Hao Zhang

Rochester

Other people with similar names: Hao Zhang (May refer to several people)


2023

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Long-Form Speech Translation through Segmentation with Finite-State Decoding Constraints on Large Language Models
Arya McCarthy | Hao Zhang | Shankar Kumar | Felix Stahlberg | Ke Wu
Findings of the Association for Computational Linguistics: EMNLP 2023

One challenge in speech translation is that plenty of spoken content is long-form, but short units are necessary for obtaining high-quality translations. To address this mismatch, we adapt large language models (LLMs) to split long ASR transcripts into segments that can be independently translated so as to maximize the overall translation quality. We overcome the tendency of hallucination in LLMs by incorporating finite-state constraints during decoding; these eliminate invalid outputs without requiring additional training. We discover that LLMs are adaptable to transcripts containing ASR errors through prompt-tuning or fine-tuning. Relative to a state-of-the-art automatic punctuation baseline, our best LLM improves the average BLEU by 2.9 points for English–German, English–Spanish, and English–Arabic TED talk translation in 9 test sets, just by improving segmentation.

2020

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Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities
Hao Zhang | Jae Ro | Richard Sproat
Proceedings of the 28th International Conference on Computational Linguistics

Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search. We link this problem to the classic problem of Chinese word segmentation and show the effectiveness of a tagging model based on Recurrent Neural Networks (RNNs) using characters as input. To compensate for the lack of training data, we propose a pre-training method on concatenated entity names in a large knowledge database. Pre-training improves the model by 33% and brings the sequence accuracy to 85%.

2019

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Neural Models of Text Normalization for Speech Applications
Hao Zhang | Richard Sproat | Axel H. Ng | Felix Stahlberg | Xiaochang Peng | Kyle Gorman | Brian Roark
Computational Linguistics, Volume 45, Issue 2 - June 2019

Machine learning, including neural network techniques, have been applied to virtually every domain in natural language processing. One problem that has been somewhat resistant to effective machine learning solutions is text normalization for speech applications such as text-to-speech synthesis (TTS). In this application, one must decide, for example, that 123 is verbalized as one hundred twenty three in 123 pages but as one twenty three in 123 King Ave. For this task, state-of-the-art industrial systems depend heavily on hand-written language-specific grammars. We propose neural network models that treat text normalization for TTS as a sequence-to-sequence problem, in which the input is a text token in context, and the output is the verbalization of that token. We find that the most effective model, in accuracy and efficiency, is one where the sentential context is computed once and the results of that computation are combined with the computation of each token in sequence to compute the verbalization. This model allows for a great deal of flexibility in terms of representing the context, and also allows us to integrate tagging and segmentation into the process. These models perform very well overall, but occasionally they will predict wildly inappropriate verbalizations, such as reading 3 cm as three kilometers. Although rare, such verbalizations are a major issue for TTS applications. We thus use finite-state covering grammars to guide the neural models, either during training and decoding, or just during decoding, away from such “unrecoverable” errors. Such grammars can largely be learned from data.

2018

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Fast and Accurate Reordering with ITG Transition RNN
Hao Zhang | Axel Ng | Richard Sproat
Proceedings of the 27th International Conference on Computational Linguistics

Attention-based sequence-to-sequence neural network models learn to jointly align and translate. The quadratic-time attention mechanism is powerful as it is capable of handling arbitrary long-distance reordering, but computationally expensive. In this paper, towards making neural translation both accurate and efficient, we follow the traditional pre-reordering approach to decouple reordering from translation. We add a reordering RNN that shares the input encoder with the decoder. The RNNs are trained jointly with a multi-task loss function and applied sequentially at inference time. The task of the reordering model is to predict the permutation of the input words following the target language word order. After reordering, the attention in the decoder becomes more peaked and monotonic. For reordering, we adopt the Inversion Transduction Grammars (ITG) and propose a transition system to parse input to trees for reordering. We harness the ITG transition system with RNN. With the modeling power of RNN, we achieve superior reordering accuracy without any feature engineering. In experiments, we apply the model to the task of text normalization. Compared to a strong baseline of attention-based RNN, our ITG RNN re-ordering model can reach the same reordering accuracy with only 1/10 of the training data and is 2.5x faster in decoding.

2014

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Enforcing Structural Diversity in Cube-pruned Dependency Parsing
Hao Zhang | Ryan McDonald
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Online Learning for Inexact Hypergraph Search
Hao Zhang | Liang Huang | Kai Zhao | Ryan McDonald
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Universal Dependency Annotation for Multilingual Parsing
Ryan McDonald | Joakim Nivre | Yvonne Quirmbach-Brundage | Yoav Goldberg | Dipanjan Das | Kuzman Ganchev | Keith Hall | Slav Petrov | Hao Zhang | Oscar Täckström | Claudia Bedini | Núria Bertomeu Castelló | Jungmee Lee
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Generalized Higher-Order Dependency Parsing with Cube Pruning
Hao Zhang | Ryan McDonald
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Binarized Forest to String Translation
Hao Zhang | Licheng Fang | Peng Xu | Xiaoyun Wu
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2009

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Binarization of Synchronous Context-Free Grammars
Liang Huang | Hao Zhang | Daniel Gildea | Kevin Knight
Computational Linguistics, Volume 35, Number 4, December 2009

2008

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Extracting Synchronous Grammar Rules From Word-Level Alignments in Linear Time
Hao Zhang | Daniel Gildea | David Chiang
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Bayesian Learning of Non-Compositional Phrases with Synchronous Parsing
Hao Zhang | Chris Quirk | Robert C. Moore | Daniel Gildea
Proceedings of ACL-08: HLT

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Efficient Multi-Pass Decoding for Synchronous Context Free Grammars
Hao Zhang | Daniel Gildea
Proceedings of ACL-08: HLT

2007

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Factorization of Synchronous Context-Free Grammars in Linear Time
Hao Zhang | Daniel Gildea
Proceedings of SSST, NAACL-HLT 2007 / AMTA Workshop on Syntax and Structure in Statistical Translation

2006

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Synchronous Binarization for Machine Translation
Hao Zhang | Liang Huang | Daniel Gildea | Kevin Knight
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

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Factoring Synchronous Grammars by Sorting
Daniel Gildea | Giorgio Satta | Hao Zhang
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Inducing Word Alignments with Bilexical Synchronous Trees
Hao Zhang | Daniel Gildea
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Efficient Search for Inversion Transduction Grammar
Hao Zhang | Daniel Gildea
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2005

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Stochastic Lexicalized Inversion Transduction Grammar for Alignment
Hao Zhang | Daniel Gildea
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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Machine Translation as Lexicalized Parsing with Hooks
Liang Huang | Hao Zhang | Daniel Gildea
Proceedings of the Ninth International Workshop on Parsing Technology

2004

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Syntax-Based Alignment: Supervised or Unsupervised?
Hao Zhang | Daniel Gildea
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Chinese Lexical Analysis Using Hierarchical Hidden Markov Model
Hua-Ping Zhang | Qun Liu | Xue-Qi Cheng | Hao Zhang | Hong-Kui Yu
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

2002

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Automatic Recognition of Chinese Unknown Words Based on Roles Tagging
Kevin Zhang | Qun Liu | Hao Zhang | Xue-Qi Cheng
COLING-02: The First SIGHAN Workshop on Chinese Language Processing