no code implementations • EMNLP (ACL) 2021 • Arkady Arkhangorodsky, Christopher Chu, Scot Fang, Yiqi Huang, Denglin Jiang, Ajay Nagesh, Boliang Zhang, Kevin Knight
We use the re-translation strategy to translate the streamed speech, resulting in caption flicker.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 8 Feb 2021 • Boliang Zhang, Ying Lyu, Ning Ding, Tianhao Shen, Zhaoyang Jia, Kun Han, Kevin Knight
This paper describes our submission for the End-to-end Multi-domain Task Completion Dialog shared task at the 9th Dialog System Technology Challenge (DSTC-9).
no code implementations • CONLL 2018 • Boliang Zhang, Spencer Whitehead, Lifu Huang, Heng Ji
Many name tagging approaches use local contextual information with much success, but fail when the local context is ambiguous or limited.
1 code implementation • 9 Oct 2020 • Arkady Arkhangorodsky, Amittai Axelrod, Christopher Chu, Scot Fang, Yiqi Huang, Ajay Nagesh, Xing Shi, Boliang Zhang, Kevin Knight
We create a new task-oriented dialog platform (MEEP) where agents are given considerable freedom in terms of utterances and API calls, but are constrained to work within a push-button environment.
no code implementations • ACL 2020 • Boliang Zhang, Ajay Nagesh, Kevin Knight
Web-crawled data provides a good source of parallel corpora for training machine translation models.
Ranked #1 on Machine Translation on WMT2019 English-Japanese
no code implementations • EMNLP 2018 • Ge Shi, Chong Feng, Lifu Huang, Boliang Zhang, Heng Ji, Lejian Liao, He-Yan Huang
Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions.
1 code implementation • WS 2018 • Qingyun Wang, Xiaoman Pan, Lifu Huang, Boliang Zhang, Zhiying Jiang, Heng Ji, Kevin Knight
We aim to automatically generate natural language descriptions about an input structured knowledge base (KB).
no code implementations • ACL 2018 • Ying Lin, Cash Costello, Boliang Zhang, Di Lu, Heng Ji, James Mayfield, Paul McNamee
We demonstrate two annotation platforms that allow an English speaker to annotate names for any language without knowing the language.
no code implementations • NAACL 2018 • Boliang Zhang, Ying Lin, Xiaoman Pan, Di Lu, Jonathan May, Kevin Knight, Heng Ji
We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap.
1 code implementation • WS 2018 • Zhiying Jiang, Boliang Zhang, Lifu Huang, Heng Ji
We present a neural recommendation model for Chengyu, which is a special type of Chinese idiom.
2 code implementations • ACL 2018 • Qingyun Wang, Zhi-Hao Zhou, Lifu Huang, Spencer Whitehead, Boliang Zhang, Heng Ji, Kevin Knight
We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract.
Ranked #1 on Paper generation on ACL Title and Abstract Dataset
no code implementations • EMNLP 2018 • Lifu Huang, Kyunghyun Cho, Boliang Zhang, Heng Ji, Kevin Knight
We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space to enable knowledge and resource transfer across languages.
no code implementations • IJCNLP 2017 • Boliang Zhang, Di Lu, Xiaoman Pan, Ying Lin, Halidanmu Abudukelimu, Heng Ji, Kevin Knight
Current supervised name tagging approaches are inadequate for most low-resource languages due to the lack of annotated data and actionable linguistic knowledge.
no code implementations • ACL 2017 • Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, Heng Ji
The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia.
no code implementations • COLING 2016 • Dongxu Zhang, Boliang Zhang, Xiaoman Pan, Xiaocheng Feng, Heng Ji, Weiran Xu
Instead of directly relying on word alignment results, this framework combines advantages of rule-based methods and deep learning methods by implementing two steps: First, generates a high-confidence entity annotation set on IL side with strict searching methods; Second, uses this high-confidence set to weakly supervise the model training.