Search Results for author: Hui Su

Found 29 papers, 9 papers with code

MovieChats: Chat like Humans in a Closed Domain

no code implementations EMNLP 2020 Hui Su, Xiaoyu Shen, Zhou Xiao, Zheng Zhang, Ernie Chang, Cheng Zhang, Cheng Niu, Jie zhou

In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.

Chatbot Retrieval

SASFormer: Transformers for Sparsely Annotated Semantic Segmentation

1 code implementation5 Dec 2022 Hui Su, Yue Ye, Wei Hua, Lechao Cheng, Mingli Song

In this work, we propose a simple yet effective sparse annotated semantic segmentation framework based on segformer, dubbed SASFormer, that achieves remarkable performance.

Semantic Segmentation Weakly supervised Semantic Segmentation

WeLM: A Well-Read Pre-trained Language Model for Chinese

no code implementations21 Sep 2022 Hui Su, Xiao Zhou, Houjin Yu, YuWen Chen, Zilin Zhu, Yang Yu, Jie zhou

Large Language Models pre-trained with self-supervised learning have demonstrated impressive zero-shot generalization capabilities on a wide spectrum of tasks.

Language Modelling Self-Supervised Learning +1

Re-Attention Transformer for Weakly Supervised Object Localization

1 code implementation3 Aug 2022 Hui Su, Yue Ye, Zhiwei Chen, Mingli Song, Lechao Cheng

Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories.

Weakly-Supervised Object Localization

Temporal Multimodal Multivariate Learning

no code implementations14 Jun 2022 Hyoshin Park, Justice Darko, Niharika Deshpande, Venktesh Pandey, Hui Su, Masahiro Ono, Dedrick Barkely, Larkin Folsom, Derek Posselt, Steve Chien

We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observations of a probability distribution with more than one peak or more than one outcome variable from one time stage to another.

Decision Making

Knowledge-enhanced Session-based Recommendation with Temporal Transformer

no code implementations16 Dec 2021 Rongzhi Zhang, Yulong Gu, Xiaoyu Shen, Hui Su

We introduce time interval embedding to represent the time pattern between the item that needs to be predicted and historical click, and use it to replace the position embedding in the original transformer (called temporal transformer).

Graph Representation Learning Session-Based Recommendations

Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study

3 code implementations7 Jun 2021 Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, Hui Su

Recent advancements in open-domain question answering (ODQA), i. e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets.

Open-Domain Question Answering

Neural Data-to-Text Generation with LM-based Text Augmentation

no code implementations EACL 2021 Ernie Chang, Xiaoyu Shen, Dawei Zhu, Vera Demberg, Hui Su

Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples.

Data-to-Text Generation Text Augmentation

Patient-Specific Seizure Prediction Using Single Seizure Electroencephalography Recording

no code implementations14 Nov 2020 Zaid Bin Tariq, Arun Iyengar, Lara Marcuse, Hui Su, Bülent Yener

But these models require a considerable number of patient-specific seizures to be recorded for extracting the preictal and interictal EEG data for training a classifier.

EEG Seizure prediction +1

Multimodal Dialogue State Tracking By QA Approach with Data Augmentation

no code implementations20 Jul 2020 Xiangyang Mou, Brandyn Sigouin, Ian Steenstra, Hui Su

Different from purely text-based dialogue state tracking, the dialogue in AVSD contains a sequence of question-answer pairs about a video and the final answer to the given question requires additional understanding of the video.

Data Augmentation Dialogue State Tracking +2

Frustratingly Hard Evidence Retrieval for QA Over Books

no code implementations WS 2020 Xiangyang Mou, Mo Yu, Bingsheng Yao, Chenghao Yang, Xiaoxiao Guo, Saloni Potdar, Hui Su

A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth.

Question Answering Retrieval

Diversifying Dialogue Generation with Non-Conversational Text

1 code implementation ACL 2020 Hui Su, Xiaoyu Shen, Sanqiang Zhao, Xiao Zhou, Pengwei Hu, Randy Zhong, Cheng Niu, Jie zhou

Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation.

Dialogue Generation Translation

Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension

no code implementations WS 2019 Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, Yifei Zhang

Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.

Reading Comprehension reinforcement-learning +1

Improving Multi-turn Dialogue Modelling with Utterance ReWriter

1 code implementation ACL 2019 Hui Su, Xiaoyu Shen, Rongzhi Zhang, Fei Sun, Pengwei Hu, Cheng Niu, Jie zhou

To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network.

Coreference Resolution Dialogue Rewriting

NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation

no code implementations EMNLP 2018 Hui Su, Xiaoyu Shen, Wenjie Li, Dietrich Klakow

Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems.

Dialogue Generation

A Cost-Effective Framework for Preference Elicitation and Aggregation

no code implementations14 May 2018 Zhibing Zhao, Haoming Li, Junming Wang, Jeffrey Kephart, Nicholas Mattei, Hui Su, Lirong Xia

We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features.

Improving Variational Encoder-Decoders in Dialogue Generation

no code implementations6 Feb 2018 Xiaoyu Shen, Hui Su, Shuzi Niu, Vera Demberg

Variational encoder-decoders (VEDs) have shown promising results in dialogue generation.

Dialogue Generation

DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

13 code implementations IJCNLP 2017 Yan-ran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, Shuzi Niu

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.

Toward Cognitive and Immersive Systems: Experiments in a Cognitive Microworld

no code implementations14 Sep 2017 Matthew Peveler, Naveen Sundar Govindarajulu, Selmer Bringsjord, Biplav Srivastava, Kartik Talamadupula, Hui Su

These \textit{cognitive and immersive systems} (CAISs) fall squarely into the intersection of AI with HCI/HRI: such systems interact with and assist the human agents that enter them, in no small part because such systems are infused with AI able to understand and reason about these humans and their knowledge, beliefs, goals, communications, plans, etc.

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