Search Results for author: Yancheng He

Found 13 papers, 8 papers with code

HITMI&T at SemEval-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On PCL Detection

no code implementations SemEval (NAACL) 2022 Zihang Liu, Yancheng He, Feiqing Zhuang, Bing Xu

Respectively, for subtask 1, that is, to judge whether a sentence is PCL, the method of retraining the model with specific task data is adopted, and the method of splicing [CLS] and the keyword representation of the last three layers as the representation of the sentence; for subtask 2, that is, to judge the PCL type of the sentence, in addition to using the same method as task1, the method of selecting a special loss for Multi-label text classification is applied.

Multi Label Text Classification Multi-Label Text Classification +1

Using Auxiliary Tasks In Multimodal Fusion Of Wav2vec 2.0 And BERT For Multimodal Emotion Recognition

no code implementations27 Feb 2023 Dekai Sun, Yancheng He, Jiqing Han

For the difficulty of multimodal fusion, we use a K-layer multi-head attention mechanism as a downstream fusion module.

Multimodal Emotion Recognition

Modeling User Repeat Consumption Behavior for Online Novel Recommendation

1 code implementation5 Sep 2022 Yuncong Li, Cunxiang Yin, Yancheng He, Guoqiang Xu, Jing Cai, Leeven Luo, Sheng-hua Zhong

In this paper, we concentrate on recommending online novels to new users of an online novel reading platform, whose first visits to the platform occurred in the last seven days.

Aspect-Sentiment-Multiple-Opinion Triplet Extraction

1 code implementation14 Oct 2021 Fang Wang, Yuncong Li, Sheng-hua Zhong, Cunxiang Yin, Yancheng He

Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion term (opinion) triplets from sentences and can tell a complete story, i. e., the discussed aspect, the sentiment toward the aspect, and the cause of the sentiment.

Aspect Sentiment Triplet Extraction Extract Aspect +1

A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task

5 code implementations29 Mar 2021 Yuncong Li, Fang Wang, Wenjun Zhang, Sheng-hua Zhong, Cunxiang Yin, Yancheng He

Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences and tries to provide a complete solution for aspect-based sentiment analysis (ABSA).

Aspect-Sentiment-Opinion Triplet Extraction

Query-Variant Advertisement Text Generation with Association Knowledge

1 code implementation14 Apr 2020 Siyu Duan, Wei Li, Cai Jing, Yancheng He, Yunfang Wu, Xu sun

In this paper, we propose the query-variant advertisement text generation task that aims to generate candidate advertisement texts for different web search queries with various needs based on queries and item keywords.

Association Text Generation

Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

2 code implementations27 Jan 2020 Bang Liu, Haojie Wei, Di Niu, Haolan Chen, Yancheng He

In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions.

Answer Generation Chatbot +5

Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model

1 code implementation ACL 2019 Wei Li, Jingjing Xu, Yancheng He, ShengLi Yan, Yunfang Wu, Xu sun

In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.

Graph-to-Sequence

Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model

1 code implementation4 Jun 2019 Wei Li, Jingjing Xu, Yancheng He, ShengLi Yan, Yunfang Wu, Xu sun

In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.

Graph-to-Sequence

Learning to Generate Questions by Learning What not to Generate

no code implementations27 Feb 2019 Bang Liu, Mingjun Zhao, Di Niu, Kunfeng Lai, Yancheng He, Haojie Wei, Yu Xu

In CGC-QG, we design a multi-task labeling strategy to identify whether a question word should be copied from the input passage or be generated instead, guiding the model to learn the accurate boundaries between copying and generation.

Multi-Task Learning Question Answering +2

Matching Article Pairs with Graphical Decomposition and Convolutions

1 code implementation ACL 2019 Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu

Identifying the relationship between two articles, e. g., whether two articles published from different sources describe the same breaking news, is critical to many document understanding tasks.

Question Answering Text Matching

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