Papers by Jianpeng Cheng
ASPERA: A Simulated Environment to Evaluate Planning for Complex Action Execution (2025.acl-long)
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Alexandru Coca, Mark Gaynor, Zhenxing Zhang, Jianpeng Cheng, Bo-Hsiang Tseng, Peter Boothroyd, Hector Martinez Alonso, Diarmuid O Seaghdha, Anders Johannsen
| Challenge: | ASPERA framework allows developers to guide LLM generation of high-quality tasks based on user queries, simulation state and corresponding validation programs. |
| Approach: | They develop a framework comprising an assistant library simulation and a human-assisted LLM data generation engine to guide LLM generation of high-quality tasks . they use a dataset to evaluate 250 tasks generated using ASPERA . |
| Outcome: | The proposed framework can guide LLM generation of high-quality tasks tackling data availability and evaluation robustness challenges. |
Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries (2024.naacl-long)
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| Challenge: | Recent studies use in-context learning with large language models (LLM) to find similar dialogue exemplars for prompt learning. |
| Approach: | They propose to use a conversation retriever to find similar in-context examples for prompt learning. |
| Outcome: | The proposed approach improves on multiWOZ datasets with GPT-Neo-2.7B and LLaMA-7B/30B . |
A Generative Model for Joint Natural Language Understanding and Generation (2020.acl-main)
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| Challenge: | Natural language understanding (NLU) and natural language generation (NLG) have opposite goals. |
| Approach: | They propose a generative model which couples NLU and NLG through a shared latent variable. |
| Outcome: | The proposed model achieves state-of-the-art performance on two dialogue datasets with flat and tree-structured formal representations. |
Conversational Semantic Parsing for Dialog State Tracking (2020.emnlp-main)
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Jianpeng Cheng, Devang Agrawal, Héctor Martínez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D. Williams, Hong Yu, Diarmuid Ó Séaghdha, Anders Johannsen
| Challenge: | Language understanding for task-based dialog systems is often termed "dialog state tracking" (DST) whereas semantic parsing is the task of converting a single-turn utterance to a graphstructured meaning representation, DST is more complex. |
| Approach: | They propose a framework for dialog state tracking that incorporates semantic compositionality, cross-domain knowledge sharing and co-reference. |
| Outcome: | The proposed framework improves on state-of-the-art approaches for dialog state tracking (DST) it incorporates semantic compositionality, cross-domain knowledge sharing and co-reference. |
LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues (2024.naacl-srw)
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Joe Stacey, Jianpeng Cheng, John Torr, Tristan Guigue, Joris Driesen, Alexandru Coca, Mark Gaynor, Anders Johannsen
| Challenge: | Existing datasets with limited domain coverage and few challenging conversational phenomena are often unlabelled . Existing data is limited in quality and lacks a robust evaluation process . |
| Approach: | They propose a high quality data generation system that generates high quality dialogues using 4,277 conversations across 100 intents. |
| Outcome: | The proposed system produces high quality dialogue data with high quality labels. |