Papers by Yizhu Liu
Multi-turn Response Selection using Dialogue Dependency Relations (2020.emnlp-main)
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| Challenge: | Existing models for multi-turn response selection ignore the dependencies between the turns. |
| Approach: | They propose a dialogue extraction algorithm to transform a dialog history into threads based on their dependency relations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on DSTC7 and DSTF8* with competitive results on UbuntuV2 . |
Opinion Summarization by Weak-Supervision from Mix-structured Data (2022.emnlp-main)
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| Challenge: | Existing methods for opinion summarization of multiple reviews lack reference summaries . OAs and ISs are often mismatched between review input and summary . |
| Approach: | They propose a method to generate mixed-structured synthetic training data for opinion summarization. |
| Outcome: | The proposed method outperforms existing methods on Yelp, Amazon and RottenTomatos datasets. |
Towards a Unified Multi-Dimensional Evaluator for Text Generation (2022.emnlp-main)
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Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, Jiawei Han
| Challenge: | Existing evaluation frameworks for natural language generation are dominated by similarity-based metrics. |
| Approach: | They propose a multi-dimensional evaluator for natural language generation that integrates multiple dimensions into one evaluer. |
| Outcome: | The proposed evaluator improves on three typical NLG tasks and improves with external knowledge. |
Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)
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Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han
| Challenge: | Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs. |
| Approach: | They propose a system that extracts chemical reactions directly from raw scientific PDFs. |
| Outcome: | The proposed system can extract chemical reactions from raw scientific PDFs. |
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions (2023.emnlp-main)
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Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han
| Challenge: | Recent advances in large language models (LLMs) have produced models that exhibit remarkable performance across a variety of NLP tasks. |
| Approach: | They analyze a large-scale collection of user-GPT conversations to identify a significant gap between academic research in NLP and the needs of real-world NLP applications. |
| Outcome: | The proposed model outperforms existing models in a large-scale collection of user-GPT conversations and identifies a significant gap between the tasks that users frequently request from LLMs and the tasks commonly studied in academic research. |
Post-Training Dialogue Summarization using Pseudo-Paraphrasing (2022.findings-naacl)
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| Challenge: | Existing approaches to dialogue summarization use dialogue-specific features that require additional knowledge to recognize or make the models harder to tune. |
| Approach: | They propose to post-train pretrained language models to rephrase from dialogue to narratives and fine-tune them as usual. |
| Outcome: | The proposed approach outperforms existing models by summary quality and implementation costs. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model (2023.emnlp-main)
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| Challenge: | Existing work evaluates faithfulness using models trained on related tasks or in-domain synthetic data. |
| Approach: | They propose to do zero-shot faithfulness evaluation with a foundation language model. |
| Outcome: | The proposed model outperforms ChatGPT on faithfulness and inconsistency detection with 24x fewer parameters and is competitive with existing models. |
In-sample Curriculum Learning by Sequence Completion for Natural Language Generation (2023.acl-long)
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| Challenge: | Existing work on curriculum learning rely on task-specific expertise and cannot generalize to different tasks. |
| Approach: | They propose to do in-sample curriculum learning for natural language generation tasks using human-crafted rules and a numeric score for each sample based on domain expertise to rank the model. |
| Outcome: | The proposed learning strategy generalizes well to different tasks and achieves significant improvements over baselines. |
Unsupervised Multi-Granularity Summarization (2022.findings-emnlp)
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Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, Jiawei Han
| Challenge: | Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines. |
| Approach: | They propose to rank events by their salience and annotate a benchmark for GranuSum that contains multiple summaries at different granularities for each document cluster. |
| Outcome: | The proposed framework is capable of producing multi-granular summaries in unsupervised manner over strong baselines. |
Length Control in Abstractive Summarization by Pretraining Information Selection (2022.acl-long)
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| Challenge: | Existing length-controllable summarization models generate summaries as long as training data . current methods only control lengths at decoding stage, but adapt to desired lengths . |
| Approach: | They propose a length-aware attention mechanism to adapt the encoding of the source based on the desired length. |
| Outcome: | The proposed method produces high-quality summaries with desired lengths and even those short lengths never seen in the training data. |
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) can't generate summaries of desired lengths due to space or length constraints. |
| Approach: | They propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. |
| Outcome: | The proposed model outperforms baseline models in terms of ROUGE score, length variations and semantic similarity. |
Reference-free Summarization Evaluation via Semantic Correlation and Compression Ratio (2022.naacl-main)
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| Challenge: | Existing evaluation metrics for summarization use human annotations as reference. |
| Approach: | They propose a new automatic reference-free evaluation metric that compares semantic distribution between source document and summary by pretrained language models and considers summary compression ratio. |
| Outcome: | The proposed metric is more consistent with human evaluation in terms of coherence, consistency, relevance and fluency. |