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Meng Fang
I am an Assistant Professor in AI at University of Liverpool. I'm also a visiting researcher at Eindhoven University of Technology (TU/e).
I received my Ph.D from University of Technology Sydney. I had been a research scientist/intern at Tencent Robotics X / AI, CSIRO and Microsoft Research Asia before.
People / Teaching & Service / Email / Github / Scholar / |
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Updates
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| Research |
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Projects |
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Text-based games |
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Question & Answering |
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Chatbots |
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Reinforcement learning |
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Publications |
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Selected: (Full publication list) |
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Where Would I Go Next? Large Language Models as Human Mobility Predictors |
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CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models |
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NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist |
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A Survey for Efficient Open Domain Question Answering |
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Stay Moral and Explore: Learn to Behave Morally in Text-based Games |
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Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach |
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COOM: A Game Benchmark for Continual Reinforcement Learning |
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Dynamic Sparsity Is Channel-Level Sparsity Learner |
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Are Large Kernels Better Teachers than Transformers for ConvNets? |
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Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost |
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Perceiving the World: Question-guided Reinforcement Learning for Text-based Games |
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Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games |
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A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation |
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You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets |
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Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL |
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TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack |
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Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics |
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Phrase-level Textual Adversarial Attack with Label Preservation |
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Diversity-augmented intrinsic motivation for deep reinforcement learning |
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Generalization in Text-based Games via Hierarchical Reinforcement Learning |
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ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection |
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DAGN: Discourse-Aware Graph Network for Logical Reasoning |
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REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement |
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Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation |
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Deep Reinforcement Learning for Prefab Assembly Planning in Robot-based Prefabricated Construction |
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Reinforcement Learning With Multiple Relational Attention for Solving Vehicle Routing Problems |
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On the Guaranteed Almost Equivalence Between Imitation Learning From Observation and Demonstration |
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TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game |
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Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games |
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Pretrained Language Models for Dialogue Generation with Multiple Input Sources |
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Unsupervised Domain Adaptation on Reading Comprehension |
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Curriculum-guided hindsight experience replay |
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DHER: Hindsight experience replay for dynamic goals |
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LIIR: Learning individual intrinsic reward in multi-agent reinforcement learning |
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Dual adversarial neural transfer for low-resource named entity recognition |
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Bag: Bi-directional attention entity graph convolutional network for multi-hop reasoning question answering |
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Learning how to Active Learn: A Deep Reinforcement Learning Approach |
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Model transfer for tagging low-resource languages using a bilingual dictionary |
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TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs |
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Networked bandits with disjoint linear payoffs |
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Active Learning for Crowdsourcing Using Knowledge Transfer |
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| Awards |
| Acknowledgements I would like to thank all my collaborators, interns and students. |
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