Hainan Xu


2019

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Saliency-driven Word Alignment Interpretation for Neural Machine Translation
Shuoyang Ding | Hainan Xu | Philipp Koehn
Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

Despite their original goal to jointly learn to align and translate, Neural Machine Translation (NMT) models, especially Transformer, are often perceived as not learning interpretable word alignments. In this paper, we show that NMT models do learn interpretable word alignments, which could only be revealed with proper interpretation methods. We propose a series of such methods that are model-agnostic, are able to be applied either offline or online, and do not require parameter update or architectural change. We show that under the force decoding setup, the alignments induced by our interpretation method are of better quality than fast-align for some systems, and when performing free decoding, they agree well with the alignments induced by automatic alignment tools.

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Robust Document Representations for Cross-Lingual Information Retrieval in Low-Resource Settings
Mahsa Yarmohammadi | Xutai Ma | Sorami Hisamoto | Muhammad Rahman | Yiming Wang | Hainan Xu | Daniel Povey | Philipp Koehn | Kevin Duh
Proceedings of Machine Translation Summit XVII Volume 1: Research Track

2018

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The JHU Parallel Corpus Filtering Systems for WMT 2018
Huda Khayrallah | Hainan Xu | Philipp Koehn
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This work describes our submission to the WMT18 Parallel Corpus Filtering shared task. We use a slightly modified version of the Zipporah Corpus Filtering toolkit (Xu and Koehn, 2017), which computes an adequacy score and a fluency score on a sentence pair, and use a weighted sum of the scores as the selection criteria. This work differs from Zipporah in that we experiment with using the noisy corpus to be filtered to compute the combination weights, and thus avoids generating synthetic data as in standard Zipporah.

2017

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Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora
Hainan Xu | Philipp Koehn
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We introduce Zipporah, a fast and scalable data cleaning system. We propose a novel type of bag-of-words translation feature, and train logistic regression models to classify good data and synthetic noisy data in the proposed feature space. The trained model is used to score parallel sentences in the data pool for selection. As shown in experiments, Zipporah selects a high-quality parallel corpus from a large, mixed quality data pool. In particular, for one noisy dataset, Zipporah achieves a 2.1 BLEU score improvement with using 1/5 of the data over using the entire corpus.