Papers by Mou Hao
A Unified One-Step Solution for Aspect Sentiment Quad Prediction (2023.findings-acl)
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| Challenge: | Existing ASQP datasets are small and low-density, hindering technical advancement . et al. (2017): aspect sentiment quad prediction provides a complete aspect-level sentiment structure. |
| Approach: | They propose a one-step solution for Aspect sentiment quad prediction that can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously. |
| Outcome: | The proposed solution can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously. |
Discreteness in Neural Natural Language Processing (D19-2)
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| Challenge: | This tutorial provides a comprehensive guide to the process of discreteness in neural NLP. |
| Approach: | This tutorial provides a comprehensive guide to the process of discreteness in neural NLP. |
| Outcome: | This tutorial explains the process of discreteness in neural NLP. |
Generating Sentences from Disentangled Syntactic and Semantic Spaces (P19-1)
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| Challenge: | Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. |
| Approach: | They propose to generate sentences from disentangled syntactic and semantic spaces by using the linearized tree sequence. |
| Outcome: | The proposed method achieves similar or better performance in various tasks compared with state-of-the-art models. |
Unsupervised Paraphrasing by Simulated Annealing (2020.acl-main)
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| Challenge: | Existing approaches to generate accurate and different-appearing paraphrases require massive parallel samples for training. |
| Approach: | They propose a novel approach that accomplishes Unsupervised Paraphrasing by Simulated Annealing by performing local editing. |
| Outcome: | The proposed approach outperforms existing models in automatic and human evaluations on Quora, Wikianswers, MSCOCO, and Twitter. |
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (N19-1)
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| Challenge: | Experimental results show that the latent space learned by WAE exhibits properties of continuity and smoothness as in VAEs. |
| Approach: | They propose to use the variational autoencoder (VAE) for probabilistic sentence generation . they propose a variant of WAE that encourages the stochasticity of the encoder . |
| Outcome: | The proposed variant encourages the stochasticity of the encoder while achieving higher BLEU scores. |
Ultra-Low-Dimensional Prompt Tuning via Random Projection (2026.eacl-long)
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| Challenge: | Prompt tuning addresses parameter-efficiency by learning embeddings, but these embeddements are typically tied to the model’s hidden dimensionality, limiting parameter saving. |
| Approach: | They propose a parameter-efficient method that learns prompt embeddings exclusively in the input layer of the model and uses a frozen random matrix for up-projection. |
| Outcome: | The proposed method outperforms previous methods using significantly fewer parameters while maintaining performance. |
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward (2024.lrec-main)
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| Challenge: | Large language models have demonstrated remarkable performance in various NLP tasks, but are typically computationally expensive and difficult to be deployed in resource-constrained environments. |
| Approach: | They propose a knowledge distillation method based on a reward function induced from large language models. |
| Outcome: | The proposed method outperforms traditional methods on multiple datasets and tasks. |
VEG: Verbal 𝜖-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. |
PARSQL: Enhancing Text-to-SQL through SQL Parsing and Reasoning (2025.findings-acl)
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| Challenge: | Large language models have made significant strides in text-to-SQL tasks, but small language models struggle to accurately interpret natural language questions due to resource limitations. |
| Approach: | They propose a SQL parser that extracts constraints from SQL to generate sub-SQLs . they use a rule-based and LLM-based method to generate step-by-step SQL explanations based on the results . |
| Outcome: | The proposed framework outperforms models with the same model size on BIRD and Spider benchmarks. |
A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers (2024.findings-emnlp)
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| Challenge: | Existing methods for length control summarization treat the length requirement as a soft constraint, which may not always be satisfied. |
| Approach: | They propose a novel length-control decoding algorithm based on the directed acyclic Transformer (DAT) their approach allows for multiple plausible sequence fragments and predicts a path to connect them. |
| Outcome: | The proposed algorithm allows for multiple plausible sequence fragments and predicts a path to connect them. |