Search Results for author: Chenyang Huang

Found 15 papers, 8 papers with code

Simulated Annealing for Emotional Dialogue Systems

no code implementations22 Sep 2021 Chengzhang Dong, Chenyang Huang, Osmar Zaïane, Lili Mou

Explicitly modeling emotions in dialogue generation has important applications, such as building empathetic personal companions.

Dialogue Generation

A Globally Normalized Neural Model for Semantic Parsing

no code implementations ACL (spnlp) 2021 Chenyang Huang, Wei Yang, Yanshuai Cao, Osmar Zaïane, Lili Mou

In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing.

Semantic Parsing

Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments

no code implementations26 May 2021 Nawshad Farruque, Chenyang Huang, Osmar Zaiane, Randy Goebel

In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers.

Multi-Label Classification Multi-Label Learning

Boundary-Aware Segmentation Network for Mobile and Web Applications

5 code implementations12 Jan 2021 Xuebin Qin, Deng-Ping Fan, Chenyang Huang, Cyril Diagne, Zichen Zhang, Adrià Cabeza Sant'Anna, Albert Suàrez, Martin Jagersand, Ling Shao

In this paper, we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet), which comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation.

Camouflaged Object Segmentation Image Segmentation +1

Optimizing Deeper Transformers on Small Datasets

1 code implementation ACL 2021 Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J. D. Prince, Yanshuai Cao

This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension.

Reading Comprehension Semantic Parsing +2

ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)

1 code implementation SEMEVAL 2020 Anandh Perumal, Chenyang Huang, Amine Trabelsi, Osmar R. Zaïane

In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning.

Model Selection Multi-Task Learning

Generating Responses Expressing Emotion in an Open-domain Dialogue System

no code implementations15 Nov 2018 Chenyang Huang, Osmar R. Zaïane

Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances.

Automatic Dialogue Generation with Expressed Emotions

1 code implementation NAACL 2018 Chenyang Huang, Osmar Za{\"\i}ane, Amine Trabelsi, Nouha Dziri

Despite myriad efforts in the literature designing neural dialogue generation systems in recent years, very few consider putting restrictions on the response itself.

Dialogue Generation

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