Papers by Xiaoyan Zhu
Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization (2022.emnlp-main)
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| Challenge: | Recent studies have shown that instruction tuning is effective in instruction learning for unseen tasks, but it relies on a large amount of human-annotated samples, which restricts its generalization. |
| Approach: | They propose an instruction tuning technique which fine-tunes a pre-trained language model on a massive collection of tasks described via human-craft instructions and then tests its generalization ability on unseen tasks. |
| Outcome: | The proposed method improves IT performance versus labeled data and training tasks by constructing pseudo-labeled data from unlabele . data is used to build a model that can learn from human instructions for zero-shot generalization on unseen tasks. |
Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training (2026.acl-long)
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| Challenge: | Large language models (LLMs) have impressive capabilities across a wide range of domains, but their generalpurpose pre-training objectives often leave them illsuited for specialized applications such as healthcare. |
| Approach: | They propose a perplexity-aware data scaling law that establishes a predictive relationship between the perplexities of domain-specific data and the test loss. |
| Outcome: | Experiments on medical and general-domain benchmarks show that the proposed scaling law consistently identifies near-optimal training subsets with significantly reduced data consumption. |
Latent Inter-User Difference Modeling for LLM Personalization (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly integrated into users’ daily lives, leading to a growing demand for personalized outputs. |
| Approach: | They propose a framework that models inter-user differences in the latent space instead of relying on language-based prompts. |
| Outcome: | The proposed framework outperforms baseline methods on personalized review generation. |
Generating Informative Responses with Controlled Sentence Function (P18-1)
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| Challenge: | Sentence function is a significant factor to achieve the purpose of the speaker, but has not been touched in large-scale conversation generation. |
| Approach: | They propose a model to generate informative responses with controlled sentence function using a latent variable and a type controller to deal with compatibility. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates responses with controlled sentence function and informative content. |
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation (2022.acl-long)
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| Challenge: | Existing reference-free metrics have obvious limitations for evaluating controlled text generation models. |
| Approach: | They propose an unsupervised reference-free metric which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks. |
| Outcome: | The proposed metric has higher correlations with human judgments while obtaining better generalization of evaluating generated texts from different models and with different qualities. |
EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning (2021.emnlp-main)
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| Challenge: | Existing knowledge-grounded conversation models lack knowledge that occurs in training data, resulting in incomplete knowledge generation. |
| Approach: | They propose an Entity-Agnostic Representation Learning method to introduce knowledge graphs to informative conversation generation using context of conversations and relational structure of knowledge graph. |
| Outcome: | The proposed model generates more informative, coherent, and natural responses than baseline models. |
An Operation Network for Abstractive Sentence Compression (C18-1)
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| Challenge: | Sentence compression is a natural language generation task that condenses a sentence . Delete-based models remove unimportant words from the source sentence and generate a shorter sentence if the source is not a word deletion problem. |
| Approach: | They propose a neural network approach for abstractive sentence compression . they model the sentence compression process as an editing procedure . |
| Outcome: | The proposed approach outperforms state-of-the-art models in the abstractive sentence compression field. |
DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering (2023.acl-long)
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| Challenge: | Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability. |
| Approach: | They propose a metric that evaluates natural language generation tasks as an instruction-style question answering task and utilizes instruction-tuned pre-trained language models without training on evaluation datasets. |
| Outcome: | The proposed metric achieves state-of-the-art performance in untrained metrics for evaluating text summarization and dialogue generation, which exhibits strong dimension-level / task-level generalization ability and interpretability. |
Building Multi-domain Dialog State Trackers from Single-domain Dialogs (2023.emnlp-main)
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| Challenge: | Existing multi-domain dialog state tracking models require significant manual effort to define domain relations and collect data. |
| Approach: | They propose a divide-and-conquer (DAC) DST paradigm and a multi-domain dialog synthesis framework to build multi- domain DST models from single-domain dialogues. |
| Outcome: | The proposed paradigm makes building multi-domain DST models easier on unseen domain combinations. |
KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation (2020.acl-main)
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| Challenge: | Existing knowledge-driven dialog data is limited due to the lack of dialog data which consists of multi-turn conversations on multiple topics and with knowledge annotations. |
| Approach: | They propose a Chinese multi-domain knowledge-driven conversation dataset which grounds the topics in multi-turn conversations to knowledge graphs. |
| Outcome: | The proposed dataset can be enhanced by introducing background knowledge, but there is still a large space for leveraging knowledge to model multi-turn conversations for further research. |
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2022.findings-acl)
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Hao Sun, Guangxuan Xu, Jiawen Deng, Jiale Cheng, Chujie Zheng, Hao Zhou, Nanyun Peng, Xiaoyan Zhu, Minlie Huang
| Challenge: | Dialogue safety problems severely limit the real-world deployment of generative conversational models. |
| Approach: | They propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings. |
| Outcome: | The proposed taxonomy captures unsafe behaviors in human-bot dialogue settings with rich context-sensitive unsafe examples. |
CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset (2020.tacl-1)
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| Challenge: | Despite the significant contributions to the community, there is still a gap between existing dialogue corpora and real-life human dialogue data. |
| Approach: | They propose to develop Chinese cross-domain wizard-of-oz task-oriented dataset CrossWOZ with rich annotations of dialogue states and dialogue acts on both user and system sides. |
| Outcome: | The proposed dataset contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. |
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model (D19-1)
