Yuexian Hou


Mining Effective Features Using Quantum Entropy for Humor Recognition
Yang Liu | Yuexian Hou
Findings of the Association for Computational Linguistics: EACL 2023

Humor recognition has been extensively studied with different methods in the past years. However, existing studies on humor recognition do not understand the mechanisms that generate humor. In this paper, inspired by the incongruity theory, any joke can be divided into two components (the setup and the punchline). Both components have multiple possible semantics, and there is an incongruous relationship between them. We use density matrices to represent the semantic uncertainty of the setup and the punchline, respectively, and design QE-Uncertainty and QE-Incongruity with the help of quantum entropy as features for humor recognition. The experimental results on the SemEval2021 Task 7 dataset show that the proposed features are more effective than the baselines for recognizing humorous and non-humorous texts.


CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling
Jinfeng Zhou | Bo Wang | Zhitong Yang | Dongming Zhao | Kun Huang | Ruifang He | Yuexian Hou
Proceedings of the 29th International Conference on Computational Linguistics

Conversational recommendation systems (CRS) aim to determine a goal item by sequentially tracking users’ interests through multi-turn conversation. In CRS, implicit patterns of user interest sequence guide the smooth transition of dialog utterances to the goal item. However, with the convenient explicit knowledge of knowledge graph (KG), existing KG-based CRS methods over-rely on the explicit separate KG links to model the user interests but ignore the rich goal-aware implicit interest sequence patterns in a dialog. In addition, interest sequence is also not fully used to generate smooth transited utterances. We propose CR-GIS with a parallel star framework. First, an interest-level star graph is designed to model the goal-aware implicit user interest sequence. Second, a hierarchical Star Transformer is designed to guide the multi-turn utterances generation with the interest-level star graph. Extensive experiments verify the effectiveness of CR-GIS in achieving more accurate recommended items with more fluent and coherent dialog utterances.

TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph
Zhitong Yang | Bo Wang | Jinfeng Zhou | Yue Tan | Dongming Zhao | Kun Huang | Ruifang He | Yuexian Hou
Proceedings of the 29th International Conference on Computational Linguistics

Target-oriented dialog aims to reach a global target through multi-turn conversation. The key to the task is the global planning towards the target, which flexibly guides the dialog concerning the context. However, existing target-oriented dialog works take a local and greedy strategy for response generation, where global planning is absent. In this work, we propose global planning for target-oriented dialog on a commonsense knowledge graph (KG). We design a global reinforcement learning with the planned paths to flexibly adjust the local response generation model towards the global target. We also propose a KG-based method to collect target-oriented samples automatically from the chit-chat corpus for model training. Experiments show that our method can reach the target with a higher success rate, fewer turns, and more coherent responses.

Aligning Recommendation and Conversation via Dual Imitation
Jinfeng Zhou | Bo Wang | Minlie Huang | Dongming Zhao | Kun Huang | Ruifang He | Yuexian Hou
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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 of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (Dual Imitation for Conversational Recommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics.


CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs
Jinfeng Zhou | Bo Wang | Ruifang He | Yuexian Hou
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Although paths of user interests shift in knowledge graphs (KGs) can benefit conversational recommender systems (CRS), explicit reasoning on KGs has not been well considered in CRS, due to the complex of high-order and incomplete paths. We propose CRFR, which effectively does explicit multi-hop reasoning on KGs with a conversational context-based reinforcement learning model. Considering the incompleteness of KGs, instead of learning single complete reasoning path, CRFR flexibly learns multiple reasoning fragments which are likely contained in the complete paths of interests shift. A fragments-aware unified model is then designed to fuse the fragments information from item-oriented and concept-oriented KGs to enhance the CRS response with entities and words from the fragments. Extensive experiments demonstrate CRFR’s SOTA performance on recommendation, conversation and conversation interpretability.


A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis
Shuqin Gu | Lipeng Zhang | Yuexian Hou | Yin Song
Proceedings of the 27th International Conference on Computational Linguistics

Aspect-level sentiment analysis aims to distinguish the sentiment polarity of each specific aspect term in a given sentence. Both industry and academia have realized the importance of the relationship between aspect term and sentence, and made attempts to model the relationship by designing a series of attention models. However, most existing methods usually neglect the fact that the position information is also crucial for identifying the sentiment polarity of the aspect term. When an aspect term occurs in a sentence, its neighboring words should be given more attention than other words with long distance. Therefore, we propose a position-aware bidirectional attention network (PBAN) based on bidirectional GRU. PBAN not only concentrates on the position information of aspect terms, but also mutually models the relation between aspect term and sentence by employing bidirectional attention mechanism. The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our proposed PBAN model.


Reinforcing the Topic of Embeddings with Theta Pure Dependence for Text Classification
Ning Xing | Yuexian Hou | Peng Zhang | Wenjie Li | Dawei Song
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


Event-Based Hyperspace Analogue to Language for Query Expansion
Tingxu Yan | Tamsin Maxwell | Dawei Song | Yuexian Hou | Peng Zhang
Proceedings of the ACL 2010 Conference Short Papers


Exploiting the Role of Position Feature in Chinese Relation Extraction
Peng Zhang | Wenjie Li | Furu Wei | Qin Lu | Yuexian Hou
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Relation extraction is the task of finding pre-defined semantic relations between two entities or entity mentions from text. Many methods, such as feature-based and kernel-based methods, have been proposed in the literature. Among them, feature-based methods draw much attention from researchers. However, to the best of our knowledge, existing feature-based methods did not explicitly incorporate the position feature and no in-depth analysis was conducted in this regard. In this paper, we define and exploit nine types of position information between two named entity mentions and then use it along with other features in a multi-class classification framework for Chinese relation extraction. Experiments on the ACE 2005 data set show that the position feature is more effective than the other recognized features like entity type/subtype and character-based N-gram context. Most important, it can be easily captured and does not require as much effort as applying deep natural language processing.

A Novel Feature-based Approach to Chinese Entity Relation Extraction
Wenjie Li | Peng Zhang | Furu Wei | Yuexian Hou | Qin Lu
Proceedings of ACL-08: HLT, Short Papers