Search Results for author: Hui Su

Found 21 papers, 6 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

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

2 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

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

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

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

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

11 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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