1 code implementation • 9 Jan 2024 • Eugene Yang, Dawn Lawrie, James Mayfield, Douglas W. Oard, Scott Miller
Applying a similar knowledge distillation approach to training an efficient dual-encoder model for Cross-Language Information Retrieval (CLIR), where queries and documents are in different languages, is challenging due to the lack of a sufficiently large training collection when the query and document languages differ.
no code implementations • 29 Apr 2023 • James Mayfield, Eugene Yang, Dawn Lawrie, Samuel Barham, Orion Weller, Marc Mason, Suraj Nair, Scott Miller
By repeating this process, collections of arbitrary size can be created in the style of MS MARCO but using naturally-occurring documents in any desired genre and domain of discourse.
1 code implementation • 22 Feb 2023 • Shantanu Agarwal, Steven Fincke, Chris Jenkins, Scott Miller, Elizabeth Boschee
Taking the task of cross-lingual event detection as a motivating example, we show that the choice of pooling strategy can have a significant impact on the target language performance.
no code implementations • 25 Sep 2021 • Steven Fincke, Shantanu Agarwal, Scott Miller, Elizabeth Boschee
We show that by enabling the language model to better compensate for the deficits of sparse and noisy training data, our approach improves both trigger and argument detection and classification significantly over the state of the art in a zero-shot cross-lingual setting.
2 code implementations • NAACL 2022 • I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng
Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a predefined pattern.
no code implementations • LREC 2020 • Joel Barry, Elizabeth Boschee, Marjorie Freedman, Scott Miller
We describe an approach to cross lingual information retrieval that does not rely on explicit translation of either document or query terms.
no code implementations • IJCNLP 2019 • Constantine Lignos, Daniel Cohen, Yen-Chieh Lien, Pratik Mehta, W. Bruce Croft, Scott Miller
When performing cross-language information retrieval (CLIR) for lower-resourced languages, a common approach is to retrieve over the output of machine translation (MT).
no code implementations • WS 2019 • Xiaoman Pan, Thamme Gowda, Heng Ji, Jonathan May, Scott Miller
Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking.
no code implementations • ACL 2019 • Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, Scott Miller
In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed.
no code implementations • IJCNLP 2017 • Benjamin Van Durme, Tom Lippincott, Kevin Duh, Deana Burchfield, Adam Poliak, Cash Costello, Tim Finin, Scott Miller, James Mayfield, Philipp Koehn, Craig Harman, Dawn Lawrie, Ch May, ler, Max Thomas, Annabelle Carrell, Julianne Chaloux, Tongfei Chen, Alex Comerford, Mark Dredze, Benjamin Glass, Shudong Hao, Patrick Martin, Pushpendre Rastogi, Rashmi Sankepally, Travis Wolfe, Ying-Ying Tran, Ted Zhang
It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users.
no code implementations • 5 Feb 2015 • Kathryn Baker, Michael Bloodgood, Bonnie J. Dorr, Chris Callison-Burch, Nathaniel W. Filardo, Christine Piatko, Lori Levin, Scott Miller
We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of semantic annotations.
no code implementations • 24 Sep 2014 • Kathryn Baker, Michael Bloodgood, Chris Callison-Burch, Bonnie J. Dorr, Nathaniel W. Filardo, Lori Levin, Scott Miller, Christine Piatko
We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation.