtain, and requires access to the models of the bots as a form of “white-box testing”. Interactive evaluation mitigates this problem but requires human involvement. In our work, we…

2022

Post-Training Dialogue Summarization using Pseudo-Paraphrasing

NAACL 2022findings

Previous dialogue summarization techniques adapt large language models pretrained on the narrative text by injecting dialogue-specific features into the models. These features either require additional knowledge to recognize or make the resulting models harder to tune. To bridge the format gap betwe…