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| Challenge: | Existing methods for data-to-text generation are insufficient to produce long and diverse texts. |
| Approach: | They propose a planning-based hierarchical variational model that plans a sequence of groups and then realizes each sentence conditioned on the planning result and the previously generated context. |
| Outcome: | The proposed model outperforms state-of-the-art models in long and diverse text generation. |
NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer (2021.findings-acl)
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| Challenge: | Autoregressive text style transfer models often ignore part of the source sentence and generate some irrelevant words with strong styles. |
| Approach: | They propose a non-autoregressive generator for unsupervised text style transfer which explicitly models word alignments to suppress irrelevant words. |
| Outcome: | The proposed generator significantly improves performance and provides explainable word alignments. |
A Semantic-based Method for Unsupervised Commonsense Question Answering (2021.acl-long)
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| Challenge: | Existing methods to score candidates without labeled task data are difficult to use . e.g., pre-trained language models can be easily affected by irrelevant factors . |
| Approach: | They propose a method that generates plausible answers with generative models and uses them to select the correct answer. |
| Outcome: | The proposed method achieves the best results in unsupervised situations. |
Learning Goal-oriented Dialogue Policy with opposite Agent Awareness (2020.aacl-main)
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| Challenge: | Existing approaches for goal-oriented dialogue policy learning focus on the target agent policy and treat the opposite agent policy as part of the environment. |
| Approach: | They propose a framework for policy learning in goal-oriented dialogues that uses the opposite agent's policy estimation to improve the target agent by regarding it as part of the target policy. |
| Outcome: | The proposed framework shows superior performance over state-of-the-art models on cooperative and competitive dialogue tasks. |
Rethinking and Refining the Distinct Metric (2022.acl-short)
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| Challenge: | Existing methods for calculating distinct scores have evident biases that assign higher penalties to longer sequences. |
| Approach: | They propose to scale the number of distinct tokens based on their expectations. |
| Outcome: | The proposed metric removes evident biases in the original distinct score . the proposed meter correlates better with human judgment in evaluating response diversity . |
Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing approaches that integrate commonsense knowledge into pre-trained language models simply transfer relational knowledge while ignoring rich connections within the knowledge graph. |
| Approach: | They propose a method that leverages structural and semantic information of the knowledge graph to generate commonsense-aware text. |
| Outcome: | The proposed method outperforms baseline models on three text generation tasks that require reasoning over commonsense knowledge. |
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs (2021.findings-acl)
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| Challenge: | Existing pre-trained models for knowledgegraph-to-text generation ignore graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments. |
| Approach: | They propose a graph-text joint representation learning model called JointGT which incorporates a structure-aware semantic aggregation module into each Transformer layer to preserve the graph structure. |
| Outcome: | The proposed model achieves state-of-the-art performance on various KG-to-text datasets. |
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation (2020.tacl-1)
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| Challenge: | Existing models for story generation suffer from repetition, logic conflicts, and lack of long-range coherence . |
| Approach: | They propose to utilize commonsense knowledge from external knowledge bases to generate reasonable stories by multi-task learning. |
| Outcome: | The proposed model can generate more reasonable stories than state-of-the-art models, compared with existing models, showing that it can capture useful semantic and syntactic features. |
An Interpretable Reasoning Network for Multi-Relation Question Answering (C18-1)
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| Challenge: | Existing models for multi-relation question answering require elaborated analysis and reasoning over multiple fact triples in knowledge base. |
| Approach: | They propose a model that employs an interpretable hop-by-hop reasoning process for question answering . it decides which part of an input question should be analyzed at each hop and then drives next-hop thinking . |
| Outcome: | The proposed model yields state-of-the-art results on two datasets. |
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)
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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang
| Challenge: | ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets. |
| Approach: | They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation. |
| Outcome: | The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models. |
Continual Prompt Tuning for Dialog State Tracking (2022.acl-long)
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| Challenge: | Existing methods to train a model on a sequence of tasks are not efficient enough to mitigate catastrophic forgetting. |
| Approach: | They propose a parameter-efficient framework that prevents forgetting and enables knowledge transfer between tasks by learning and freezing a pre-trained model. |
| Outcome: | The proposed framework avoids forgetting and enables knowledge transfer between tasks. |
SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge (2020.emnlp-main)
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| Challenge: | Existing pre-trained models neglect to consider linguistic knowledge of texts . existing models neglect linguistic information, which is important for sentiment analysis . |
| Approach: | They propose a model that introduces word-level linguistic knowledge into pre-trained models to enhance sentiment analysis by querying SentiWordNet to acquire sentiment polarity. |
| Outcome: | The proposed model obtains state-of-the-art performance on a variety of sentiment analysis tasks. |
Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have revolutionized open-domain dialogue agents but face challenges in multi-character role-playing (MCRP) scenarios. |
| Approach: | They propose a framework for efficient multi-character role-playing that employs a dynamic low-rank adapter strategy and distinct LoRA blocks for each character. |
| Outcome: | Neeko employs a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to diverse characters. |
ARAML: A Stable Adversarial Training Framework for Text Generation (D19-1)
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| Challenge: | Existing generative adversarial networks suffer from the instability of reinforcement learning training algorithms such as policy gradient, leading to unstable performance. |
| Approach: | They propose a framework where the discriminator assigns rewards to samples acquired from a stationary distribution near the data rather than the generator’s distribution. |
| Outcome: | The proposed framework outperforms state-of-the-art text GANs with a more stable training process. |