Geoffrey Zweig

Also published as: G. Zweig, Geoff Zweig


2020

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Multilingual Graphemic Hybrid ASR with Massive Data Augmentation
Chunxi Liu | Qiaochu Zhang | Xiaohui Zhang | Kritika Singh | Yatharth Saraf | Geoffrey Zweig
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

Towards developing high-performing ASR for low-resource languages, approaches to address the lack of resources are to make use of data from multiple languages, and to augment the training data by creating acoustic variations. In this work we present a single grapheme-based ASR model learned on 7 geographically proximal languages, using standard hybrid BLSTM-HMM acoustic models with lattice-free MMI objective. We build the single ASR grapheme set via taking the union over each language-specific grapheme set, and we find such multilingual graphemic hybrid ASR model can perform language-independent recognition on all 7 languages, and substantially outperform each monolingual ASR model. Secondly, we evaluate the efficacy of multiple data augmentation alternatives within language, as well as their complementarity with multilingual modeling. Overall, we show that the proposed multilingual graphemic hybrid ASR with various data augmentation can not only recognize any within training set languages, but also provide large ASR performance improvements.

2017

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Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
Jason D. Williams | Kavosh Asadi | Geoffrey Zweig
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors. We introduce Hybrid Code Networks (HCNs), which combine an RNN with domain-specific knowledge encoded as software and system action templates. Compared to existing end-to-end approaches, HCNs considerably reduce the amount of training data required, while retaining the key benefit of inferring a latent representation of dialog state. In addition, HCNs can be optimized with supervised learning, reinforcement learning, or a mixture of both. HCNs attain state-of-the-art performance on the bAbI dialog dataset (Bordes and Weston, 2016), and outperform two commercially deployed customer-facing dialog systems at our company.

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May I take your order? A Neural Model for Extracting Structured Information from Conversations
Baolin Peng | Michael Seltzer | Y.C. Ju | Geoffrey Zweig | Kam-Fai Wong
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In this paper we tackle a unique and important problem of extracting a structured order from the conversation a customer has with an order taker at a restaurant. This is motivated by an actual system under development to assist in the order taking process. We develop a sequence-to-sequence model that is able to map from unstructured conversational input to the structured form that is conveyed to the kitchen and appears on the customer receipt. This problem is critically different from other tasks like machine translation where sequence-to-sequence models have been used: the input includes two sides of a conversation; the output is highly structured; and logical manipulations must be performed, for example when the customer changes his mind while ordering. We present a novel sequence-to-sequence model that incorporates a special attention-memory gating mechanism and conversational role markers. The proposed model improves performance over both a phrase-based machine translation approach and a standard sequence-to-sequence model.

2015

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Fast and easy language understanding for dialog systems with Microsoft Language Understanding Intelligent Service (LUIS)
Jason D. Williams | Eslam Kamal | Mokhtar Ashour | Hani Amr | Jessica Miller | Geoff Zweig
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Language Models for Image Captioning: The Quirks and What Works
Jacob Devlin | Hao Cheng | Hao Fang | Saurabh Gupta | Li Deng | Xiaodong He | Geoffrey Zweig | Margaret Mitchell
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2013

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Joint Language and Translation Modeling with Recurrent Neural Networks
Michael Auli | Michel Galley | Chris Quirk | Geoffrey Zweig
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Linguistic Regularities in Continuous Space Word Representations
Tomas Mikolov | Wen-tau Yih | Geoffrey Zweig
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Combining Heterogeneous Models for Measuring Relational Similarity
Alisa Zhila | Wen-tau Yih | Christopher Meek | Geoffrey Zweig | Tomas Mikolov
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Polarity Inducing Latent Semantic Analysis
Wen-tau Yih | Geoffrey Zweig | John Platt
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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A Challenge Set for Advancing Language Modeling
Geoffrey Zweig | Chris J.C. Burges
Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT

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Computational Approaches to Sentence Completion
Geoffrey Zweig | John C. Platt | Christopher Meek | Christopher J.C. Burges | Ainur Yessenalina | Qiang Liu
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2009

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Multi-scale Personalization for Voice Search Applications
Daniel Bolaños | Geoffrey Zweig | Patrick Nguyen
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Learning N-Best Correction Models from Implicit User Feedback in a Multi-Modal Local Search Application
Dan Bohus | Xiao Li | Patrick Nguyen | Geoffrey Zweig
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue

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Optimal Dialog in Consumer-Rating Systems using POMDP Framework
Zhifei Li | Patrick Nguyen | Geoffrey Zweig
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue

2007

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Voice-Rate: A Dialog System for Consumer Ratings
Geoffrey Zweig | Y.C. Ju | Patrick Nguyen | Dong Yu | Ye-Yi Wang | Alex Acero
Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)

2006

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Automated Quality Monitoring for Call Centers using Speech and NLP Technologies
G. Zweig | O. Siohan | G. Saon | B. Ramabhadran | D. Povey | L. Mangu | B. Kingsbury
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Demonstrations

2001

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Information Extraction from Voicemail
Jing Huang | Geoffrey Zweig | Mukund Padmanabhan
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics