Yinggong Zhao


2022

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Lite Unified Modeling for Discriminative Reading Comprehension
Yilin Zhao | Hai Zhao | Libin Shen | Yinggong Zhao
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

As a broad and major category in machine reading comprehension (MRC), the generalized goal of discriminative MRC is answer prediction from the given materials. However, the focuses of various discriminative MRC tasks may be diverse enough: multi-choice MRC requires model to highlight and integrate all potential critical evidence globally; while extractive MRC focuses on higher local boundary preciseness for answer extraction. Among previous works, there lacks a unified design with pertinence for the overall discriminative MRC tasks. To fill in above gap, we propose a lightweight POS-Enhanced Iterative Co-Attention Network (POI-Net) as the first attempt of unified modeling with pertinence, to handle diverse discriminative MRC tasks synchronously. Nearly without introducing more parameters, our lite unified design brings model significant improvement with both encoder and decoder components. The evaluation results on four discriminative MRC benchmarks consistently indicate the general effectiveness and applicability of our model, and the code is available at https://github.com/Yilin1111/poi-net.

2021

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What If Sentence-hood is Hard to Define: A Case Study in Chinese Reading Comprehension
Jiawei Wang | Hai Zhao | Yinggong Zhao | Libin Shen
Findings of the Association for Computational Linguistics: EMNLP 2021

Machine reading comprehension (MRC) is a challenging NLP task for it requires to carefully deal with all linguistic granularities from word, sentence to passage. For extractive MRC, the answer span has been shown mostly determined by key evidence linguistic units, in which it is a sentence in most cases. However, we recently discovered that sentences may not be clearly defined in many languages to different extents, so that this causes so-called location unit ambiguity problem and as a result makes it difficult for the model to determine which sentence exactly contains the answer span when sentence itself has not been clearly defined at all. Taking Chinese language as a case study, we explain and analyze such a linguistic phenomenon and correspondingly propose a reader with Explicit Span-Sentence Predication to alleviate such a problem. Our proposed reader eventually helps achieve a new state-of-the-art on Chinese MRC benchmark and shows great potential in dealing with other languages.

2020

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Dialogue State Tracking with Explicit Slot Connection Modeling
Yawen Ouyang | Moxin Chen | Xinyu Dai | Yinggong Zhao | Shujian Huang | Jiajun Chen
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent proposed approaches have made promising progress in dialogue state tracking (DST). However, in multi-domain scenarios, ellipsis and reference are frequently adopted by users to express values that have been mentioned by slots from other domains. To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across different domains. Given a target slot, the slot connecting mechanism in DST-SC can infer its source slot and copy the source slot value directly, thus significantly reducing the difficulty of learning and reasoning. Experimental results verify the benefits of explicit slot connection modeling, and our model achieves state-of-the-art performance on MultiWOZ 2.0 and MultiWOZ 2.1 datasets.

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Learning Numeral Embedding
Chengyue Jiang | Zhonglin Nian | Kaihao Guo | Shanbo Chu | Yinggong Zhao | Libin Shen | Kewei Tu
Findings of the Association for Computational Linguistics: EMNLP 2020

Word embedding is an essential building block for deep learning methods for natural language processing. Although word embedding has been extensively studied over the years, the problem of how to effectively embed numerals, a special subset of words, is still underexplored. Existing word embedding methods do not learn numeral embeddings well because there are an infinite number of numerals and their individual appearances in training corpora are highly scarce. In this paper, we propose two novel numeral embedding methods that can handle the out-of-vocabulary (OOV) problem for numerals. We first induce a finite set of prototype numerals using either a self-organizing map or a Gaussian mixture model. We then represent the embedding of a numeral as a weighted average of the prototype number embeddings. Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training. We evaluated our methods and showed its effectiveness on four intrinsic and extrinsic tasks: word similarity, embedding numeracy, numeral prediction, and sequence labeling.

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Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks
Chengyue Jiang | Yinggong Zhao | Shanbo Chu | Libin Shen | Kewei Tu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Neural networks can achieve impressive performance on many natural language processing applications, but they typically need large labeled data for training and are not easily interpretable. On the other hand, symbolic rules such as regular expressions are interpretable, require no training, and often achieve decent accuracy; but rules cannot benefit from labeled data when available and hence underperform neural networks in rich-resource scenarios. In this paper, we propose a type of recurrent neural networks called FA-RNNs that combine the advantages of neural networks and regular expression rules. An FA-RNN can be converted from regular expressions and deployed in zero-shot and cold-start scenarios. It can also utilize labeled data for training to achieve improved prediction accuracy. After training, an FA-RNN often remains interpretable and can be converted back into regular expressions. We apply FA-RNNs to text classification and observe that FA-RNNs significantly outperform previous neural approaches in both zero-shot and low-resource settings and remain very competitive in rich-resource settings.

2013

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Decoding with Large-Scale Neural Language Models Improves Translation
Ashish Vaswani | Yinggong Zhao | Victoria Fossum | David Chiang
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Adapting Conventional Chinese Word Segmenter for Segmenting Micro-blog Text: Combining Rule-based and Statistic-based Approaches
Ning Xi | Bin Li | Guangchao Tang | Shujian Huang | Yinggong Zhao | Hao Zhou | Xinyu Dai | Jiajun Chen
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing

2011

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Language Model Weight Adaptation Based on Cross-entropy for Statistical Machine Translation
Yinggong Zhao | Yangsheng Ji | Ning Xi | Shujian Huang | Jiajun Chen
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

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Word Alignment Combination over Multiple Word Segmentation
Ning Xi | Guangchao Tang | Boyuan Li | Yinggong Zhao
Proceedings of the ACL 2011 Student Session

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Transductive Minimum Error Rate Training for Statistical Machine Translation
Yinggong Zhao | Shujie Liu | Yangsheng Ji | Jiajun Chen | Guodong Zhou
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

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Adaptive Development Data Selection for Log-linear Model in Statistical Machine Translation
Mu Li | Yinggong Zhao | Dongdong Zhang | Ming Zhou
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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The MSRA machine translation system for IWSLT 2010
Chi-Ho Li | Nan Duan | Yinggong Zhao | Shujie Liu | Lei Cui | Mei-yuh Hwang | Amittai Axelrod | Jianfeng Gao | Yaodong Zhang | Li Deng
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign