Papers by Bei Shi
Unsupervised KB-to-Text Generation with Auxiliary Triple Extraction using Dual Learning (2020.aacl-main)
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| Challenge: | Existing methods to generate text from KB triples are limited and expensive . a novel approach is proposed to train the generation model in unsupervised way . |
| Approach: | They propose a method which trains the generation model in a completely unsupervised way with unaligned raw text data and KB triples. |
| Outcome: | The proposed method outperforms existing methods and is cost-effective. |
Benchmarking and Learning Real-World Customer Service Dialogue (2026.acl-long)
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| Challenge: | Existing benchmarks and training pipelines for industrial intelligent customer service (ICS) focus on task completion and tool correctness. |
| Approach: | They propose a benchmark-to-optimization loop that bridges offline gains to deployment . they propose OlaMind, which distills reusable reasoning patterns from expert dialogues . |
| Outcome: | The proposed benchmark surpasses GPT-5.2 and Gemini 3 Pro on OlaBench . it delivers an average +23.67% issue resolution and -6.6% human transfer rate versus baseline . |
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training (2022.findings-emnlp)
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| Challenge: | Existing approaches to language-based environment manipulation are difficult to generalize across environments. |
| Approach: | They propose a general framework for language-based environment manipulation tasks that can deal with various environments using the same generative language model. |
| Outcome: | The proposed framework achieves new state-of-the-art results on four of the tasks and the execution-guided pre-training strategy brings remarkable improvements on all experimental tasks. |
Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations (2024.naacl-long)
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| Challenge: | Pre-trained vector representations can inadvertently encode undesirable social biases. |
| Approach: | They propose a framework for reducing bias by transforming vector representations to an unbiased subspace using sufficient projection. |
| Outcome: | The proposed framework mitigates bias across debiasing and fairness tasks and across various vector representation types, including word embeddings and output representations of transformer models. |
Partially-Aligned Data-to-Text Generation with Distant Supervision (2020.emnlp-main)
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| Challenge: | Using partially-aligned data is an alternative way of solving the dataset scarcity problem. |
| Approach: | They propose a task to generate human-readable text for describing some given structured data enabling more interpretability. |
| Outcome: | The proposed framework outperforms baseline models and validates the feasibility of using partially-aligned data. |
Transformation Networks for Target-Oriented Sentiment Classification (P18-1)
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| Challenge: | a new model for sentiment classification uses attention instead of attention to classify sentiment polarities over individual opinion targets. |
| Approach: | They propose a model that uses a CNN layer to extract salient features from transformed word representations from a bi-directional RNN layer. |
| Outcome: | The proposed model achieves state-of-the-art on a few benchmarks. |
Learning Domain-Sensitive and Sentiment-Aware Word Embeddings (P18-1)
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| Challenge: | Existing word embeddings cannot produce domain-sensitive embeddables due to domain-specific nature of words. |
| Approach: | They propose a method for learning domain-sensitive and sentiment-aware embeddings that captures sentiment semantics and domain sensitivity of individual words. |
| Outcome: | The proposed method can produce domain-common embeddings and domain-specific embedds. |