Papers by Jianzhong Qi
PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages (D18-1)
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| Challenge: | Using a hierarchical model, we aim to extract all the publication strings from a researcher's homepage. |
| Approach: | They propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF and an alternating training method for training the model. |
| Outcome: | The proposed model outperforms the state-of-the-art models by 11.8% in F1-score on real data. |
Grouped-Attention for Content-Selection and Content-Plan Generation (2021.findings-emnlp)
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| Challenge: | Recent neural data-to-text generation models explicitly learn content-plan given a set of attributes as input. |
| Approach: | They propose a neural content-planner that captures local and global contexts . they use a token-level attention constrained within each input attribute . |
| Outcome: | The proposed model outperforms competitors by 4.92%, 4.70%, and 16.56% on real-world datasets. |
Factual Dialogue Summarization via Learning from Large Language Models (2025.coling-main)
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| Challenge: | Existing models generate fluent and coherent summaries, but inconsistencies can be found in generated summary. |
| Approach: | They propose to use symbolic knowledge distillation to improve the factual consistency of smaller pretrained models for dialogue summarization. |
| Outcome: | The proposed model outperforms baseline models in BART, PEGASUS, and Flan-T5 in factual consistency and accuracy. |
SPIDE: Serial and Parallel Intertwined Speculative Decoding (2026.findings-acl)
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Wenru Xu, Peixuan Xu, Ziqi Yang, Ming Hu, Zihui Wang, Jianzhong Qi, Rongshan Yu, Xiaoliang Fan, Cheng Wang
| Challenge: | Speculative decoding (SD) is a training-free SD framework that orchestrates dynamic alternation combining serial dynamic drafting with parallel draft verification. |
| Approach: | They propose a serial and parallel intertwined speculative DEcoding framework that orchestrates dynamic alternation combining serial dynamic drafting and parallel draft verification. |
| Outcome: | The proposed framework accelerates inference while reducing the LLM usage costs. |
GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data (P18-1)
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| Challenge: | Knowledge bases are becoming an enabling resource for many applications including Q&A systems, recommender systems, and summarization tools. |
| Approach: | They propose a system to translate RDF triples into natural sentences using an encoder-decoder framework. |
| Outcome: | The proposed model outperforms the baseline model by 17.6%, 6.0%, and 16.4% in terms of BLEU, METEOR, and TER scores. |
Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation (2020.acl-main)
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| Challenge: | Existing methods for dialogue policy optimization do not provide sufficient supervision signals at the end of dialogues. |
| Approach: | They propose to learn from state-action pairs of an optimal policy to provide turn-by-turn rewards. |
| Outcome: | The proposed approach outperforms competitive policy learning baselines on a benchmark multi-domain dataset. |
Latent Reasoning for Low-Resource Question Generation (2021.findings-acl)
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| Challenge: | Existing approaches to multihop question generation require extensive data collection and decomposition. |
| Approach: | They propose a generative approach that optimizes the two-phase model without question decomposition data. |
| Outcome: | The proposed approach outperforms baselines on HOTPOTQA, a benchmark multi-hop question answering dataset. |
Generalizable and Explainable Dialogue Generation via Explicit Action Learning (2020.findings-emnlp)
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| Challenge: | Conditioned response generation for task-oriented dialogues implicitly optimizes task completion and language quality. |
| Approach: | They propose to learn natural language actions that represent utterances as a span of words. |
| Outcome: | The proposed approach outperforms latent action baselines on a multi-domain dataset. |
Evaluating Document Coherence Modeling (2021.tacl-1)
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| Challenge: | a new study examines pretrained language models' ability to model discourse and pragmatic phenomena. |
| Approach: | They propose a sentence intrusion detection task using a dictionary dataset . they show that pretrained LMs perform impressively in in-domain evaluation . |
| Outcome: | The proposed dataset shows that pretrained LMs perform impressively in in-domain evaluation, but experience a substantial drop in the cross-domain setting, indicating limited generalization capacity. |
Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation (2025.emnlp-main)
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| Challenge: | Knowledge base question answering (KBQA) aims to answer user questions in natural language using rich human knowledge stored in large KBs. |
| Approach: | They propose a model that injects schema contexts into entity retrieval and logical form generation to enhance generalizability. |
| Outcome: | The proposed model outperforms state-of-the-art models on two commonly used benchmark datasets across a variety of test settings. |
Modelling Uncertainty in Collaborative Document Quality Assessment (D19-55)
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| Challenge: | Existing work on document quality assessment relies on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task. |
| Approach: | They propose to use Gaussian processes and random forests to measure the uncertainty in document quality predictions. |
| Outcome: | The proposed methods can predict the quality of Wikipedia articles while providing an estimate of uncertainty when there is inconsistency in the quality labels from the contributors. |
Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization (2023.acl-long)
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| Challenge: | Existing work on factual inconsistency in abstractive summarization addresses this problem. |
| Approach: | They propose a dataset with fine-grained factual error annotations named DIASUMFACT and an unsupervised model named ENDERANKER. |
| Outcome: | The proposed model performs on par with the state-of-the-art models while requiring fewer resources. |
Neural Relation Extraction for Knowledge Base Enrichment (P19-1)
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| Challenge: | Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment. |
| Approach: | They propose an end-to-end relation extraction model for knowledge base enrichment based on a neural encoder-decoder model . they propose to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end to end manner. |
| Outcome: | The proposed model outperforms state-of-the-art baselines by 15.51% and 8.38% on two real-world datasets. |