no code implementations • COLING 2022 • Jamell Dacon, Haochen Liu, Jiliang Tang
In this work, we conduct a pioneering study of the English variety use of African American English (AAE) in NLI task.
no code implementations • 6 Jan 2025 • Zaiyi Zheng, Yushun Dong, Song Wang, Haochen Liu, Qi Wang, Jundong Li
Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC).
1 code implementation • 19 Jun 2024 • Haochen Liu, Song Wang, Chen Chen, Jundong Li
To overcome these challenges, we propose SAFER (Subgraph Adaptation for Few-shot Relational Reasoning), a novel approach that effectively adapts the information in contextualized graphs to various subgraphs generated from support and query triplets to perform the prediction.
1 code implementation • 19 Jun 2024 • Haochen Liu, Song Wang, Yaochen Zhu, Yushun Dong, Jundong Li
In addition, LLMs tend to pick only knowledge with direct semantic relationship with the input text, while potentially useful knowledge with indirect semantics can be ignored.
no code implementations • 4 Feb 2024 • Haochen Liu, Zhiyu Huang, Wenhui Huang, Haohan Yang, Xiaoyu Mo, Chen Lv
First, we introduce marginal-conditioned occupancy prediction to align joint occupancy with agent-wise perceptions.
1 code implementation • 19 Jan 2024 • Xu Weng, KV Ling, Haochen Liu, Kun Cao
The pseudorange error is one of the root causes of localization inaccuracy in GPS.
no code implementations • 24 Oct 2023 • Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, Jundong Li
Afterward, we provide an innovative taxonomy of KME techniques based on how the new knowledge is introduced into pre-trained LLMs, and investigate existing KME strategies while analyzing key insights, advantages, and limitations of methods from each category.
1 code implementation • 16 Sep 2023 • Xu Weng, Keck Voon Ling, Haochen Liu
We present a neural network for mitigating biased errors in pseudoranges to improve localization performance with data collected from mobile phones.
no code implementations • 15 Sep 2023 • Haochen Liu, Sai Krishna Rallabandi, Yijing Wu, Parag Pravin Dakle, Preethi Raghavan
Self-training has recently emerged as an economical and efficient technique for developing sentiment analysis models by leveraging a small amount of labeled data and a large amount of unlabeled data.
no code implementations • 16 Feb 2023 • Xiao Chen, Wenqi Fan, Jingfan Chen, Haochen Liu, Zitao Liu, Zhaoxiang Zhang, Qing Li
Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i. e., clicked by a user) and negative items (i. e., obtained by negative sampling).
1 code implementation • 24 Aug 2022 • Haochen Liu, Zhiyu Huang, Xiaoyu Mo, Chen Lv
Decision-making for urban autonomous driving is challenging due to the stochastic nature of interactive traffic participants and the complexity of road structures.
1 code implementation • 31 Jul 2022 • Haochen Liu, Zhiyu Huang, Chen Lv
Therefore, this paper proposes a novel Multi-modal Hierarchical Transformer network that fuses the vectorized (agent motion) and visual (scene flow, map, and occupancy) modalities and jointly predicts the flow and occupancy of the scene.
no code implementations • ACL 2022 • Haochen Liu, Joseph Thekinen, Sinem Mollaoglu, Da Tang, Ji Yang, Youlong Cheng, Hui Liu, Jiliang Tang
We conduct experiments on both synthetic and real-world datasets.
no code implementations • 12 Jul 2021 • Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil K. Jain, Jiliang Tang
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society.
no code implementations • 12 Jun 2021 • Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang
Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.
no code implementations • Findings (ACL) 2021 • Haochen Liu, Wei Jin, Hamid Karimi, Zitao Liu, Jiliang Tang
The results show that the text classification models trained under our proposed framework outperform traditional models significantly in terms of fairness, and also slightly in terms of classification performance.
no code implementations • COLING 2020 • Haochen Liu, Zitao Liu, Zhongqin Wu, Jiliang Tang
The automatic evaluation for school assignments is an important application of AI in the education field.
1 code implementation • EMNLP 2020 • Haochen Liu, Wentao Wang, Yiqi Wang, Hui Liu, Zitao Liu, Jiliang Tang
Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.
no code implementations • 26 Jun 2020 • Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long
Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.
1 code implementation • 17 Jun 2020 • Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, Jiliang Tang
Thus, we seek to harness SSL for GNNs to fully exploit the unlabeled data.
no code implementations • 27 May 2020 • Haochen Liu, Zhiwei Wang, Tyler Derr, Jiliang Tang
Recently, neural network based dialogue systems have become ubiquitous in our increasingly digitalized society.
no code implementations • 16 May 2020 • Gale Yan Huang, Jiahao Chen, Haochen Liu, Weiping Fu, Wenbiao Ding, Jiliang Tang, Songfan Yang, Guoliang Li, Zitao Liu
Asking questions is one of the most crucial pedagogical techniques used by teachers in class.
1 code implementation • COLING 2020 • Haochen Liu, Jamell Dacon, Wenqi Fan, Hui Liu, Zitao Liu, Jiliang Tang
In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models.
3 code implementations • 17 Sep 2019 • Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, Anil K. Jain
In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for the three popular data types, i. e., images, graphs and text.
no code implementations • 13 Sep 2019 • Haochen Liu, Tyler Derr, Zitao Liu, Jiliang Tang
Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations.