BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation

Liu, Dayiheng; Fu, Jie; Qu, Qian; Lv, Jiancheng

doi:10.1109/taslp.2019.2943018

Cited by 34 publications

(26 citation statements)

References 23 publications

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“…We follow the work of [6] and extend the backward and forward generators to adapt to the loanword identification task. In our study, we use the loanwords of a specific language as the lexical constraint to generate more training data.…”

Section: Generatorsmentioning

confidence: 99%

“…As a common used method to alleviate the data sparseness, data augmentation is one of the most popular methods in this topic. For example, Liu et al [6] proposed to use a GAN model consisting of two generators and one discriminator to produce meaningful natural language sentences. Motivated by this study, we propose to use a lexical constraint-based data augmentation model to generate more training data for loanword identification.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by this study, we propose to use a lexical constraint-based data augmentation model to generate more training data for loanword identification. Different from [6], we take the loanwords in training data as a lexical constraint to produce more sentences containing the loanwords.…”

Section: Introductionmentioning

confidence: 99%

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Improving Loanword Identification in Low‐Resource Language with Data Augmentation and Multiple Feature Fusion

Zhu

Nie

2021

Computational Intelligence and Neuroscience

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Loanword identification is studied in recent years to alleviate data sparseness in several natural language processing (NLP) tasks, such as machine translation, cross-lingual information retrieval, and so on. However, recent studies on this topic usually put efforts on high-resource languages (such as Chinese, English, and Russian); for low-resource languages, such as Uyghur and Mongolian, due to the limitation of resources and lack of annotated data, loanword identification on these languages tends to have lower performance. To overcome this problem, we first propose a lexical constraint-based data augmentation method to generate training data for low-resource language loanword identification; then, a loanword identification model based on a log-linear RNN is introduced to improve the performance of low-resource loanword identification by incorporating features such as word-level embeddings, character-level embeddings, pronunciation similarity, and part-of-speech (POS) into one model. Experimental results on loanword identification in Uyghur (in this study, we mainly focus on Arabic, Chinese, Russian, and Turkish loanwords in Uyghur) showed that our proposed method achieves best performance compared with several strong baseline systems.

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Section: Generatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Loanword Identification in Low‐Resource Language with Data Augmentation and Multiple Feature Fusion

Zhu

Nie

2021

Computational Intelligence and Neuroscience

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“…The authors declare no conflict of interest. [20] x x Visual Genome Dataset [21] x x x 19.9 13.7 13.1 [22] x x x x [23] x [35] x x Recall Evaluation metric [36] x x OI, VG, VRD [37] x x X X 71.6 51.8 37.1 26.5 24.3 [38] x x APRC, CSMC [39] x x x x F-1 score metrics 21.6 [45] x x x x IAPRTC-12 [46] x x x x [47] x x x x x R [48] x…”

Section: Conflicts Of Interestmentioning

confidence: 99%

A Systematic Literature Review on Image Captioning

Staniūtė

Šešok

2019

Applied Sciences

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Natural language problems have already been investigated for around five years. Recent progress in artificial intelligence (AI) has greatly improved the performance of models. However, the results are still not sufficiently satisfying. Machines cannot imitate human brains and the way they communicate, so it remains an ongoing task. Due to the increasing amount of information on this topic, it is very difficult to keep on track with the newest researches and results achieved in the image captioning field. In this study a comprehensive Systematic Literature Review (SLR) provides a brief overview of improvements in image captioning over the last four years. The main focus of the paper is to explain the most common techniques and the biggest challenges in image captioning and to summarize the results from the newest papers. Inconsistent comparison of results achieved in image captioning was noticed during this study and hence the awareness of incomplete data collection is raised in this paper. Therefore, it is very important to compare results of a newly created model produced with the newest information and not only with the state of the art methods. This SLR is a source of such information for researchers in order for them to be precisely correct on result comparison before publishing new achievements in the image caption generation field.

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“…Text infilling aims at filling in the missing part of a sentence or paragraph by making use of the past and future information around the missing part, which can be used in many real-world natural language generation scenarios, for example, fill-in-the-blank image captioning (Sun et al, 2017), lexically constrained sentence generation (Liu et al, 2018b), missing value reconstruction (e.g. for damaged or historical documents) (Berglund et al, 2015), acrostic poetry generation (Liu et al, 2018a), and text representation learning (Devlin et al, 2018).…”

Section: Introductionmentioning

confidence: 99%