Papers by Chenlei Guo
Joint Goal Segmentation and Goal Success Prediction on Multi-Domain Conversations (2022.coling-1)
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| Challenge: | Existing metrics to measure the performance of conversational AI assistants are difficult to establish due to their slow nature. |
| Approach: | They propose an automatic dialogue evaluation framework that performs goal segmentation and success prediction by adding multi-task learning heads. |
| Outcome: | The proposed model achieves on-par with human annotation compared to a gold annotation benchmark. |
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation (2021.emnlp-main)
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| Challenge: | Existing approaches to generate paraphrases with weak supervision are limited in real-world scenarios due to the lack of coherent and controllable generated paraphrase. |
| Approach: | They propose a method to generate high-quality paraphrases with weak supervision . they obtain abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion . |
| Outcome: | The proposed approach achieves significant improvements over existing methods and is even comparable in performance with supervised state-of-the-arts. |
PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding (2022.emnlp-industry)
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| Challenge: | In a large fraction of the global traffic from smart digital assistants, frictions in dialogues may be attributed to incorrect understanding of the entities in a user's query due to factors including ambiguous mentions, mispronunciation, background noise and faulty on-device signal processing. |
| Approach: | They propose a parametric transformer-based language model to learn patterns from in-session customer-device interactions coupled with a non-parametric personalized entity index to compute the correct query. |
| Outcome: | The proposed system improves on the existing system and shows that it can learn the correct query from in-session customer-device interactions. |
Contextual Rephrase Detection for Reducing Friction in Dialogue Systems (2021.emnlp-main)
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| Challenge: | Large-scale conversational AI based dialogue systems like Alexa, Siri, and Google Assistant, are getting more and more prevalent in real-world applications to help users across the globe. |
| Approach: | They propose a contextual rephrase detection model ContReph to automatically identify rephrasings from multi-turn dialogues using contextual information and user-agent interaction signals. |
| Outcome: | The proposed model outperforms the pairwise rephrase detection models by leveraging the context and user-agent interaction signals. |
CL-QR: Cross-Lingual Enhanced Query Reformulation for Multi-lingual Conversational AI Agents (2023.emnlp-industry)
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| Challenge: | Existing QR systems that reformulate defective user queries are limited in English due to the scarcity of non-English QR labels. |
| Approach: | They propose a query reformulation method which reformulates defective user queries to improve non-English QR performance. |
| Outcome: | The proposed framework improves non-English QR performance by leveraging abundant reformulation resources in English. |
CGF: Constrained Generation Framework for Query Rewriting in Conversational AI (2022.emnlp-industry)
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Jie Hao, Yang Liu, Xing Fan, Saurabh Gupta, Saleh Soltan, Rakesh Chada, Pradeep Natarajan, Chenlei Guo, Gokhan Tur
| Challenge: | Large-scale conversational AI agents such as Alexa, Siri and Google Assistant help millions of users to perform a lot of tasks. |
| Approach: | They propose a Constrained Generation Framework for query rewriting at global and personalized levels. |
| Outcome: | The proposed framework significantly boosts the query rewriting performance. |
Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation (2022.naacl-main)
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| Challenge: | Existing work on lifelong learning requires incremental memory space to learn a model . existing work on experience replay or elastic weighted consolidation requires incremental space . |
| Approach: | They propose a framework that leverages a recall optimization mechanism to memorize parameters of previous tasks via regularization and a domain drift estimation algorithm to compensate the drift between different domains in the embedding space. |
| Outcome: | The proposed framework outperforms SOTA models on paraphrase and dialog response generation tasks. |
Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI (2022.naacl-industry)
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| Challenge: | Large-scale conversational AI systems require constant update to adapt to changing customer behavior and trends . lack of self-awareness in feedback-based systems can cause degradation of performance . et al., e. alderman and scott k. d. argues that such systems are not scalable enough to sustain the rapid update pace of conversational systems. |
| Approach: | They propose a superposition-based model that reactively learns local-adaptive decision boundaries . they propose rewritings with a bi-variate beta setting to improve the model's performance . |
| Outcome: | The proposed model improves the PR-AUC by 27.45% and reduces relative defect reductions by 31.22% . the proposed model can adapt faster to changes in global preferences across a large number of customers . |
Analyzing and Internalizing Complex Policy Documents for LLM Agents (2026.acl-long)
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Jiateng Liu, Zhenhailong Wang, Xiaojiang Huang, Yingjie Li, Xiang Li, Chenlei Guo, Xing Fan, Ruhi Sarikaya, Heng Ji
| Challenge: | Large language model agents rely on in-context policy documents to act as effective user assistants. |
| Approach: | They propose an agentic benchmark generator with Controllable Complexity in agent policy across four levels to evaluate agents under increasing complexity. |
| Outcome: | The proposed method outperforms the baseline in data-sparse and high-complexity settings. |
VEG: Verbal đťś–-greedy for Semantic Exploration in Multi-Turn RL Agents (2026.acl-industry)
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Yongchang Hao, Jie Hao, Yongsheng Mei, Ze Ye, Junyi Chai, Bin Guo, Benjamin Z. Yao, Chenlei Guo, Lili Mou
| Challenge: | Standard RL approaches suffer from reward sparsity and mode-seeking behavior . lack of diversity hinders exploration necessary for optimal learning . |
| Approach: | They propose a framework that leverages external feedback as a dynamic control variable to explicitly balance exploration and exploitation within the semantic space. |
| Outcome: | Experiments on Tau Bench and SearchQA show that the proposed framework outperforms standard RL baselines. |
Improving Contextual Query Rewrite for Conversational AI Agents through User-preference Feedback Learning (2023.emnlp-industry)
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| Challenge: | Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging contextual information from previous user-agent conversations to improve comprehension of current user intent. |
| Approach: | They propose a framework to enhance the CQR model's capability in generating user preference-aligned rewrites. |
| Outcome: | The proposed framework improves the CQR model's ability to generate user preference-aligned rewrites. |
PAIGE: Personalized Adaptive Interactions Graph Encoder for Query Rewriting in Dialogue Systems (2022.emnlp-industry)
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| Challenge: | Existing methods to fix faulty queries are limited in their ability to fix them. |
| Approach: | They propose a Personalized Adaptive Interactions Graph Encoder that integrates user's affinities and query semantics to refine utterance embeddings. |
| Outcome: | The proposed Query Rewriting (QR) techniques improve the rewrite accuracy of state-of-the-art baselines by 12.5–17.5% while having nearly ten times fewer parameters. |