Minlie Huang
23 papers in the PaperMetrix corpus
Papers by this author
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Encoding Syntactic Knowledge in Neural Networks for Sentiment Classification
2017 · ACM Transactions on Information Systems
Phrase/Sentence representation is one of the most important problems in natural language processing. Many neural network models such as Convolutional Neural Network (CNN), Recursive Neural Network (RNN), and Long Short-Term Memory (LSTM) have been proposed …
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Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond
2020 · arXiv (Cornell University)
Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense. …
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UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation
2020
Despite the success of existing referenced metrics (e.g., BLEU and MoverScore), they correlate poorly with human judgments for openended text generation including story or dialog generation because of the notorious oneto-many issue: there are many …
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Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence
2021 · arXiv (Cornell University)
Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation. Despite the success in modeling intra-sentence coherence, existing generation models (e.g., BART) still struggle …
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KuiLeiXi: a Chinese Open-Ended Text Adventure Game
2021
Yadong Xi, Xiaoxi Mao, Le Li, Lei Lin, Yanjiang Chen, Shuhan Yang, Xuhan Chen, Kailun Tao, Zhi Li, Gongzheng Li, Lin Jiang, Siyan Liu, Zeng Zhao, Minlie Huang, Changjie Fan, Zhipeng Hu. Proceedings of the …
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On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark
2022 · Findings of the Association for Computational Linguistics: ACL 2022
Dialogue safety problems severely limit the real-world deployment of neural conversational models and have attracted great research interests recently. However, dialogue safety problems remain under-defined and the corresponding dataset is scarce. We propose a taxonomy …
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COLD: A Benchmark for Chinese Offensive Language Detection
2022 · arXiv (Cornell University)
Offensive language detection is increasingly crucial for maintaining a civilized social media platform and deploying pre-trained language models. However, this task in Chinese is still under exploration due to the scarcity of reliable datasets. To …
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CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
2022
Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers’ personalities and emotions after cognitive processing have an important influence on conversation. However, …
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CEM: Commonsense-aware Empathetic Response Generation
2021 · arXiv (Cornell University)
A key trait of daily conversations between individuals is the ability to express empathy towards others, and exploring ways to implement empathy is a crucial step towards human-like dialogue systems. Previous approaches on this topic …
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Aligning Recommendation and Conversation via Dual Imitation
2022
Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage …
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ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving
2023 · arXiv (Cornell University)
Large language models have made significant progress in various language tasks, yet they still struggle with complex mathematics. In this paper, we propose ToRA a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical …
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Guiding not Forcing: Enhancing the Transferability of Jailbreaking Attacks on LLMs via Removing Superfluous Constraints
2025 · arXiv (Cornell University)
Jailbreaking attacks can effectively induce unsafe behaviors in Large Language Models (LLMs); however, the transferability of these attacks across different models remains limited. This study aims to understand and enhance the transferability of gradient-based jailbreaking …
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Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network
2015
Qiao Qian, Bo Tian, Minlie Huang, Yang Liu, Xuan Zhu, Xiaoyan Zhu. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume …
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Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
2017 · arXiv (Cornell University)
Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose …
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Reinforcement Learning for Relation Classification from Noisy Data
2018 · arXiv (Cornell University)
Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair. Such methods, performing classification at the bag …
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Commonsense Knowledge Aware Conversation Generation with Graph Attention
2018
Commonsense knowledge is vital to many natural language processing tasks. In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. Given a …
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Personalized Dialogue Generation with Diversified Traits
2019 · arXiv (Cornell University)
Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this …
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Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
2018 · Proceedings of the AAAI Conference on Artificial Intelligence
Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose …
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A Hierarchical Framework for Relation Extraction with Reinforcement Learning
2019 · Proceedings of the AAAI Conference on Artificial Intelligence
Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modeled. This paper presents a novel paradigm to deal …
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Augmenting End-to-End Dialogue Systems With Commonsense Knowledge
2018 · Proceedings of the AAAI Conference on Artificial Intelligence
Building dialogue systems that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human utterances in an …
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Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
2019
Zhihong Shao, Minlie Huang, Jiangtao Wen, Wenfei Xu, Xiaoyan Zhu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation
2020 · Transactions of the Association for Computational Linguistics
Story generation, namely, generating a reasonable story from a leading context, is an important but challenging task. In spite of the success in modeling fluency and local coherence, existing neural language generation models (e.g., GPT-2) …
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SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge
2020
Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, which can promote language understanding in NLP tasks. To benefit the downstream tasks in sentiment analysis, we propose a …