Search Results for author: Tagyoung Chung

Found 25 papers, 8 papers with code

Unsupervised Melody-Guided Lyrics Generation

no code implementations12 May 2023 Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng

At inference time, we leverage the crucial alignments between melody and lyrics and compile the given melody into constraints to guide the generation process.

Text Generation

ExPUNations: Augmenting Puns with Keywords and Explanations

1 code implementation24 Oct 2022 Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master.

Explanation Generation Natural Language Understanding

Context-Situated Pun Generation

1 code implementation24 Oct 2022 Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

In this work, we propose a new task, context-situated pun generation, where a specific context represented by a set of keywords is provided, and the task is to first identify suitable pun words that are appropriate for the context, then generate puns based on the context keywords and the identified pun words.


Style Control for Schema-Guided Natural Language Generation

no code implementations EMNLP (NLP4ConvAI) 2021 Alicia Y. Tsai, Shereen Oraby, Vittorio Perera, Jiun-Yu Kao, Yuheng Du, Anjali Narayan-Chen, Tagyoung Chung, Dilek Hakkani-Tur

Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods.

Task-Oriented Dialogue Systems Text Generation

Few Shot Dialogue State Tracking using Meta-learning

1 code implementation EACL 2021 Saket Dingliwal, Bill Gao, Sanchit Agarwal, Chien-Wei Lin, Tagyoung Chung, Dilek Hakkani-Tur

Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information, etc.

Chatbot Dialogue State Tracking +1

Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems

no code implementations16 Nov 2020 Chien-Wei Lin, Vincent Auvray, Daniel Elkind, Arijit Biswas, Maryam Fazel-Zarandi, Nehal Belgamwar, Shubhra Chandra, Matt Zhao, Angeliki Metallinou, Tagyoung Chung, Charlie Shucheng Zhu, Suranjit Adhikari, Dilek Hakkani-Tur

Our approach includes a novel goal-sampling technique for sampling plausible user goals and a dialog simulation technique that uses heuristic interplay between the user and the system (Alexa), where the user tries to achieve the sampled goal.

Goal-Oriented Dialog Natural Language Understanding

Schema-Guided Natural Language Generation

1 code implementation INLG (ACL) 2020 Yuheng Du, Shereen Oraby, Vittorio Perera, Minmin Shen, Anjali Narayan-Chen, Tagyoung Chung, Anu Venkatesh, Dilek Hakkani-Tur

We train different state-of-the-art models for neural natural language generation on this dataset and show that in many cases, including rich schema information allows our models to produce higher quality outputs both in terms of semantics and diversity.

dialog state tracking Text Generation

MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension

2 code implementations1 Oct 2019 Di Jin, Shuyang Gao, Jiun-Yu Kao, Tagyoung Chung, Dilek Hakkani-Tur

Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language.

Logical Reasoning Machine Reading Comprehension +3

Dialog State Tracking: A Neural Reading Comprehension Approach

no code implementations WS 2019 Shuyang Gao, Abhishek Sethi, Sanchit Agarwal, Tagyoung Chung, Dilek Hakkani-Tur

In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation.

dialog state tracking Machine Reading Comprehension +2

Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators

no code implementations WS 2019 Sanghyun Yi, Rahul Goel, Chandra Khatri, Alessandra Cervone, Tagyoung Chung, Behnam Hedayatnia, Anu Venkatesh, Raefer Gabriel, Dilek Hakkani-Tur

Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches.

Chatbot Open-Domain Dialog +1

Simple Question Answering with Subgraph Ranking and Joint-Scoring

no code implementations NAACL 2019 Wenbo Zhao, Tagyoung Chung, Anuj Goyal, Angeliki Metallinou

Using this framework as a starting point, we focus on two aspects: improving subgraph selection through a novel ranking method and leveraging the subject--relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores.

Fact Selection Question Answering

Practical Semantic Parsing for Spoken Language Understanding

no code implementations NAACL 2019 Marco Damonte, Rahul Goel, Tagyoung Chung

Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response.

Multi-Task Learning Question Answering +2

Flexible and Scalable State Tracking Framework for Goal-Oriented Dialogue Systems

no code implementations30 Nov 2018 Rahul Goel, Shachi Paul, Tagyoung Chung, Jeremie Lecomte, Arindam Mandal, Dilek Hakkani-Tur

This limits such systems in two different ways: If there is an update in the task domain, the dialogue system usually needs to be updated or completely re-trained.

Dialogue State Tracking Goal-Oriented Dialogue Systems +1

Parsing Coordination for Spoken Language Understanding

no code implementations26 Oct 2018 Sanchit Agarwal, Rahul Goel, Tagyoung Chung, Abhishek Sethi, Arindam Mandal, Spyros Matsoukas

Typical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology.

Spoken Language Understanding

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