Yu-Yin Hsu


2022

pdf bib
Proceedings of the Workshop on Cognitive Aspects of the Lexicon
Michael Zock | Emmanuele Chersoni | Yu-Yin Hsu | Enrico Santus
Proceedings of the Workshop on Cognitive Aspects of the Lexicon

pdf bib
(In)Alienable Possession in Mandarin Relative Clauses
Deran Kong | Yu-Yin Hsu
Proceedings of the Workshop on Cognitive Aspects of the Lexicon

Inalienable possession differs from alienable possession in that, in the former – e.g., kinships and part-whole relations – there is an intrinsic semantic dependency between the possessor and possessum. This paper reports two studies that used acceptability-judgment tasks to investigate whether native Mandarin speakers experienced different levels of interpretational costs while resolving different types of possessive relations, i.e., inalienable possessions (kinship terms and body parts) and alienable ones, expressed within relative clauses. The results show that sentences received higher acceptability ratings when body parts were the possessum as compared to sentences with alienable possessum, indicating that the inherent semantic dependency facilitates the resolution. However, inalienable kinship terms received the lowest acceptability ratings. We argue that this was because the kinship terms, which had the [+human] feature and appeared at the beginning of the experimental sentences, tended to be interpreted as the subject in shallow processing; these features contradicted the semantic-syntactic requirements of the experimental sentences.

pdf bib
HkAmsters at CMCL 2022 Shared Task: Predicting Eye-Tracking Data from a Gradient Boosting Framework with Linguistic Features
Lavinia Salicchi | Rong Xiang | Yu-Yin Hsu
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Eye movement data are used in psycholinguistic studies to infer information regarding cognitive processes during reading. In this paper, we describe our proposed method for the Shared Task of Cognitive Modeling and Computational Linguistics (CMCL) 2022 - Subtask 1, which involves data from multiple datasets on 6 languages. We compared different regression models using features of the target word and its previous word, and target word surprisal as regression features. Our final system, using a gradient boosting regressor, achieved the lowest mean absolute error (MAE), resulting in the best system of the competition.

pdf bib
Discovering Financial Hypernyms by Prompting Masked Language Models
Bo Peng | Emmanuele Chersoni | Yu-Yin Hsu | Chu-Ren Huang
Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022

With the rising popularity of Transformer-based language models, several studies have tried to exploit their masked language modeling capabilities to automatically extract relational linguistic knowledge, although this kind of research has rarely investigated semantic relations in specialized domains. The present study aims at testing a general-domain and a domain-adapted Transformer models on two datasets of financial term-hypernym pairs using the prompt methodology. Our results show that the differences of prompts impact critically on models’ performance, and that domain adaptation on financial text generally improves the capacity of the models to associate the target terms with the right hypernyms, although the more successful models are those retaining a general-domain vocabulary.

2021

pdf bib
Modeling the Influence of Verb Aspect on the Activation of Typical Event Locations with BERT
Won Ik Cho | Emmanuele Chersoni | Yu-Yin Hsu | Chu-Ren Huang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

pdf bib
Is Domain Adaptation Worth Your Investment? Comparing BERT and FinBERT on Financial Tasks
Bo Peng | Emmanuele Chersoni | Yu-Yin Hsu | Chu-Ren Huang
Proceedings of the Third Workshop on Economics and Natural Language Processing

With the recent rise in popularity of Transformer models in Natural Language Processing, research efforts have been dedicated to the development of domain-adapted versions of BERT-like architectures. In this study, we focus on FinBERT, a Transformer model trained on text from the financial domain. By comparing its performances with the original BERT on a wide variety of financial text processing tasks, we found continual pretraining from the original model to be the more beneficial option. Domain-specific pretraining from scratch, conversely, seems to be less effective.

2018

pdf bib
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation
Stephen Politzer-Ahles | Yu-Yin Hsu | Chu-Ren Huang | Yao Yao
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

pdf bib
Prosodic Organization and Focus Realization in Taiwan Mandarin
Yu-Yin Hsu | James German
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

pdf bib
Whether and How Mandarin Sandhied Tone 3 and Underlying Tone 2 differ in Terms of Vowel Quality?
Yu-Jung Lin | Yu-Yin Hsu
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

pdf bib
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing
Stephen Politzer-Ahles | Yu-Yin Hsu | Chu-Ren Huang | Yao Yao
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing

pdf bib
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation
Stephen Politzer-Ahles | Yu-Yin Hsu | Chu-Ren Huang | Yao Yao
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation

2012

pdf bib
UBIU for Multilingual Coreference Resolution in OntoNotes
Desislava Zhekova | Sandra Kübler | Joshua Bonner | Marwa Ragheb | Yu-Yin Hsu
Joint Conference on EMNLP and CoNLL - Shared Task