Papers by Liwen Wang
Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing frameworks for dialogue state tracking with domain-slot-value labels are expensive . current models are limited due to high cost of data annotation and lack of data in some domains . |
| Approach: | They propose a framework based on domain-slot related description to tackle the challenge of few-shot cross-domain DST. |
| Outcome: | The proposed framework outperforms existing methods on MultiWOZ and gains strong slot accuracy compared to existing models. |
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for zero-shot cross-domain slot filling do not achieve effective knowledge transfer to the target domain. |
| Approach: | They propose a novel approach based on prototypical contrastive learning and a dynamic label confusion strategy for zero-shot slot filling. |
| Outcome: | The proposed model improves on unseen slots while setting new state-of-the-arts on slot filling task. |
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)
Copied to clipboard
Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang
| Challenge: | Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting. |
| Approach: | They propose a representation-aware model merging framework for continual learning without access to historical data. |
| Outcome: | The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios. |
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling (2022.coling-1)
Copied to clipboard
Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu
| Challenge: | Existing slot filling models memorize inherent patterns of entities and contexts from training data. |
| Approach: | They propose a perturbed semantic structure awareness transferring method for slot filling models . they use two MLM-based training strategies to learn contextual semantic structure and word distribution . |
| Outcome: | The proposed method outperforms existing methods and gains strong generalization while preventing model from memorizing inherent patterns of entities and contexts. |
AISFG: Abundant Information Slot Filling Generator (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to zero/few-shot slot filling focus on slot descriptions and examples . AISFG model is based on domain-specific labels, which is not capable of transferring to new domains with little or no data. |
| Approach: | They propose a model with a query template that incorporates domain descriptions, slot descriptions, and examples with context. |
| Outcome: | Experimental results show that the proposed model outperforms state-of-the-art approaches in zero/few-shot slot filling task. |
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to learn representations from text often reflect social biases . previous methods rely on pre-specified direction or suffer from unstable training . |
| Approach: | They propose an adversarial disentangled debiasing model to decouple social bias attributes from intermediate representations trained on the main task. |
| Outcome: | The proposed model decouples social bias attributes from intermediate representations trained on the main task. |
VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding (2025.emnlp-main)
Copied to clipboard
Zhaowei Liu, Xin Guo, Haotian Xia, Lingfeng Zeng, Fangqi Lou, Jinyi Niu, Mengping Li, Qi Qi, Jiahuan Li, Wei Zhang, Yinglong Wang, Weige Cai, Weining Shen, Liwen Zhang
| Challenge: | Existing benchmarks focus on text comprehension, but MLLMs lack the ability to integrate visual data over financial visuals. |
| Approach: | They evaluate 21 state-of-the-art multimodal large language models in a zero-shot setting . they use an annotated question–answer pair from eight common financial image modalities . |
| Outcome: | The new benchmark outperforms existing models but trailed financial experts by 14 percentage points. |
Learning Multilingual Sentence Representations with Cross-lingual Consistency Regularization (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Experimental results on multilingual similarity search and bitext mining tasks show the effectiveness of our approach. |
| Approach: | They propose a multilingual sentence representation model that aligns different languages in a shared representation space. |
| Outcome: | The proposed model performs better than LASER3 on similarity searches and bitext mining tasks. |
Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing (2021.acl-long)
Copied to clipboard
| Challenge: | Discourse dependency parsing is a task that requires a large amount of training data, but there is little research on it. |
| Approach: | They propose to adapt unsupervised syntactic dependency parsing methods for unsupervised discourse dependency parses using unlabeled training data. |
| Outcome: | The proposed methods outperform existing methods in semi-supervised and supervised settings and outperformed existing methods. |
Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to improve zero-shot translation performance by learning language-agnostic representations and maximizing cross-lingual transfer have been proposed. |
| Approach: | They propose a cross-lingual consistency regularization to bridge the representation gap between different languages and boost zero-shot translation performance. |
| Outcome: | The proposed model improves translation performance on low-resource and high-res benchmarks and closes the sentence representation gap and aligns the representation space. |
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)
Copied to clipboard
Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang
| Challenge: | Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored. |
| Approach: | They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities. |
| Outcome: | The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities. |
Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to slot filling only learn surface mapping of slot types between D S and D T and get poor generalization capability or robustness. |
| Approach: | They propose a generative zero-shot prompt learning framework for cross-domain slot filling which improves generalization and robustness than previous work. |
| Outcome: | The proposed framework improves generalization and robustness on unseen slots and an efficient prompt tuning strategy boosts performance. |