Chen Huang


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TRAVEL: Tag-Aware Conversational FAQ Retrieval via Reinforcement Learning
Yue Chen | Dingnan Jin | Chen Huang | Jia Liu | Wenqiang Lei
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Efficiently retrieving FAQ questions that match users’ intent is essential for online customer service. Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions. However, the conversation context contains noise, e.g., users may click questions they don’t like, leading to inaccurate semantics modeling. To tackle this, we introduce tags of FAQ questions, which can help us eliminate irrelevant information. We later integrate them into a reinforcement learning framework and minimize the negative impact of irrelevant information in the dynamic conversation context. We experimentally demonstrate our efficiency and effectiveness on conversational FAQ retrieval compared to other baselines.

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Reduce Human Labor On Evaluating Conversational Information Retrieval System: A Human-Machine Collaboration Approach
Chen Huang | Peixin Qin | Wenqiang Lei | Jiancheng Lv
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Evaluating conversational information retrieval (CIR) systems is a challenging task that requires a significant amount of human labor for annotation. It is imperative to invest significant effort into researching more labor-effective methods for evaluating CIR systems. To touch upon this challenge, we take the first step to involve active testing in CIR evaluation and propose a novel method, called HomCoE. It strategically selects a few data for human annotation, then calibrates the evaluation results to eliminate evaluation biases. As such, it makes an accurate evaluation of the CIR system at low human labor. We experimentally reveal that it consumes less than 1% of human labor and achieves a consistency rate of 95%-99% with human evaluation results. This emphasizes the superiority of our method over other baselines.

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Towards Effective Automatic Debt Collection with Persona Awareness
Tong Zhang | Junhong Liu | Chen Huang | Jia Liu | Hongru Liang | Zujie Wen | Wenqiang Lei
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

Understanding debtor personas is crucial for collectors to empathize with debtors and develop more effective collection strategies. In this paper, we take the first step towards comprehensively investigating the significance of debtor personas and present a successful commercial practice on automatic debt collection agents. Specifically, we organize the debtor personas into a taxonomy and construct a persona-aware conversation dataset. Building upon it, we implement a simple yet effective persona-aware agent called PAD. After two-month online testing, PAD increases the recovery rate by 3.31% and collects an additional ~100K RMB. Our commercial practice brings inspiration to the debt collection industry by providing an effective automatic solution.


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Neural-DINF: A Neural Network based Framework for Measuring Document Influence
Jie Tan | Changlin Yang | Ying Li | Siliang Tang | Chen Huang | Yueting Zhuang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Measuring the scholarly impact of a document without citations is an important and challenging problem. Existing approaches such as Document Influence Model (DIM) are based on dynamic topic models, which only consider the word frequency change. In this paper, we use both frequency changes and word semantic shifts to measure document influence by developing a neural network framework. Our model has three steps. Firstly, we train the word embeddings for different time periods. Subsequently, we propose an unsupervised method to align vectors for different time periods. Finally, we compute the influence value of documents. Our experimental results show that our model outperforms DIM.