no code implementations • 12 Jun 2019 • Pushpendre Rastogi
Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE).
no code implementations • WS 2019 • Tongfei Chen, Chetan Naik, Hua He, Pushpendre Rastogi, Lambert Mathias
One such approach for tracking the dialogue state is slot carryover, where a model makes a binary decision if a slot from the context is relevant to the current turn.
1 code implementation • 28 Mar 2019 • Michael Regan, Pushpendre Rastogi, Arpit Gupta, Lambert Mathias
In this paper, we describe our methodology for creating the query reformulation extension to the dialog corpus, and present an initial set of experiments to establish a baseline for the CQR task.
no code implementations • NAACL 2019 • Pushpendre Rastogi, Arpit Gupta, Tongfei Chen, Lambert Mathias
We present a novel approach to dialogue state tracking and referring expression resolution tasks.
Dialogue State Tracking Multi-domain Dialogue State Tracking +3
no code implementations • IJCNLP 2017 • Aaron Steven White, Pushpendre Rastogi, Kevin Duh, Benjamin Van Durme
We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE).
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.
1 code implementation • EACL 2017 • Adam Poliak, Pushpendre Rastogi, M. Patrick Martin, Benjamin Van Durme
We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive.
no code implementations • 16 May 2016 • Pushpendre Rastogi, Benjamin Van Durme
Link prediction in large knowledge graphs has received a lot of attention recently because of its importance for inferring missing relations and for completing and improving noisily extracted knowledge graphs.
1 code implementation • WS 2016 • Manaal Faruqui, Yulia Tsvetkov, Pushpendre Rastogi, Chris Dyer
Our study suggests that the use of word similarity tasks for evaluation of word vectors is not sustainable and calls for further research on evaluation methods.
2 code implementations • 7 Aug 2015 • Pushpendre Rastogi, Benjamin Van Durme
The output scores of a neural network classifier are converted to probabilities via normalizing over the scores of all competing categories.
no code implementations • LREC 2014 • Jennifer Drexler, Pushpendre Rastogi, Jacqueline Aguilar, Benjamin Van Durme, Matt Post
We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language.