Project Goal: Innovate Conversational AI with Knowledge Bases; Build
Multimodal Artificially Intelligent Assistants
Selected to participate with
PARC Digital
Workforce in Perceptually-enabled Task Guidance (PTG):The
Perceptually-enabled Task Guidance (PTG) program aims to develop artificial
intelligence (AI) technologies to help users perform complex physical tasks
while making them more versatile by expanding their skillset and more proficient
by reducing their errors. PTG seeks to develop methods, techniques, and
technology for artificially intelligent assistants that provide just-in-time
visual and audio feedback to help with task execution.
Selected to participate in 2023
Amazon Alexa
Socialbot Grand Challenge 5: The
challenge is
focused on creating conversational social bots that can speak coherently and
engagingly with humans for 20 minutes on a range of current
events and topics.
Selected to participate in 2022-2023
Amazon Alexa
Simbot Challenge : The
challenge is focused on helping advance development of next-generation virtual
assistants that will assist humans in completing real-world tasks by
continuously learning, and gaining the ability to perform commonsense reasoning.
Finalist in 2021-2022
Amazon Alexa Taskbot Challenge [Technical
Report] (Our GauchoBot entered the final event and constantly received the
highest rate by Amazon Alexa users):
The challenge is focused on developing agents that assist customers in
completing tasks requiring multiple steps and decisions. It's the first
conversational AI challenge to incorporate multimodal (voice and vision)
customer experiences.
Publications
-
Guiding Large Language Models via Directional Stimulus Prompting
by
Z. Li, B. Peng, P. He, M. Galley, J. Gao, X. Yan, 2023 [arxiv]
-
Explanations from Large Language Models Make Small Reasoners Better
by S. Li, J. Chen, Y. Shen, Z. Chen, X. Zhang, Z. Li, H. Wang, J. Qian,
B. Peng, Y. Mao, W. Chen, X. Yan, 2023 [arxiv]
-
Visually-augmented language modeling
by W. Wang, L. Dong, H. Cheng, H.
Song, X. Liu, X. Yan, J. Gao, F. Wei
ICLR'23
(Proceedings of Int. Conf. on Learning Representations) [pdf]
- Limitations of Language Models in Arithmetic and Symbolic Induction
by J. Qian, H. Wang, Z. Li, S. Li, X. Yan, 2022 [arxiv]
- Language Model Detoxification in Dialogue with Contextualized Stance
Control
by J. Qian and X. Yan
EMNLP'22
(Proceedings of Findings of EMNLP 2022) [pdf]
- Controllable Dialogue Simulation with In-context Learning
by Z. Li, W. Chen, S. Li, H. Wang, J. Qian and X. Yan
EMNLP'22
(Proceedings of Findings of EMNLP 2022) [pdf]
- Explanations from Large Language Models Make Small Reasoners Better
by S. Li, J. Chen, Y. Shen, Z. Chen, X. Zhang, Z. Li, H. Wang, J. Qian,
B. Peng, Y. Mao, W. Chen, X. Yan, 2022 [arxiv]
- Visualization Question Answering Using Introspective Program Synthesis
(PLDI'22 Distinguished Paper Award)
by
Y. Chen, X. Yan, Y. Feng
PLDI'22 (The 43rd ACM SIGPLAN Conference on Programming
Language Design and Implementation) [pdf]
[code]
Making something out of nothing: Building robust task-oriented dialogue
systems from scratch
by
Z. Li, H. Wang, A. Albalak, Y. Yang, J. Qian, S. Li, X. Yan
Amazon Alexa Prize TaskBot Challenge Proceedings 2022 [pdf]
Inductive Relation Prediction by BERT
by H. Zha, Z. Chen, X. Yan
AAAI'22 (Thirty-Sixth AAAI Conference on Artificial
Intelligence) [arxiv]
Composite Re-Ranking for Efficient Document Search with BERT
Y. Yang, Y. Qiao, J. Shao, X. Yan, T. Yang,
WSDM'22 (ACM
International Conference on Web Search and Data Mining) [arxiv]
Task-adaptive Pre-training and Self-training are Complementary for
Natural Language Understanding
by S. Li, S. Yavuz, W. Chen and X. Yan
EMNLP'21
(Proceedings of Findings of EMNLP 2021) [pdf]
CoCo: Controllable Counterfactuals for Evaluating Dialogue State
Trackers
by S. Li*, S. Yavuz*, K. Hashimoto, J. Li, T. Niu, N. Rajani, X. Yan, Y.
