no code implementations • EMNLP 2020 • Mi Zhang, Tieyun Qian
Moreover, we build a concept hierarchy on both the syntactic and lexical graphs for differentiating various types of dependency relations or lexical word pairs.
no code implementations • EMNLP 2020 • Zhuang Chen, Tieyun Qian
Aspect term extraction (ATE) aims to extract aspect terms from a review sentence that users have expressed opinions on.
1 code implementation • 11 Jun 2023 • Ting Zhang, Zhuang Chen, Ming Zhong, Tieyun Qian
It is a challenging task since the recognition of the emotion in one utterance involves many complex factors, such as the conversational context, the speaker's background, and the subtle difference between emotion labels.
no code implementations • 24 May 2023 • Yongqi Li, Mayi Xu, Xin Miao, Shen Zhou, Tieyun Qian
Based on this framework, we 1) investigate the strengths and weaknesses of LLMs as the counterfactual generator, and 2) disclose the factors that affect LLMs when generating counterfactuals, including both the intrinsic properties of LLMs and prompt designing.
no code implementations • 3 May 2023 • Wanli Li, Tieyun Qian
However, current generative models lack the optimization process of model generalization on different tasks during training, and thus have limited generalization capability.
2 code implementations • 13 Feb 2023 • Yongqi Li, Yu Yu, Tieyun Qian
Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the overdetected false spans at the span detection stage and the inaccurate and unstable prototypes at the type classification stage remain to be challenging problems.
Ranked #2 on Few-shot NER on Few-NERD (INTRA)
no code implementations • 22 Feb 2022 • Mi Zhang, Tieyun Qian, Ting Zhang
In this paper, we formulate the problem of automatically generating CAD for RC tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities.
1 code implementation • 2 Dec 2021 • Wanli Li, Tieyun Qian
Specifically, we first let the teachers correspond to the multiple models and select the samples in the intersection set of the last iteration in SSRE methods to augment labeled data as usual.
1 code implementation • ACL 2021 • Zhuang Chen, Tieyun Qian
Existing methods solve this problem by associating aspect terms with pivot words (we call this passive domain adaptation because the transfer of aspect terms relies on the links to pivots).
no code implementations • 28 Jul 2021 • Mi Zhang, Tieyun Qian
Specifically, we first develop a multi-scale convolutional neural network to aggregate the non-successive mainstays in the lexical sequence.
no code implementations • 28 Jun 2021 • Tieyun Qian, Yile Liang, Qing Li, Xuan Ma, Ke Sun, Zhiyong Peng
Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems.
no code implementations • 4 Jan 2021 • Yile Liang, Tieyun Qian
Specifically, we encode domain level diversity by adaptively balancing accurate recommendation in the conventional branch and diversified recommendation in the adaptive branch of a bilateral branch network.
no code implementations • 22 Oct 2020 • Wanli Li, Tieyun Qian
To tackle this limitation, we propose to build the connection between the unlabeled data and the labeled ones rather than directly mapping the unlabeled samples to the classes.
1 code implementation • ACL 2020 • Zhuang Chen, Tieyun Qian
Aspect-based sentiment analysis (ABSA) involves three subtasks, i. e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +5
no code implementations • 28 Dec 2019 • Tieyun Qian, Yile Liang, Qing Li
More importantly, for a cold start user/item that does not have any interactions, such methods are unable to learn the preference embedding of the user/item since there is no link to this user/item in the graph.
no code implementations • 16 Dec 2019 • Ke Sun, Tieyun Qian
We then generalize recent advancements in translation model from sequences of words in two languages to sequences of items and contexts in recommender systems.
1 code implementation • ACL 2019 • Zhuang Chen, Tieyun Qian
In this paper, we propose a Transfer Capsule Network (TransCap) model for transferring document-level knowledge to aspect-level sentiment classification.
no code implementations • COLING 2018 • Peisong Zhu, Tieyun Qian
In our model, a main memory is used to capture the important context words for sentiment classification.
no code implementations • COLING 2018 • Zhenni You, Tieyun Qian, Bing Liu
With the abundant attributes in existing entities and knowledge in other domains, we successfully solve the problem of data scarcity in the cold-start settings.