Yu-Yin Hsu

Also published as: Yu-yin Hsu


2024

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Predicting Mandarin and Cantonese Adult Speakers’ Eye-Movement Patterns in Natural Reading
Li Junlin | Yu-Yin Hsu | Emmanuele Chersoni | Bo Peng
Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

Please find the attached PDF file for the extended abstract of our study.

2023

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Collecting and Predicting Neurocognitive Norms for Mandarin Chinese
Le Qiu | Yu-Yin Hsu | Emmanuele Chersoni
Proceedings of the 15th International Conference on Computational Semantics

Language researchers have long assumed that concepts can be represented by sets of semantic features, and have traditionally encountered challenges in identifying a feature set that could be sufficiently general to describe the human conceptual experience in its entirety. In the dataset of English norms presented by Binder et al. (2016), also known as Binder norms, the authors introduced a new set of neurobiologically motivated semantic features in which conceptual primitives were defined in terms of modalities of neural information processing. However, no comparable norms are currently available for other languages. In our work, we built the Mandarin Chinese norm by translating the stimuli used in the original study and developed a comparable collection of human ratings for Mandarin Chinese. We also conducted some experiments on the automatic prediction of the Chinese Binder Norms based on the word embeddings of the corresponding words to assess the feasibility of modeling experiential semantic features via corpus-based representations.

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Are Language Models Sensitive to Semantic Attraction? A Study on Surprisal
Yan Cong | Emmanuele Chersoni | Yu-yin Hsu | Alessandro Lenci
Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)

In psycholinguistics, semantic attraction is a sentence processing phenomenon in which a given argument violates the selectional requirements of a verb, but this violation is not perceived by comprehenders due to its attraction to another noun in the same sentence, which is syntactically unrelated but semantically sound. In our study, we use autoregressive language models to compute the sentence-level and the target phrase-level Surprisal scores of a psycholinguistic dataset on semantic attraction. Our results show that the models are sensitive to semantic attraction, leading to reduced Surprisal scores, although none of them perfectly matches the human behavioral pattern.

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Investigating the Effect of Discourse Connectives on Transformer Surprisal: Language Models Understand Connectives, Even So They Are Surprised
Yan Cong | Emmanuele Chersoni | Yu-Yin Hsu | Philippe Blache
Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP

As neural language models (NLMs) based on Transformers are becoming increasingly dominant in natural language processing, several studies have proposed analyzing the semantic and pragmatic abilities of such models. In our study, we aimed at investigating the effect of discourse connectives on NLMs with regard to Transformer Surprisal scores by focusing on the English stimuli of an experimental dataset, in which the expectations about an event in a discourse fragment could be reversed by a concessive or a contrastive connective. By comparing the Surprisal scores of several NLMs, we found that bigger NLMs show patterns similar to humans’ behavioral data when a concessive connective is used, while connective-related effects tend to disappear with a contrastive one. We have additionally validated our findings with GPT-Neo using an extended dataset, and results mostly show a consistent pattern.

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Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation
Chu-Ren Huang | Yasunari Harada | Jong-Bok Kim | Si Chen | Yu-Yin Hsu | Emmanuele Chersoni | Pranav A | Winnie Huiheng Zeng | Bo Peng | Yuxi Li | Junlin Li
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation

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Comparing and Predicting Eye-tracking Data of Mandarin and Cantonese
Junlin Li | Bo Peng | Yu-yin Hsu | Emmanuele Chersoni
Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023)

Eye-tracking data in Chinese languages present unique challenges due to the non-alphabetic and unspaced nature of the Chinese writing systems. This paper introduces the first deeply-annotated joint Mandarin-Cantonese eye-tracking dataset, from which we achieve a unified eye-tracking prediction system for both language varieties. In addition to the commonly studied first fixation duration and the total fixation duration, this dataset also includes the second fixation duration, expressing fixation patterns that are more relevant to higher-level, structural processing. A basic comparison of the features and measurements in our dataset revealed variation between Mandarin and Cantonese on fixation patterns related to word class and word position. The test of feature usefulness suggested that traditional features are less powerful in predicting the second-pass fixation, to which the linear distance to root makes a leading contribution in Mandarin. In contrast, Cantonese eye-movement behavior relies more on word position and part of speech.

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Identifying ESG Impact with Key Information
Le Qiu | Bo Peng | Jinghang Gu | Yu-Yin Hsu | Emmanuele Chersoni
Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing

The paper presents a concise summary of our work for the ML-ESG-2 shared task, exclusively on the Chinese and English datasets. ML-ESG-2 aims to ascertain the influence of news articles on corporations, specifically from an ESG perspective. To this end, we generally explored the capability of key information for impact identification and experimented with various techniques at different levels. For instance, we attempted to incorporate important information at the word level with TF-IDF, at the sentence level with TextRank, and at the document level with summarization. The final results reveal that the one with GPT-4 for summarisation yields the best predictions.

2022

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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.

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PolyU-CBS at TSAR-2022 Shared Task: A Simple, Rank-Based Method for Complex Word Substitution in Two Steps
Emmanuele Chersoni | Yu-Yin Hsu
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)

In this paper, we describe the system we presented at the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022) regarding the shared task on Lexical Simplification for English, Portuguese, and Spanish. We proposed an unsupervised approach in two steps: First, we used a masked language model with word masking for each language to extract possible candidates for the replacement of a difficult word; second, we ranked the candidates according to three different Transformer-based metrics. Finally, we determined our list of candidates based on the lowest average rank across different metrics.

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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

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(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.

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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

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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

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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

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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

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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

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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

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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

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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

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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