no code implementations • ACL 2022 • Etsuko Ishii, Bryan Wilie, Yan Xu, Samuel Cahyawijaya, Pascale Fung
Resolving dependencies among dialogue history is one of the main obstacles in the research on conversational question answering (QA).
1 code implementation • ACL (dialdoc) 2021 • Yan Xu, Etsuko Ishii, Genta Indra Winata, Zhaojiang Lin, Andrea Madotto, Zihan Liu, Peng Xu, Pascale Fung
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users’ needs, which.
1 code implementation • 3 Jul 2024 • Ziwei Ji, Delong Chen, Etsuko Ishii, Samuel Cahyawijaya, Yejin Bang, Bryan Wilie, Pascale Fung
The hallucination problem of Large Language Models (LLMs) significantly limits their reliability and trustworthiness.
no code implementations • 28 Jun 2024 • Bryan Wilie, Samuel Cahyawijaya, Etsuko Ishii, Junxian He, Pascale Fung
We evaluate $\sim$30 LMs across diverse prompting strategies and found that LMs generally struggle to appropriately revise their beliefs in response to new information.
no code implementations • 1 May 2024 • Delong Chen, Samuel Cahyawijaya, Etsuko Ishii, Ho Shu Chan, Yejin Bang, Pascale Fung
This paper establishes a formal information-theoretic framework for image captioning, conceptualizing captions as compressed linguistic representations that selectively encode semantic units in images.
1 code implementation • 11 Apr 2024 • Samuel Cahyawijaya, Delong Chen, Yejin Bang, Leila Khalatbari, Bryan Wilie, Ziwei Ji, Etsuko Ishii, Pascale Fung
there is an urgent need to understand the scope and nature of human values injected into these models before their release.
1 code implementation • 19 Oct 2023 • Etsuko Ishii, Yan Xu, Bryan Wilie, Ziwei Ji, Holy Lovenia, Willy Chung, Pascale Fung
Inference, especially those derived from inductive processes, is a crucial component in our conversation to complement the information implicitly or explicitly conveyed by a speaker.
no code implementations • 10 Oct 2023 • Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, Pascale Fung
Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks.
1 code implementation • insights (ACL) 2022 • Etsuko Ishii, Yan Xu, Samuel Cahyawijaya, Bryan Wilie
Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form.
no code implementations • 1 Mar 2022 • Ziwei Ji, Yan Xu, I-Tsun Cheng, Samuel Cahyawijaya, Rita Frieske, Etsuko Ishii, Min Zeng, Andrea Madotto, Pascale Fung
In order to offer a customized script tool and inspire professional scriptwriters, we present VScript.
no code implementations • 8 Feb 2022 • Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Delong Chen, Wenliang Dai, Ho Shu Chan, Andrea Madotto, Pascale Fung
This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation.
no code implementations • 14 Sep 2021 • Samuel Cahyawijaya, Genta Indra Winata, Holy Lovenia, Bryan Wilie, Wenliang Dai, Etsuko Ishii, Pascale Fung
While the recent advances in deep neural networks (DNN) bring remarkable success, the computational cost also increases considerably.
1 code implementation • SIGDIAL (ACL) 2021 • Yejin Bang, Nayeon Lee, Etsuko Ishii, Andrea Madotto, Pascale Fung
In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence.
1 code implementation • 7 Jun 2021 • Etsuko Ishii, Yan Xu, Genta Indra Winata, Zhaojiang Lin, Andrea Madotto, Zihan Liu, Peng Xu, Pascale Fung
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users' needs, which.
no code implementations • SIGDIAL (ACL) 2021 • Etsuko Ishii, Genta Indra Winata, Samuel Cahyawijaya, Divesh Lala, Tatsuya Kawahara, Pascale Fung
Over the past year, research in various domains, including Natural Language Processing (NLP), has been accelerated to fight against the COVID-19 pandemic, yet such research has just started on dialogue systems.
no code implementations • 1 Jun 2021 • Genta Indra Winata, Holy Lovenia, Etsuko Ishii, Farhad Bin Siddique, Yongsheng Yang, Pascale Fung
The current pandemic has forced people globally to remain in isolation and practice social distancing, which creates the need for a system to combat the resulting loneliness and negative emotions.
1 code implementation • dialdoc (ACL) 2022 • Yan Xu, Etsuko Ishii, Samuel Cahyawijaya, Zihan Liu, Genta Indra Winata, Andrea Madotto, Dan Su, Pascale Fung
This paper proposes KnowExpert, a framework to bypass the explicit retrieval process and inject knowledge into the pre-trained language models with lightweight adapters and adapt to the knowledge-grounded dialogue task.
no code implementations • 11 Jan 2021 • Yejin Bang, Etsuko Ishii, Samuel Cahyawijaya, Ziwei Ji, Pascale Fung
Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Andrea Madotto, Etsuko Ishii, Zhaojiang Lin, Sumanth Dathathri, Pascale Fung
These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model.
no code implementations • LREC 2020 • Masayasu Muraoka, Ryosuke Kohita, Etsuko Ishii
Datasets for these tasks contain a large number of pairs of an image and the corresponding sentence as an instance.
1 code implementation • EMNLP (NLP4ConvAI) 2021 • Zhaojiang Lin, Zihan Liu, Genta Indra Winata, Samuel Cahyawijaya, Andrea Madotto, Yejin Bang, Etsuko Ishii, Pascale Fung
Experimental results show that the multilingual trained models outperform the translation-pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages.