Papers by Bingfeng Luo
Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model (2020.findings-emnlp)
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| Challenge: | Neural network based data2text models drop or modify information in inputs and it is hard to control the generated contents. |
| Approach: | They propose a template-based data2text system powered by a text stitch model that automatically stitches adjacent template units. |
| Outcome: | The proposed system outperforms template-based systems in fidelity and human involvement on a benchmark dataset. |
Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL (2025.naacl-long)
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| Challenge: | Existing approaches to generative language models struggle to handle the increasing complexity of multi-turn Text-to-SQL tasks. |
| Approach: | They propose a framework which enhances generative language models with dual-extractive modules designed to track schema and contextual changes in multi-turn Text-to-SQL. |
| Outcome: | The proposed framework achieves state-of-the-art performance on SparC and CoSQL datasets and significantly improves execution accuracy in multi-turn interactions by 7.1% and 9.55%. |
CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction (2025.coling-main)
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| Challenge: | Existing generative ASQP approaches do not model the contextual relationship of the review sentence to predict implicit terms. |
| Approach: | They propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network to enhance alignment of aspects and opinions. |
| Outcome: | The proposed framework improves the alignment of aspects and opinions, whether explicit or implicit, and improves on three benchmark datasets. |
Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)
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| Challenge: | Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available. |
| Approach: | They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN. |
| Outcome: | The proposed approach significantly improves learning effectiveness when a small number of training examples are available. |
S2GSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis (2024.acl-long)
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| Challenge: | Existing graph-based approaches to learn static structures and dynamic latent trees are lacking in incorporating semantic and syntactic information simultaneously within complex global structures. |
| Approach: | They propose a graph-based framework that incorporates semantic and syntactic information simultaneously within global structures. |
| Outcome: | The proposed framework removes irrelevant contexts and syntactic dependencies and achieves complementarity across diverse structures. |
Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)
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| Challenge: | The 20 Questions (Q20) game encourages deductive reasoning and creativity. |
| Approach: | They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward . |
| Outcome: | The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment. |