Zhou and C. Xiong (*Equal Contribution)
ICLR'21
(International Conference on Learning Representations), 2021. [pdf]
Leaderboard No.1 as
Jan 2021 in Multiwoz
Beyond I.I.D.: Three Levels of Generalization for Question Answering on
Knowledge Bases
by Y. Gu, S. Kase, M. Vanni, B. Sadler, P. Liang, X.
Yan, Y. Su
WWW'21
(The World Wide Web Conf.) 2021. [arxiv] [Dataset:
GrailQA]
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation,
by W. Chen, Y. Su, X. Yan, W. Wang,
EMNLP'20
(Proc. of the 2020 Conference on Empirical Methods in Natural
Language Processing) [pdf]
[data/code]
HierCon: Hierarchical
Organization of Technical Documents based on Concepts,
by K. Li,
Shiyang Li, Semih Yavuz, Hanwen Zha, Yu Su, and Xifeng Yan,
ICDM'19 (Proc.
2019 IEEE Int. Conf. on Data Mining), Dec 2019. [pdf]
(Best of ICDM 2019 selection)
Mining Algorithm Roadmap in Scientific Publications,
by H. Zha, W. Chen,
K. Li and X. Yan,
KDD'19
(Proc. of the 25th Int. Conf. on Knowledge Discovery and Data Mining)
[pdf]
Global Textual Relation Embedding for Relational Understanding,
by Z.
Chen, H. Zha, H. Liu, W. Chen, X. Yan and Y. Su,
ACL'19 (Proc. of the Annual Meeting of the Association for Computational
Linguistics)
(Short Paper) [pdf]
Variational Knowledge
Graph Reasoning,
by W. Chen, W. Xiong, X. Yan and
W. Wang,
NAACL-HLT'18 (Proc. of
the 16th North American Chapter of ACL: Human Language Technologies, 2018)
[pdf]
Global Relation Embedding for Relation Extraction
by Yu Su*, Honglei Liu*, Semih Yavuz, Izzeddin Gur, Huan Sun, Xifeng
Yan. [pdf] [code]
(*: Equal Contribution) https://arxiv.org/abs/1704.05958, April 2017
NAACL-HLT'18 (Proc. of
the 16th North American Chapter of ACL: Human Language Technologies, 2018)
What It Takes to Achieve 100% Condition Accuracy on WikiSQL,
by S. Yavuz, I. Gur, Y. Su, X. Yan,
EMNLP'18
(Proc. of the 2018 Conference on Empirical Methods in Natural
Language Processing) [pdf]
XL-NBT: A Cross-lingual Neural Belief Tracking Framework,
by W. Chen, J. Chen, Y. Su, X. Wang, D. Yu, X. Yan and W. Wang,
EMNLP'18
(Proc. of the 2018 Conf. on Empirical Methods in Natural Language
Processing) [pdf]
DialSQL: Dialogue Based Structured Query Generation,
by I. Gur, S.
Yavuz, Y. Su, X. Yan,
ACL'18
(Proc. of the Annual Meeting of the Association for Computational
Linguistics, 2018) [pdf]Scalable Construction and Querying of Massive Knowledge Bases
(Tutorial),
by X. Ren, Y. Su, P. Szekely, X. Yan.
WWW'18 (Proc. of the International Conference
on World Wide Web), 2018 [website][slides1][slides2][slides3]Construction and Querying of
Large-scale Knowledge Bases (Tutorial),
by X. Ren, Y. Su, X. Yan.
CIKM'17(Proc. of the
ACM International Conference on Information and Knowledge Management), 2017
[website][slides]Cross-domain Semantic Parsing via Paraphrasing,
by Y.
Su, X. Yan.
