1 code implementation • EMNLP 2021 • Nianzu Ma, Alexander Politowicz, Sahisnu Mazumder, Jiahua Chen, Bing Liu, Eric Robertson, Scott Grigsby
This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example.
no code implementations • 8 Mar 2023 • Sumanta Bhattacharyya, Ramesh Manuvinakurike, Sahisnu Mazumder, Saurav Sahay
In this work, we develop a prompting approach for incremental summarization of task videos.
no code implementations • 12 Feb 2023 • Hsuan Su, Shachi H Kumar, Sahisnu Mazumder, Wenda Chen, Ramesh Manuvinakurike, Eda Okur, Saurav Sahay, Lama Nachman, Shang-Tse Chen, Hung-Yi Lee
With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems.
no code implementations • 12 Nov 2022 • Sahisnu Mazumder, Bing Liu
This book introduces the new paradigm of lifelong learning dialogue systems to endow chatbots the ability to learn continually by themselves through their own self-initiated interactions with their users and working environments to improve themselves.
1 code implementation • 31 Oct 2022 • Nianzu Ma, Sahisnu Mazumder, Alexander Politowicz, Bing Liu, Eric Robertson, Scott Grigsby
Much of the existing work on text novelty detection has been studied at the topic level, i. e., identifying whether the topic of a document or a sentence is novel or not.
no code implementations • 26 Oct 2022 • Sahisnu Mazumder, Bing Liu, Shuai Wang, Yingxuan Zhu, Xiaotian Yin, Lifeng Liu, Jian Li
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of state-action permissibility (SAP).
no code implementations • 17 Mar 2022 • Bing Liu, Sahisnu Mazumder, Eric Robertson, Scott Grigsby
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances.
no code implementations • 21 Oct 2021 • Bing Liu, Eric Robertson, Scott Grigsby, Sahisnu Mazumder
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can learn by themselves in a self-motivated and self-supervised manner rather than being retrained periodically on the initiation of human engineers using expanded training data.
no code implementations • COLING 2020 • Hao Wang, Shuai Wang, Sahisnu Mazumder, Bing Liu, Yan Yang, Tianrui Li
After each sentiment classification task is learned, its knowledge is retained to help future task learning.
no code implementations • NAACL 2021 • Sahisnu Mazumder, Oriana Riva
AI assistants can now carry out tasks for users by directly interacting with website UIs.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Jiahua Chen, Shuai Wang, Sahisnu Mazumder, Bing Liu
Classifying and resolving coreferences of objects (e. g., product names) and attributes (e. g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance.
no code implementations • 22 Sep 2020 • Bing Liu, Sahisnu Mazumder
Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs.
no code implementations • ACL 2020 • Nianzu Ma, Sahisnu Mazumder, Hao Wang, Bing Liu
This paper studies the task of comparative preference classification (CPC).
no code implementations • Findings (ACL) 2021 • Shuai Wang, Guangyi Lv, Sahisnu Mazumder, Bing Liu
We refer to this problem as domain polarity-changes of words.
no code implementations • 30 Oct 2019 • Sahisnu Mazumder, Bing Liu, Shuai Wang, Sepideh Esmaeilpour
Traditional approaches to building natural language (NL) interfaces typically use a semantic parser to parse the user command and convert it to a logical form, which is then translated to an executable action in an application.
no code implementations • WS 2019 • Sahisnu Mazumder, Bing Liu, Shuai Wang, Nianzu Ma
Dialogue systems are increasingly using knowledge bases (KBs) storing real-world facts to help generate quality responses.
no code implementations • 27 Sep 2018 • Sahisnu Mazumder, Bing Liu, Shuai Wang, Yingxuan Zhu, Xiaotian Yin, Lifeng Liu, Jian Li, Yongbing Huang
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of \textit{state-action permissibility} (SAP).
no code implementations • ACL 2018 • Shuai Wang, Sahisnu Mazumder, Bing Liu, Mianwei Zhou, Yi Chang
In MNs, attention mechanism plays a crucial role in detecting the sentiment context for the given target.
no code implementations • 16 Feb 2018 • Shuai Wang, Mianwei Zhou, Sahisnu Mazumder, Bing Liu, Yi Chang
Stage one extracts/groups the target-related words (call t-words) for a given target.
no code implementations • 16 Feb 2018 • Sahisnu Mazumder, Nianzu Ma, Bing Liu
We model the task as an open-world knowledge base completion problem and propose a novel technique called lifelong interactive learning and inference (LiLi) to solve it.
no code implementations • 20 Dec 2017 • Sahisnu Mazumder, Bing Liu
PR algorithms enumerate paths between entity pairs in a KB and use those paths as features to train a model for missing fact prediction.