28 citations · 38 across the 9 of their papers we have counts for
10 papers · 1 filter
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
Bo-Ru Lu, Nikita Haduong, Chia-Hsuan Lee +7
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fac…
MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages
Akari Asai, Shayne Longpre, Jungo Kasai +6
We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typo…
In-Context Learning for Few-Shot Dialogue State Tracking
Yushi Hu, Chia-Hsuan Lee, Tianbao Xie +3
Collecting and annotating task-oriented dialogues is time-consuming and costly; thus, zero and few shot learning could greatly benefit dialogue state tracking (DST). In this work,…
DOCmT5: Document-Level Pretraining of Multilingual Language Models
Chia-Hsuan Lee, Aditya Siddhant, Viresh Ratnakar +1
In this paper, we introduce DOCmT5, a multilingual sequence-to-sequence language model pretrained with large scale parallel documents. While previous approaches have focused on lev…
Dialogue State Tracking with a Language Model using Schema-Driven Prompting
Chia-Hsuan Lee, Hao Cheng, Mari Ostendorf
Task-oriented conversational systems often use dialogue state tracking to represent the user's intentions, which involves filling in values of pre-defined slots. Many approaches ha…
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
Chia-Hsuan Lee, Oleksandr Polozov, Matthew Richardson
The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such…
Cross-Lingual Transfer Learning for Question Answering
Chia-Hsuan Lee, Hung-Yi Lee
Deep learning based question answering (QA) on English documents has achieved success because there is a large amount of English training examples. However, for most languages, tra…
Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation
Chia-Hsuan Lee, Yun-Nung Chen, Hung-Yi Lee
Spoken question answering (SQA) is challenging due to complex reasoning on top of the spoken documents. The recent studies have also shown the catastrophic impact of automatic spee…
ODSQA: Open-domain Spoken Question Answering Dataset
Chia-Hsuan Lee, Shang-Ming Wang, Huan-Cheng Chang +1
Reading comprehension by machine has been widely studied, but machine comprehension of spoken content is still a less investigated problem. In this paper, we release Open-Domain Sp…
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension
Chia-Hsuan Li, Szu-Lin Wu, Chi-Liang Liu +1
Reading comprehension has been widely studied. One of the most representative reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine is alread…