EMNLP'17 (Proc. of the 2017 Conf. on Empirical
Methods in Natural Language Processing), 2017 [pdf]Recovering Question Answering Errors via Query Revision,
by S. Yavuz, I.
Gur, Y. Su, X. Yan.
EMNLP'17 (Proc.
of the 2017 Conference on Empirical Methods in Natural Language Processing),
2017 [pdf]
Entity Disambiguation with Linkless Knowledge Bases,
by Y. Li, S. Tan, H. Sun, J. Han, D. Roth and X. Yan,
WWW'16 (Proc.
of the 25th Int. World Wide Web Conference), 2016. [pdf]
Distributed Representations of Expertise,
by F. Han, S. Tan, H. Sun, M. Srivatsa, D. Cai, X. Yan,
SDM'16 (SIAM
Int. Conf. on Data Mining), 2016. [pdf]
On Generating Characteristic-rich Question Sets for QA Evaluation,
by Y.
Su, H. Sun, B. Sadler, M. Srivatsa, I. Gur, Z. Yan, and X. Yan,
EMNLP'16
(Proc. of the
2016 Conf. on Empirical Methods in Natural Language Processing) 2016 [pdf]Improving Semantic Parsing via Answer Type Inference,
by S. Yavuz, I. Gur,
Y. Su, M. Srivatsa, X. Yan,
EMNLP'16
(Proc. of the 2016 Conf. on Empirical Methods in
Natural Language Processing), 2016 [pdf]Semantic SPARQL Similarity Search Over RDF Knowledge Graphs,
by W. Zheng, L.
Zou, W. Peng, X. Yan, S. Song, D. Zhao,
VLDB'16
(Prof. of the 42nd International
Conference on Very Large Data Bases), 2016. [pdf]Exploiting Relevance Feedback in Knowledge Graph Search,
by Y. Su, S. Yang,
H. Sun, M. Srivatsa, S. Kase, M. Vanni and X. Yan,
KDD'15
(Proc. of Int. Conf. on Knowledge Discovery and Data Mining),
2015 [pdf]SLQ: A User-friendly Graph Querying System,
by S. Yang, Y. Xie, Y.
Wu, T. Wu, H. Sun, J. Wu, X. Yan,
SIGMOD'14
(Proc. 2014 Int. Conf. on Management of Data) (demo paper), 2014. [pdf]Schemaless and Structureless Graph Querying,
by S. Yang, Y. Wu, H. Sun,
X. Yan,
VLDB'14
(Proc. of the 40th Int. Conf. on Very Large Databases),
2014. [pdf]
Mining Evidences for Named Entity
Disambiguation,
by Y. Li, C. Wang, F. Han, J. Han, D.
Roth, and X. Yan,
KDD'13 (Proc.
of the 19th Int. Conf. on Knowledge Discovery and Data Mining), Aug
2013. [pdf]
EntityRank: Searching
Entities Directly and Holistically,
by T. Cheng, X. Yan and K. Chang.
VLDB'07b (Proc.
of 2007 Int. Conf. on Very Large Data Bases), Sep. 2007. [pdf]
Dissertations
* Jing Qian, Ph.D., "Text Detoxification in
Natural Language Processing," 2022 [pdf]
* Zhiyu Chen, Ph.D., "Knowledge-Grounded Natural
Language Processing," 2022 [pdf]
* Hanwen Zha, Ph.D., "Towards Effort-Saving Knowledge
Mining and Reasoning over the Web," 2021 [pdf]
* Wenhu Chen, Ph.D., "Knowledge-Grounded Natural Language Processing," 2021 [pdf]
* Semih Yavuz, Ph.D., "DeepAssist: Deep Knowledge
Grounding for Factual and Conversational Natural Language Interfaces," 2019 [pdf]
* Izzeddin Gur, Ph.D., "Learning Natural Language Interfaces using Deep Neural
Networks," 2019 [pdf]
* Yu Su, Ph.D., "Towards Democratizing Data Science with Natural
Language Interfaces,"
2018 [pdf]
* Huan Sun, Ph.D., "Mining Disparate Sources for Question Answering," 2016 [pdf]
* Yang Li, Ph.D., "Connecting
Text with Knowledge," 2015 [pdf]