Yu-Hsiang Tseng


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CxLM: A Construction and Context-aware Language Model
Yu-Hsiang Tseng | Cing-Fang Shih | Pin-Er Chen | Hsin-Yu Chou | Mao-Chang Ku | Shu-Kai Hsieh
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Constructions are direct form-meaning pairs with possible schematic slots. These slots are simultaneously constrained by the embedded construction itself and the sentential context. We propose that the constraint could be described by a conditional probability distribution. However, as this conditional probability is inevitably complex, we utilize language models to capture this distribution. Therefore, we build CxLM, a deep learning-based masked language model explicitly tuned to constructions’ schematic slots. We first compile a construction dataset consisting of over ten thousand constructions in Taiwan Mandarin. Next, an experiment is conducted on the dataset to examine to what extent a pretrained masked language model is aware of the constructions. We then fine-tune the model specifically to perform a cloze task on the opening slots. We find that the fine-tuned model predicts masked slots more accurately than baselines and generates both structurally and semantically plausible word samples. Finally, we release CxLM and its dataset as publicly available resources and hope to serve as new quantitative tools in studying construction grammar.


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Exploring sentiment constructions: connecting deep learning models with linguistic construction
Shu-Kai Hsieh | Yu-Hsiang Tseng
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation

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What confuses BERT? Linguistic Evaluation of Sentiment Analysis on Telecom Customer Opinion
Cing-Fang Shih | Yu-Hsiang Tseng | Ching-Wen Yang | Pin-Er Chen | Hsin-Yu Chou | Lian-Hui Tan | Tzu-Ju Lin | Chun-Wei Wang | Shu-Kai Hsieh
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

Ever-expanding evaluative texts on online forums have become an important source of sentiment analysis. This paper proposes an aspect-based annotated dataset consisting of telecom reviews on social media. We introduce a category, implicit evaluative texts, impevals for short, to investigate how the deep learning model works on these implicit reviews. We first compare two models, BertSimple and BertImpvl, and find that while both models are competent to learn simple evaluative texts, they are confused when classifying impevals. To investigate the factors underlying the correctness of the model’s predictions, we conduct a series of analyses, including qualitative error analysis and quantitative analysis of linguistic features with logistic regressions. The results show that local features that affect the overall sentential sentiment confuse the model: multiple target entities, transitional words, sarcasm, and rhetorical questions. Crucially, these linguistic features are independent of the model’s confidence measured by the classifier’s softmax probabilities. Interestingly, the sentence complexity indicated by syntax-tree depth is not correlated with the model’s correctness. In sum, this paper sheds light on the characteristics of the modern deep learning model and when it might need more supervision through linguistic evaluations.


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From Sense to Action: A Word-Action Disambiguation Task in NLP
Shu-Kai Hsieh | Yu-Hsiang Tseng | Chiung-Yu Chiang | Richard Lian | Yong-fu Liao | Mao-Chang Ku | Ching-Fang Shih
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation

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Computational Modeling of Affixoid Behavior in Chinese Morphology
Yu-Hsiang Tseng | Shu-Kai Hsieh | Pei-Yi Chen | Sara Court
Proceedings of the 28th International Conference on Computational Linguistics

The morphological status of affixes in Chinese has long been a matter of debate. How one might apply the conventional criteria of free/bound and content/function features to distinguish word-forming affixes from bound roots in Chinese is still far from clear. Issues involving polysemy and diachronic dynamics further blur the boundaries. In this paper, we propose three quantitative features in a computational model of affixoid behavior in Mandarin Chinese. The results show that, except for in a very few cases, there are no clear criteria that can be used to identify an affix’s status in an isolating language like Chinese. A diachronic check using contextualized embeddings with the WordNet Sense Inventory also demonstrates the possible role of the polysemy of lexical roots across diachronic settings.


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Augmenting Chinese WordNet semantic relations with contextualized embeddings
Yu-Hsiang Tseng | Shu-Kai Hsieh
Proceedings of the 10th Global Wordnet Conference

Constructing semantic relations in WordNet has been a labour-intensive task, especially in a dynamic and fast-changing language environment. Combined with recent advancements of contextualized embeddings, this paper proposes the concept of morphology-guided sense vectors, which can be used to semi-automatically augment semantic relations in Chinese Wordnet (CWN). This paper (1) built sense vectors with pre-trained contextualized embedding models; (2) demonstrated the sense vectors computed were consistent with the sense distinctions made in CWN; and (3) predicted the potential semantically-related sense pairs with high accuracy by sense vectors model.

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Eigencharacter: An Embedding of Chinese Character Orthography
Yu-Hsiang Tseng | Shu-Kai Hsieh
Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)

Chinese characters are unique in its logographic nature, which inherently encodes world knowledge through thousands of years evolution. This paper proposes an embedding approach, namely eigencharacter (EC) space, which helps NLP application easily access the knowledge encoded in Chinese orthography. These EC representations are automatically extracted, encode both structural and radical information, and easily integrate with other computational models. We built EC representations of 5,000 Chinese characters, investigated orthography knowledge encoded in ECs, and demonstrated how these ECs identified visually similar characters with both structural and radical information.


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Fluid Annotation: A Granularity-aware Annotation Tool for Chinese Word Fluidity
Shu-Kai Hsieh | Yu-Hsiang Tseng | Chih-Yao Lee | Chiung-Yu Chiang
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)