Xiaolin Wang


2022

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Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task
Liang Wen | Juan Li | Houfeng Wang | Yingwei Luo | Xiaolin Wang | Xiaodong Zhang | Zhicong Cheng | Dawei Yin
Proceedings of the 29th International Conference on Computational Linguistics

Answer selection task requires finding appropriate answers to questions from informative but crowdsourced candidates. A key factor impeding its solution by current answer selection approaches is the redundancy and lengthiness issues of crowdsourced answers. Recently, Deng et al. (2020) constructed a new dataset, WikiHowQA, which contains a corresponding reference summary for each original lengthy answer. And their experiments show that leveraging the answer summaries helps to attend the essential information in original lengthy answers and improve the answer selection performance under certain circumstances. However, when given a question and a set of long candidate answers, human beings could effortlessly identify the correct answer without the aid of additional answer summaries since the original answers contain all the information volume that answer summaries contain. In addition, pretrained language models have been shown superior or comparable to human beings on many natural language processing tasks. Motivated by those, we design a series of neural models, either pretraining-based or non-pretraining-based, to check wether the additional answer summaries are helpful for ranking the relevancy degrees of question-answer pairs on WikiHowQA dataset. Extensive automated experiments and hand analysis show that the additional answer summaries are not useful for achieving the best performance.

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A Multimodal Simultaneous Interpretation Prototype: Who Said What
Xiaolin Wang | Masao Utiyama | Eiichiro Sumita
Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)

“Who said what” is essential for users to understand video streams that have more than one speaker, but conventional simultaneous interpretation systems merely present “what was said” in the form of subtitles. Because the translations unavoidably have delays and errors, users often find it difficult to trace the subtitles back to speakers. To address this problem, we propose a multimodal SI system that presents users “who said what”. Our system takes audio-visual approaches to recognize the speaker of each sentence, and then annotates its translation with the textual tag and face icon of the speaker, so that users can quickly understand the scenario. Furthermore, our system is capable of interpreting video streams in real-time on a single desktop equipped with two Quadro RTX 4000 GPUs owing to an efficient sentence-based architecture.

2019

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Online Sentence Segmentation for Simultaneous Interpretation using Multi-Shifted Recurrent Neural Network
Xiaolin Wang | Masao Utiyama | Eiichiro Sumita
Proceedings of Machine Translation Summit XVII: Research Track

2018

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CytonMT: an Efficient Neural Machine Translation Open-source Toolkit Implemented in C++
Xiaolin Wang | Masao Utiyama | Eiichiro Sumita
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

This paper presents an open-source neural machine translation toolkit named CytonMT. The toolkit is built from scratch only using C++ and NVIDIA’s GPU-accelerated libraries. The toolkit features training efficiency, code simplicity and translation quality. Benchmarks show that cytonMT accelerates the training speed by 64.5% to 110.8% on neural networks of various sizes, and achieves competitive translation quality.

2017

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Empirical Study of Dropout Scheme for Neural Machine Translation
Xiaolin Wang | Masao Utiyama | Eiichiro Sumita
Proceedings of Machine Translation Summit XVI: Research Track

2016

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A Prototype Automatic Simultaneous Interpretation System
Xiaolin Wang | Andrew Finch | Masao Utiyama | Eiichiro Sumita
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

Simultaneous interpretation allows people to communicate spontaneously across language boundaries, but such services are prohibitively expensive for the general public. This paper presents a fully automatic simultaneous interpretation system to address this problem. Though the development is still at an early stage, the system is capable of keeping up with the fastest of the TED speakers while at the same time delivering high-quality translations. We believe that the system will become an effective tool for facilitating cross-lingual communication in the future.

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Target-Bidirectional Neural Models for Machine Transliteration
Andrew Finch | Lemao Liu | Xiaolin Wang | Eiichiro Sumita
Proceedings of the Sixth Named Entity Workshop

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An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation
Xiaolin Wang | Andrew Finch | Masao Utiyama | Eiichiro Sumita
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

Simultaneous interpretation is a very challenging application of machine translation in which the input is a stream of words from a speech recognition engine. The key problem is how to segment the stream in an online manner into units suitable for translation. The segmentation process proceeds by calculating a confidence score for each word that indicates the soundness of placing a sentence boundary after it, and then heuristics are employed to determine the position of the boundaries. Multiple variants of the confidence scoring method and segmentation heuristics were studied. Experimental results show that the best performing strategy is not only efficient in terms of average latency per word, but also achieved end-to-end translation quality close to an offline baseline, and close to oracle segmentation.

2015

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Hierarchical Phrase-based Stream Decoding
Andrew Finch | Xiaolin Wang | Masao Utiyama | Eiichiro Sumita
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Leave-one-out Word Alignment without Garbage Collector Effects
Xiaolin Wang | Masao Utiyama | Andrew Finch | Taro Watanabe | Eiichiro Sumita
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Neural Network Transduction Models in Transliteration Generation
Andrew Finch | Lemao Liu | Xiaolin Wang | Eiichiro Sumita
Proceedings of the Fifth Named Entity Workshop

2014

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The NICT translation system for IWSLT 2014
Xiaolin Wang | Andrew Finch | Masao Utiyama | Taro Watanabe | Eiichiro Sumita
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes NICT’s participation in the IWSLT 2014 evaluation campaign for the TED Chinese-English translation shared-task. Our approach used a combination of phrase-based and hierarchical statistical machine translation (SMT) systems. Our focus was in several areas, specifically system combination, word alignment, and various language modeling techniques including the use of neural network joint models. Our experiments on the test set from the 2013 shared task, showed that an improvement in BLEU score can be gained in translation performance through all of these techniques, with the largest improvements coming from using large data sizes to train the language model.

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An exploration of segmentation strategies in stream decoding
Andrew Finch | Xiaolin Wang | Eiichiro Sumita
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

In this paper we explore segmentation strategies for the stream decoder a method for decoding from a continuous stream of input tokens, rather than the traditional method of decoding from sentence segmented text. The behavior of the decoder is analyzed and modifications to the decoding algorithm are proposed to improve its performance. The experimental results show our proposed decoding strategies to be effective, and add support to the original findings that this approach is capable of approaching the performance of the underlying phrase-based machine translation decoder, at useful levels of latency. Our experiments evaluated the stream decoder on a broader set of language pairs than in previous work. We found most European language pairs were similar in character, and report results on English-Chinese and English-German pairs which are of interest due to the reordering required.

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Refining Word Segmentation Using a Manually Aligned Corpus for Statistical Machine Translation
Xiaolin Wang | Masao Utiyama | Andrew Finch | Eiichiro Sumita
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Empirical Study of Unsupervised Chinese Word Segmentation Methods for SMT on Large-scale Corpora
Xiaolin Wang | Masao Utiyama | Andrew Finch | Eiichiro Sumita
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Labeled Alignment for Recognizing Textual Entailment
Xiaolin Wang | Hai Zhao | Bao-Liang Lu
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Spell Checking for Chinese
Shaohua Yang | Hai Zhao | Xiaolin Wang | Bao-liang Lu
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper presents some novel results on Chinese spell checking. In this paper, a concise algorithm based on minimized-path segmentation is proposed to reduce the cost and suit the needs of current Chinese input systems. The proposed algorithm is actually derived from a simple assumption that spelling errors often make the number of segments larger. The experimental results are quite positive and implicitly verify the effectiveness of the proposed assumption. Finally, all approaches work together to output a result much better than the baseline with 12% performance improvement.

2008

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Cross Language Text Categorization Using a Bilingual Lexicon
Ke Wu | Xiaolin Wang | Bao-Liang Lu
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I