Xuan Zhang


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Approaching Sign Language Gloss Translation as a Low-Resource Machine Translation Task
Xuan Zhang | Kevin Duh
Proceedings of the 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL)

A cascaded Sign Language Translation system first maps sign videos to gloss annotations and then translates glosses into a spoken languages. This work focuses on the second-stage gloss translation component, which is challenging due to the scarcity of publicly available parallel data. We approach gloss translation as a low-resource machine translation task and investigate two popular methods for improving translation quality: hyperparameter search and backtranslation. We discuss the potentials and pitfalls of these methods based on experiments on the RWTH-PHOENIX-Weather 2014T dataset.


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Reproducible and Efficient Benchmarks for Hyperparameter Optimization of Neural Machine Translation Systems
Xuan Zhang | Kevin Duh
Transactions of the Association for Computational Linguistics, Volume 8

Hyperparameter selection is a crucial part of building neural machine translation (NMT) systems across both academia and industry. Fine-grained adjustments to a model’s architecture or training recipe can mean the difference between a positive and negative research result or between a state-of-the-art and underperforming system. While recent literature has proposed methods for automatic hyperparameter optimization (HPO), there has been limited work on applying these methods to neural machine translation (NMT), due in part to the high costs associated with experiments that train large numbers of model variants. To facilitate research in this space, we introduce a lookup-based approach that uses a library of pre-trained models for fast, low cost HPO experimentation. Our contributions include (1) the release of a large collection of trained NMT models covering a wide range of hyperparameters, (2) the proposal of targeted metrics for evaluating HPO methods on NMT, and (3) a reproducible benchmark of several HPO methods against our model library, including novel graph-based and multiobjective methods.

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Machine Translation System Selection from Bandit Feedback
Jason Naradowsky | Xuan Zhang | Kevin Duh
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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SEMA: Text Simplification Evaluation through Semantic Alignment
Xuan Zhang | Huizhou Zhao | KeXin Zhang | Yiyang Zhang
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications

Text simplification is an important branch of natural language processing. At present, methods used to evaluate the semantic retention of text simplification are mostly based on string matching. We propose the SEMA (text Simplification Evaluation Measure through Semantic Alignment), which is based on semantic alignment. Semantic alignments include complete alignment, partial alignment and hyponymy alignment. Our experiments show that the evaluation results of SEMA have a high consistency with human evaluation for the simplified corpus of Chinese and English news texts.


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HABLex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation
Brian Thompson | Rebecca Knowles | Xuan Zhang | Huda Khayrallah | Kevin Duh | Philipp Koehn
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Bilingual lexicons are valuable resources used by professional human translators. While these resources can be easily incorporated in statistical machine translation, it is unclear how to best do so in the neural framework. In this work, we present the HABLex dataset, designed to test methods for bilingual lexicon integration into neural machine translation. Our data consists of human generated alignments of words and phrases in machine translation test sets in three language pairs (Russian-English, Chinese-English, and Korean-English), resulting in clean bilingual lexicons which are well matched to the reference. We also present two simple baselines - constrained decoding and continued training - and an improvement to continued training to address overfitting.

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Curriculum Learning for Domain Adaptation in Neural Machine Translation
Xuan Zhang | Pamela Shapiro | Gaurav Kumar | Paul McNamee | Marine Carpuat | Kevin Duh
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.


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The JHU/KyotoU Speech Translation System for IWSLT 2018
Hirofumi Inaguma | Xuan Zhang | Zhiqi Wang | Adithya Renduchintala | Shinji Watanabe | Kevin Duh
Proceedings of the 15th International Conference on Spoken Language Translation

This paper describes the Johns Hopkins University (JHU) and Kyoto University submissions to the Speech Translation evaluation campaign at IWSLT2018. Our end-to-end speech translation systems are based on ESPnet and implements an attention-based encoder-decoder model. As comparison, we also experiment with a pipeline system that uses independent neural network systems for both the speech transcription and text translation components. We find that a transfer learning approach that bootstraps the end-to-end speech translation system with speech transcription system’s parameters is important for training on small datasets.


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Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings
Deyu Zhou | Xuan Zhang | Yulan He
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

To extract structured representations of newsworthy events from Twitter, unsupervised models typically assume that tweets involving the same named entities and expressed using similar words are likely to belong to the same event. Hence, they group tweets into clusters based on the co-occurrence patterns of named entities and topical keywords. However, there are two main limitations. First, they require the number of events to be known beforehand, which is not realistic in practical applications. Second, they don’t recognise that the same named entity might be referred to by multiple mentions and tweets using different mentions would be wrongly assigned to different events. To overcome these limitations, we propose a non-parametric Bayesian mixture model with word embeddings for event extraction, in which the number of events can be inferred automatically and the issue of lexical variations for the same named entity can be dealt with properly. Our model has been evaluated on three datasets with sizes ranging between 2,499 and over 60 million tweets. Experimental results show that our model outperforms the baseline approach on all datasets by 5-8% in F-measure.


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Emotion Distribution Learning from Texts
Deyu Zhou | Xuan Zhang | Yin Zhou | Quan Zhao | Xin Geng
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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The Virginia Tech System at CoNLL-2016 Shared Task on Shallow Discourse Parsing
Prashant Chandrasekar | Xuan Zhang | Saurabh Chakravarty | Arijit Ray | John Krulick | Alla Rozovskaya
Proceedings of the CoNLL-16 shared task