Search Results for author: Cao Liu

Found 13 papers, 3 papers with code

Generative Calibration for In-context Learning

1 code implementation16 Oct 2023 Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jun Zhao, Kang Liu

In this paper, we for the first time theoretically and empirically identify that such a paradox is mainly due to the label shift of the in-context model to the data distribution, in which LLMs shift the label marginal $p(y)$ while having a good label conditional $p(x|y)$.

In-Context Learning text-classification +1

Interpreting Sentiment Composition with Latent Semantic Tree

1 code implementation31 Aug 2023 Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jiansong Chen, Jun Zhao, Kang Liu

As the key to sentiment analysis, sentiment composition considers the classification of a constituent via classifications of its contained sub-constituents and rules operated on them.

Classification Domain Adaptation +1

MME-CRS: Multi-Metric Evaluation Based on Correlation Re-Scaling for Evaluating Open-Domain Dialogue

no code implementations19 Jun 2022 Pengfei Zhang, Xiaohui Hu, Kaidong Yu, Jian Wang, Song Han, Cao Liu, Chunyang Yuan

Firstly, we build an evaluation metric composed of 5 groups of parallel sub-metrics called Multi-Metric Evaluation (MME) to evaluate the quality of dialogue comprehensively.

Dialogue Evaluation

Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss

no code implementations17 Mar 2022 Yantao Gong, Cao Liu, Fan Yang, Xunliang Cai, Guanglu Wan, Jiansong Chen, Weipeng Zhang, Houfeng Wang

Experiments on the open datasets verify that our model outperforms the existing calibration methods and achieves a significant improvement on the calibration metric.

Intent Detection

Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots

no code implementations CONLL 2019 Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, Jun Zhao

Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC.

Chatbot Response Generation

Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation

no code implementations ACL 2019 Cao Liu, Shizhu He, Kang Liu, Jun Zhao

To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN) which is able to incorporate multi-pass encoding and decoding with multi-level vocabularies into response generation.

Clustering Response Generation

IJCNLP-2017 Task 5: Multi-choice Question Answering in Examinations

no code implementations IJCNLP 2017 Shangmin Guo, Kang Liu, Shizhu He, Cao Liu, Jun Zhao, Zhuoyu Wei

The IJCNLP-2017 Multi-choice Question Answering(MCQA) task aims at exploring the performance of current Question Answering(QA) techniques via the realworld complex questions collected from Chinese Senior High School Entrance Examination papers and CK12 website1.

Question Answering

Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning

no code implementations ACL 2017 Shizhu He, Cao Liu, Kang Liu, Jun Zhao

Generating answer with natural language sentence is very important in real-world question answering systems, which needs to obtain a right answer as well as a coherent natural response.

Question Answering Sentence

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