most citedTowards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks

14 citations · 25 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL20201 cited

Cross-Domain Generalization Through Memorization: A Study of Nearest Neighbors in Neural Duplicate Question Detection

Yadollah Yaghoobzadeh, Alexandre Rochette, Timothy J. Hazen

Duplicate question detection (DQD) is important to increase efficiency of community and automatic question answering systems. Unfortunately, gathering supervised data in a domain i…

cs.CL201914 cited

Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks

Timothy J. Hazen, Shehzaad Dhuliawala, Daniel Boies

This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains. Current QA systems using…

cs.CL20199 cited

Unsupervised Domain Adaptation of Contextual Embeddings for Low-Resource Duplicate Question Detection

Alexandre Rochette, Yadollah Yaghoobzadeh, Timothy J. Hazen

Answering questions is a primary goal of many conversational systems or search products. While most current systems have focused on answering questions against structured databases…

cs.CL2019

Increasing Robustness to Spurious Correlations using Forgettable Examples

Yadollah Yaghoobzadeh, Soroush Mehri, Remi Tachet +2

Neural NLP models tend to rely on spurious correlations between labels and input features to perform their tasks. Minority examples, i.e., examples that contradict the spurious cor…

cs.CL20191 cited

Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings

Yadollah Yaghoobzadeh, Katharina Kann, Timothy J. Hazen +2

Word embeddings typically represent different meanings of a word in a single conflated vector. Empirical analysis of embeddings of ambiguous words is currently limited by the small…