Papers by Jiawei Ren
Facet-Aware Evaluation for Extractive Summarization (2020.acl-main)
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| Challenge: | lexical overlap is a common evaluation metric for extractive summarization, but recent studies reveal its limitations. |
| Approach: | They propose a facet-aware evaluation setup for better assessment of information coverage in extractive summaries. |
| Outcome: | The proposed evaluation setup improves human correlation with extractive summarization datasets and improves comparative analysis. |
Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling (D18-1)
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| Challenge: | Existing efforts to train pre-trained language models have brought significant improvements to various NLP applications. |
| Approach: | They propose to compress bulky LMs while preserving useful information for a specific task. |
| Outcome: | The proposed method can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow. |
Hierarchical Text Classification with Reinforced Label Assignment (D19-1)
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| Challenge: | Existing hierarchical text classification methods make local decisions regarding labels or ignore hierarchy information during inference. |
| Approach: | They propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process. |
| Outcome: | The proposed method outperforms state-of-the-art methods on five datasets and four base models and achieves an average improvement of 33.4% over flat classifiers. |
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery (2020.emnlp-main)
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| Challenge: | Entity set expansion and synonym discovery are two critical NLP tasks that are often performed separately, without exploring their interdependencies. |
| Approach: | They propose a framework that enables two tasks to mutually enhance each other by including popular entities’ infrequent synonyms into the set, which boosts set expansion recall. |
| Outcome: | The proposed framework can be used to enhance two NLP tasks by including popular entities’ infrequent synonyms into the set, which boosts set expansion recall. |
Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)
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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠 林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang
| Challenge: | Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications. |
| Approach: | They propose a reliable strategy for domains to choose more robust LLMs for real-world applications. |
| Outcome: | The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications. |
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Recent studies on single-document summarization (SDS) benefit from advances in neural sequence learning, but they produce unsatisfactory results on multi-document summary (MDS). |
| Approach: | They propose a neural sequence learning method that unifies advanced neural SDS methods and statistical measures used in classical MDS. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmark MDS datasets. |
RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance (2024.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) have improved the open-domain QA’s performance, but how to efficiently handle enterprise-exclusive corpora and build domain-specific QA systems are still not studied for industrial applications. |
| Approach: | They propose a general and comprehensive framework based on Retrieval Augmented Generation (RAG) and facilitate the whole business process of establishing QA systems for IT operations and maintenance. |
| Outcome: | The proposed framework achieves superior results on two kinds of QA tasks. |
TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names (2021.naacl-main)
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| Challenge: | Hierarchical multi-label text classification (HMTC) aims to assign each text document to a set of relevant classes from a taxonomy. |
| Approach: | They propose to conduct HMTC based on only class surface names as supervision signals to mimic human experts. |
| Outcome: | The proposed framework outperforms the best existing method by 25% on two challenging datasets. |
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation (2021.emnlp-main)
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| Challenge: | Existing models for text-to-text generation do not explicitly focus on important concepts in the input and output. |
| Approach: | They propose a framework to automatically extract, denoise, and enforce important input concepts as lexical constraints. |
| Outcome: | The proposed framework performs comparably or better than its unconstrained counterpart on automatic metrics and receives better ratings in the human evaluation. |
Learning Named Entity Tagger using Domain-Specific Dictionary (D18-1)
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| Challenge: | Existing methods to build reliable named entity recognition systems require large amounts of manually-labeled training data. |
| Approach: | They propose a revised fuzzy CRF layer to handle tokens with multiple possible labels to address noisy distant supervision. |
| Outcome: | The proposed model can handle tokens with multiple possible labels under the traditional framework and improves on the existing model with a new Tie or Break scheme. |
End-to-End Reinforcement Learning for Automatic Taxonomy Induction (P18-1)
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| Challenge: | Existing methods for automating taxonomy induction often divide the problem into two subtasks . a novel end-to-end reinforcement learning approach is proposed to improve the accuracy of such methods. |
| Approach: | They propose an end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two public datasets of different domains. |
Adaptive and Robust Translation from Natural Language to Multi-model Query Languages (2025.acl-long)
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| Challenge: | Multi-model databases and polystore systems are increasingly studied for managing multi-model data holistically. |
| Approach: | They propose an adaptive Text-to-MMQL framework that includes a schema embedding module and an MMQl representation strategy to generate concise intermediate query formats with error correction in generated queries. |
| Outcome: | The proposed framework achieves over 9% accuracy improvement over baseline methods. |