46 citations · 173 across the 22 of their papers we have counts for
23 papers
RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
Jie Huang, Wei Ping, Peng Xu +3
In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models. We first conduct a comprehensive analysis of existing models a…
Evaluating Parameter Efficient Learning for Generation
Peng Xu, Mostofa Patwary, Shrimai Prabhumoye +6
Parameter efficient learning methods (PERMs) have recently gained significant attention as they provide an efficient way for pre-trained language models (PLMs) to adapt to a downst…
Context Generation Improves Open Domain Question Answering
Dan Su, Mostofa Patwary, Shrimai Prabhumoye +6
Closed-book question answering (QA) requires a model to directly answer an open-domain question without access to any external knowledge. Prior work on closed-book QA either direct…
QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation
Dan Su, Peng Xu, Pascale Fung
Multi-hop question generation (MQG) aims to generate complex questions which require reasoning over multiple pieces of information of the input passage. Most existing work on MQG h…
CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System
Etsuko Ishii, Yan Xu, Genta Indra Winata +5
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on…
X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing
Zihan Liu, Genta Indra Winata, Peng Xu +1
Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries and serves as an essential component of virtual assistants. Current TCSP models rely on nume…
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling
Zhaojiang Lin, Andrea Madotto, Genta Indra Winata +5
Task-oriented dialogue (ToD) benchmarks provide an important avenue to measure progress and develop better conversational agents. However, existing datasets for end-to-end ToD mode…
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling
Zhixian Yang, Pengxuan Xu, Xiaojun Wan
Neural text generation models are likely to suffer from the low-diversity problem. Various decoding strategies and training-based methods have been proposed to promote diversity on…
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models
Peng Xu, Mostofa Patwary, Mohammad Shoeybi +4
Existing pre-trained large language models have shown unparalleled generative capabilities. However, they are not controllable. In this paper, we propose MEGATRON-CNTRL, a novel fr…
Meta-Transfer Learning for Code-Switched Speech Recognition
Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin +3
An increasing number of people in the world today speak a mixed-language as a result of being multilingual. However, building a speech recognition system for code-switching remains…
Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling
Zihan Liu, Genta Indra Winata, Peng Xu +1
As an essential task in task-oriented dialog systems, slot filling requires extensive training data in a certain domain. However, such data are not always available. Hence, cross-d…
Variational Transformers for Diverse Response Generation
Zhaojiang Lin, Genta Indra Winata, Peng Xu +2
Despite the great promise of Transformers in many sequence modeling tasks (e.g., machine translation), their deterministic nature hinders them from generalizing to high entropy tas…
Learning Fast Adaptation on Cross-Accented Speech Recognition
Genta Indra Winata, Samuel Cahyawijaya, Zihan Liu +4
Local dialects influence people to pronounce words of the same language differently from each other. The great variability and complex characteristics of accents creates a major ch…
Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems
Zihan Liu, Genta Indra Winata, Zhaojiang Lin +2
Recently, data-driven task-oriented dialogue systems have achieved promising performance in English. However, developing dialogue systems that support low-resource languages remain…
Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables
Zihan Liu, Jamin Shin, Yan Xu +4
Despite the surging demands for multilingual task-oriented dialog systems (e.g., Alexa, Google Home), there has been less research done in multilingual or cross-lingual scenarios.…
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning
Peng Xu, Chien-Sheng Wu, Andrea Madotto +1
Sensational headlines are headlines that capture people's attention and generate reader interest. Conventional abstractive headline generation methods, unlike human writers, do not…
MoEL: Mixture of Empathetic Listeners
Zhaojiang Lin, Andrea Madotto, Jamin Shin +2
Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions. However, being empathetic not only requires the ability of gener…
Getting To Know You: User Attribute Extraction from Dialogues
Chien-Sheng Wu, Andrea Madotto, Zhaojiang Lin +2
User attributes provide rich and useful information for user understanding, yet structured and easy-to-use attributes are often sparsely populated. In this paper, we leverage dialo…
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
Genta Indra Winata, Andrea Madotto, Zhaojiang Lin +4
Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed. One could consider the emotion of each dialogue turn to be independent, but in this paper…
Generating Empathetic Responses by Looking Ahead the User's Sentiment
Jamin Shin, Peng Xu, Andrea Madotto +1
An important aspect of human conversation difficult for machines is conversing with empathy, which is to understand the user's emotion and respond appropriately. Recent neural conv…
A novel repetition normalized adversarial reward for headline generation
Peng Xu, Pascale Fung
While reinforcement learning can effectively improve language generation models, it often suffers from generating incoherent and repetitive phrases \cite{paulus2017deep}. In this p…
Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
Peng Xu, Andrea Madotto, Chien-Sheng Wu +2
In this paper, we propose Emo2Vec which encodes emotional semantics into vectors. We train Emo2Vec by multi-task learning six different emotion-related tasks, including emotion/sen…
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags
Ji Ho Park, Peng Xu, Pascale Fung
This paper describes our system that has been submitted to SemEval-2018 Task 1: Affect in Tweets (AIT) to solve five subtasks. We focus on modeling both sentence and word level rep…