Jianmin Yao

Also published as: Jian-Min Yao, Jian-min Yao


Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection
Zhongqiu Li | Yu Hong | Jie Wang | Shiming He | Jianmin Yao | Guodong Zhou
Proceedings of the 29th International Conference on Computational Linguistics

We leverage cross-language data expansion and retraining to enhance neural Event Detection (abbr., ED) on English ACE corpus. Machine translation is utilized for expanding English training set of ED from that of Chinese. However, experimental results illustrate that such strategy actually results in performance degradation. The survey of translations suggests that the mistakenly-aligned triggers in the expanded data negatively influences the retraining process. We refer this phenomenon to “trigger falsification”. To overcome the issue, we apply heuristic rules for regulating the expanded data, fixing the distracting samples that contain the falsified triggers. The supplementary experiments show that the rule-based regulation is beneficial, yielding the improvement of about 1.6% F1-score for ED. We additionally prove that, instead of transfer learning from the translated ED data, the straight data combination by random pouring surprisingly performs better.

Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation
Xiaolin Xing | Yu Hong | Minhan Xu | Jianmin Yao | Guodong Zhou
Proceedings of the 29th International Conference on Computational Linguistics

Training Neural Machine Translation (NMT) models suffers from sparse parallel data, in the infrequent translation scenarios towards low-resource source languages. The existing solutions primarily concentrate on the utilization of Parent-Child (PC) transfer learning. It transfers well-trained NMT models on high-resource languages (namely Parent NMT) to low-resource languages, so as to produce Child NMT models by fine-tuning. It has been carefully demonstrated that a variety of PC variants yield significant improvements for low-resource NMT. In this paper, we intend to enhance PC-based NMT by a bidirectionally-adaptive learning strategy. Specifically, we divide inner constituents (6 transformers) of Parent encoder into two “teams”, i.e., T1 and T2. During representation learning, T1 learns to encode low-resource languages conditioned on bilingual shareable latent space. Generative adversarial network and masked language modeling are used for space-shareable encoding. On the other hand, T2 is straightforwardly transferred to low-resource languages, and fine-tuned together with T1 for low-resource translation. Briefly, T1 and T2 take actions separately for different goals. The former aims to adapt to characteristics of low-resource languages during encoding, while the latter adapts to translation experiences learned from high-resource languages. We experiment on benchmark corpora SETIMES, conducting low-resource NMT for Albanian (Sq), Macedonian (Mk), Croatian (Hr) and Romanian (Ro). Experimental results show that our method yields substantial improvements, which allows the NMT performance to reach BLEU4-scores of 62.24%, 56.93%, 50.53% and 54.65% for Sq, Mk, Hr and Ro, respectively.


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Feature-level Incongruence Reduction for Multimodal Translation
Zhifeng Li | Yu Hong | Yuchen Pan | Jian Tang | Jianmin Yao | Guodong Zhou
Proceedings of the Second Workshop on Advances in Language and Vision Research

Caption translation aims to translate image annotations (captions for short). Recently, Multimodal Neural Machine Translation (MNMT) has been explored as the essential solution. Besides of linguistic features in captions, MNMT allows visual(image) features to be used. The integration of multimodal features reinforces the semantic representation and considerably improves translation performance. However, MNMT suffers from the incongruence between visual and linguistic features. To overcome the problem, we propose to extend MNMT architecture with a harmonization network, which harmonizes multimodal features(linguistic and visual features)by unidirectional modal space conversion. It enables multimodal translation to be carried out in a seemingly monomodal translation pipeline. We experiment on the golden Multi30k-16 and 17. Experimental results show that, compared to the baseline,the proposed method yields the improvements of 2.2% BLEU for the scenario of translating English captions into German (En→De) at best,7.6% for the case of English-to-French translation(En→Fr) and 1.5% for English-to-Czech(En→Cz). The utilization of harmonization network leads to the competitive performance to the-state-of-the-art.


Don’t Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction
Zhenkai Wei | Yu Hong | Bowei Zou | Meng Cheng | Jianmin Yao
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The current aspect extraction methods suffer from boundary errors. In general, these errors lead to a relatively minor difference between the extracted aspects and the ground-truth. However, they hurt the performance severely. In this paper, we propose to utilize a pointer network for repositioning the boundaries. Recycling mechanism is used, which enables the training data to be collected without manual intervention. We conduct the experiments on the benchmark datasets SE14 of laptop and SE14-16 of restaurant. Experimental results show that our method achieves substantial improvements over the baseline, and outperforms state-of-the-art methods.


Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition
Yu Hong | Yang Xu | Huibin Ruan | Bowei Zou | Jianmin Yao | Guodong Zhou
Proceedings of the 27th International Conference on Computational Linguistics

Event relation recognition is a challenging language processing task. It is required to determine the relation class of a pair of query events, such as causality, under the condition that there isn’t any reliable clue for use. We follow the traditional statistical approach in this paper, speculating the relation class of the target events based on the relation-class distributions on the similar events. There is minimal supervision used during the speculation process. In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events. We test our method on the ACE-R2 corpus and compared our model with the fully-supervised neural network models. Experimental results show that we achieve a comparable performance to CNN while slightly better than LSTM.

Using active learning to expand training data for implicit discourse relation recognition
Yang Xu | Yu Hong | Huibin Ruan | Jianmin Yao | Min Zhang | Guodong Zhou
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans. Implicit relation recognition is challenging due to the lack of explicit relational clues. The increasingly popular neural network techniques have been proven effective for semantic encoding, whereby widely employed to boost semantic relation discrimination. However, learning to predict semantic relations at a deep level heavily relies on a great deal of training data, but the scale of the publicly available data in this field is limited. In this paper, we follow Rutherford and Xue (2015) to expand the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives. On the basis, we carry out an experiment of sampling, in which a simple active learning approach is used, so as to take the informative instances for data expansion. The goal is to verify whether the selective use of external data not only reduces the time consumption of retraining but also ensures a better system performance. Using the expanded training data, we retrain a convolutional neural network (CNN) based classifer which is a simplified version of Qin et al. (2016)’s stacking gated relation recognizer. Experimental results show that expanding the training set with small-scale carefully-selected external data yields substantial performance gain, with the improvements of about 4% for accuracy and 3.6% for F-score. This allows a weak classifier to achieve a comparable performance against the state-of-the-art systems.


Image-Image Search for Comparable Corpora Construction
Yu Hong | Liang Yao | Mengyi Liu | Tongtao Zhang | Wenxuan Zhou | Jianmin Yao | Heng Ji
Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6)

We present a novel method of comparable corpora construction. Unlike the traditional methods which heavily rely on linguistic features, our method only takes image similarity into consid-eration. We use an image-image search engine to obtain similar images, together with the cap-tions in source language and target language. On the basis, we utilize captions of similar imag-es to construct sentence-level bilingual corpora. Experiments on 10,371 target captions show that our method achieves a precision of 0.85 in the top search results.


Effective Selection of Translation Model Training Data
Le Liu | Yu Hong | Hao Liu | Xing Wang | Jianmin Yao
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

An Iterative Link-based Method for Parallel Web Page Mining
Le Liu | Yu Hong | Jun Lu | Jun Lang | Heng Ji | Jianmin Yao
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


Thread Cleaning and Merging for Microblog Topic Detection
Jianfeng Zhang | Yunqing Xia | Bin Ma | Jianmin Yao | Yu Hong
Proceedings of 5th International Joint Conference on Natural Language Processing

Using Cross-Entity Inference to Improve Event Extraction
Yu Hong | Jianfeng Zhang | Bin Ma | Jianmin Yao | Guodong Zhou | Qiaoming Zhu
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


Jumping Distance based Chinese Person Name Disambiguation
Yu Hong | Fei Pei | Yue-hui Yang | Jian-min Yao | Qiao-ming Zhu
CIPS-SIGHAN Joint Conference on Chinese Language Processing

A Novel Method for Bilingual Web Page Acquisition from Search Engine Web Records
Yanhui Feng | Yu Hong | Zhenxiang Yan | Jianmin Yao | Qiaoming Zhu
Coling 2010: Posters

Negative Feedback: The Forsaken Nature Available for Re-ranking
Yu Hong | Qing-qing Cai | Song Hua | Jian-min Yao | Qiao-ming Zhu
Coling 2010: Posters


An Equivalent Pseudoword Solution to Chinese Word Sense Disambiguation
Zhimao Lu | Haifeng Wang | Jianmin Yao | Ting Liu | Sheng Li
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

A Visualization method for machine translation evaluation results
Jian-Min Yao | Yun-Qian Qu | Qiao-Ming Zhu | Jing Zhang
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation

Performance Analysis and Visualization of Machine Translation Evaluation
Jianmin Yao | Yunqian Qu | Qiang Lv | Qiaoming Zhu | Jing Zhang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 11, Number 3, September 2006: Special Issue on Selected Papers from ROCLING XVII


An Automatic Evaluation Method for Localization Oriented Lexicalised EBMT System
Jianmin Yao | Ming Zhou | Tiejun Zhao | Hao Yu | Sheng Li
COLING 2002: The 19th International Conference on Computational Linguistics

Automatic Information Transfer between English and Chinese
Jianmin Yao | Hao Yu | Tiejun Zhao | Xiaohong Li
COLING-02: Machine Translation in Asia


Statistics Based Hybrid Approach to Chinese Base Phrase Identification
Tie-jun Zhao | Mu-yun Yang | Fang Liu | Jian-min Yao | Hao Yu
Second Chinese Language Processing Workshop