Yating Yang


ASCM: An Answer Space Clustered Prompting Method without Answer Engineering
Zhen Wang | Yating Yang | Zhou Xi | Bo Ma | Lei Wang | Rui Dong | Azmat Anwar
Findings of the Association for Computational Linguistics: ACL 2022

Prompt-based learning, which exploits knowledge from pre-trained language models by providing textual prompts and designing appropriate answer-category mapping methods, has achieved impressive successes on few-shot text classification and natural language inference (NLI). Because of the diverse linguistic expression, there exist many answer tokens for the same category. However, both manual answer design and automatic answer search constrain answer space and therefore hardly achieve ideal performance. To address this issue, we propose an answer space clustered prompting model (ASCM) together with a synonym initialization method (SI) which automatically categorizes all answer tokens in a semantic-clustered embedding space. We also propose a stable semi-supervised method named stair learning (SL) that orderly distills knowledge from better models to weaker models. Extensive experiments demonstrate that our ASCM+SL significantly outperforms existing state-of-the-art techniques in few-shot settings.


基于时间注意力胶囊网络的维吾尔语情感分类模型(Uyghur Sentiment Classification Model Based on Temporal Attention Capsule Networks)
Hantian Luo (罗涵天) | Yating Yang (杨雅婷) | Rui Dong (董瑞) | Bo Ma (马博)
Proceedings of the 20th Chinese National Conference on Computational Linguistics



Multi-Task Neural Model for Agglutinative Language Translation
Yirong Pan | Xiao Li | Yating Yang | Rui Dong
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Neural machine translation (NMT) has achieved impressive performance recently by using large-scale parallel corpora. However, it struggles in the low-resource and morphologically-rich scenarios of agglutinative language translation task. Inspired by the finding that monolingual data can greatly improve the NMT performance, we propose a multi-task neural model that jointly learns to perform bi-directional translation and agglutinative language stemming. Our approach employs the shared encoder and decoder to train a single model without changing the standard NMT architecture but instead adding a token before each source-side sentence to specify the desired target outputs of the two different tasks. Experimental results on Turkish-English and Uyghur-Chinese show that our proposed approach can significantly improve the translation performance on agglutinative languages by using a small amount of monolingual data.


A Neural Network Based Model for Loanword Identification in Uyghur
Chenggang Mi | Yating Yang | Lei Wang | Xi Zhou | Tonghai Jiang
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings
Chenggang Mi | Yating Yang | Lei Wang | Xi Zhou | Tonghai Jiang
Proceedings of the 27th International Conference on Computational Linguistics

To enrich vocabulary of low resource settings, we proposed a novel method which identify loanwords in monolingual corpora. More specifically, we first use cross-lingual word embeddings as the core feature to generate semantically related candidates based on comparable corpora and a small bilingual lexicon; then, a log-linear model which combines several shallow features such as pronunciation similarity and hybrid language model features to predict the final results. In this paper, we use Uyghur as the receipt language and try to detect loanwords in four donor languages: Arabic, Chinese, Persian and Russian. We conduct two groups of experiments to evaluate the effectiveness of our proposed approach: loanword identification and OOV translation in four language pairs and eight translation directions (Uyghur-Arabic, Arabic-Uyghur, Uyghur-Chinese, Chinese-Uyghur, Uyghur-Persian, Persian-Uyghur, Uyghur-Russian, and Russian-Uyghur). Experimental results on loanword identification show that our method outperforms other baseline models significantly. Neural machine translation models integrating results of loanword identification experiments achieve the best results on OOV translation(with 0.5-0.9 BLEU improvements)


Log-linear Models for Uyghur Segmentation in Spoken Language Translation
Chenggang Mi | Yating Yang | Rui Dong | Xi Zhou | Lei Wang | Xiao Li | Tonghai Jiang
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach. Instead of learning model only from monolingual annotated corpus, this approach optimizes Uyghur segmentation for spoken translation based on both bilingual and monolingual corpus. Our approach relies on several features such as traditional conditional random field (CRF) feature, bilingual word alignment feature and monolingual suffixword co-occurrence feature. Experimental results shown that our proposed segmentation model for Uyghur spoken translation achieved 1.6 BLEU score improvements compared with the state-of-the-art baseline.


Recurrent Neural Network Based Loanwords Identification in Uyghur
Chenggang Mi | Yating Yang | Xi Zhou | Lei Wang | Xiao Li | Tonghai Jiang
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers

A Bilingual Discourse Corpus and Its Applications
Yang Liu | Jiajun Zhang | Chengqing Zong | Yating Yang | Xi Zhou
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Existing discourse research only focuses on the monolingual languages and the inconsistency between languages limits the power of the discourse theory in multilingual applications such as machine translation. To address this issue, we design and build a bilingual discource corpus in which we are currently defining and annotating the bilingual elementary discourse units (BEDUs). The BEDUs are then organized into hierarchical structures. Using this discourse style, we have annotated nearly 20K LDC sentences. Finally, we design a bilingual discourse based method for machine translation evaluation and show the effectiveness of our bilingual discourse annotations.


Discriminative Learning with Natural Annotations: Word Segmentation as a Case Study
Wenbin Jiang | Meng Sun | Yajuan Lü | Yating Yang | Qun Liu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)