Papers by Yu-Yin Hsu
Towards LLM-powered Attentive Listener: A Pragmatic Approach through Quantity Self-Repair (2025.acl-short)
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| Challenge: | Quantity Maxims dictates that human speakers aim for optimal quantity of information during conversation. |
| Approach: | They propose to use heuristic path-finding to enable decoder-only LLMs to travel among multiple "Q-alternatives" and search for optimal quantity in coordination with a conversation goal. |
| Outcome: | The proposed techniques are based on heuristic path-finding and can be used to construct human-like, user-centered conversation agents. |
EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information (2024.findings-emnlp)
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| Challenge: | Existing methods for metaphor detection rely on heuristics such as Metaphor Identification Procedure (MIP) and Selection Preference Violation (SPV). |
| Approach: | They propose a cognitively motivated module that leverages the cognitive information of embodiment that can be derived from word embeddings and explicitly models the process of sensorimotor change that has been demonstrated as essential for metaphor processing. |
| Outcome: | The proposed module can improve metaphor detection compared with the heuristic MIP that has been applied previously. |
Comparing Static and Contextual Distributional Semantic Models on Intrinsic Tasks: An Evaluation on Mandarin Chinese Datasets (2024.lrec-main)
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| Challenge: | Distributional Semantics has undergone significant changes with the introduction of contextualized distributional models. |
| Approach: | They compare static and contextual distributional models for Mandarin Chinese . they find that static models are stronger for some of the classical tasks . |
| Outcome: | The proposed models perform better on some of the classical tasks that consider word meaning independent of context, while contextualized models excel in identifying semantic relations between word pairs and categorization of words into abstract semantic classes. |
Be Helpful but Don’t Talk too Much - Enhancing Helpfulness in Conversations through Relevance in Multi-Turn Emotional Support (2024.emnlp-main)
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| Challenge: | a helpful speaker should maintain an "effect-effort" tradeoff for a conversation to help and support . a study aimed to cultivate the awareness of "optimal relevance" into the cognitive process of conversation agents . |
| Approach: | They integrate the "Cognitive Relevance Principle" into emotional support agents . they found that the "relevance principle" is effective in generating human-like, helpful, harmless conversations . |
| Outcome: | The proposed method improves human-likedness and support in multi-turn conversations . the source code will be available at https://github.com/CN-Eyetk/VLESA-ORL.git . |
Not Every Metric is Equal: Cognitive Models for Predicting N400 and P600 Components During Reading Comprehension (2025.coling-main)
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| Challenge: | Several studies have focused on predicting the surprisal of a word and its reading time, but only recently, attention has been given to other components, such as P600. |
| Approach: | They propose to model reading times and ERP amplitudes using surprisal and entropy . they also propose a metric based on semantic similarity for N400 and P600 . |
| Outcome: | The proposed metric predicts reading times and ERP amplitudes in Mandarin Chinese. |
Facilitating Cross-lingual Transfer of Empathy through Language-independent Latent Diffusion: A Case Study in Chinese (2025.findings-emnlp)
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| Challenge: | Existing human empathy data are limited to English . a new study examines the pragmatic transferability of empathy across languages . |
| Approach: | a team of researchers integrate language-independent diffusion processes to facilitate the cross-lingual transfer of empathy. |
| Outcome: | The proposed method demonstrates that empathy can be transferred across languages without compromising linguistic naturalness. |
Modeling the Influence of Verb Aspect on the Activation of Typical Event Locations with BERT (2021.findings-acl)
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| Challenge: | Prior studies have shown that aspect of the main verb plays an important role in non-core semantic roles such as locations. |
| Approach: | They tested the popular language model BERT to determine whether its predictions of prototypical locations were influenced by aspect. |
| Outcome: | The language model BERT modelled the typicality of locations independently of the verb aspect. |
Investigating Aspect Features in Contextualized Embeddings with Semantic Scales and Distributional Similarity (2024.starsem-1)
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| Challenge: | Aspect is a linguistic category describing how actions and events unfold over time. |
| Approach: | They propose to use semantic projections to examine whether the vector dimensions of annotated verbs reflect human linguistic distinctions. |
| Outcome: | The proposed models encode the aspects of stativity, durativity and telicity in most of their layers, while durativité is the most challenging feature. |
Lending Eyesight to Language Models: Modeling and Probing Human scanpath through Transformer Decoder (2026.findings-acl)
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| Challenge: | Decoded language models have shown to exhibit striking parallels with human cognitive processes. |
| Approach: | They propose a plug-and-play module that transforms an autoregressive language model into an autorregressive eye model and probes it through a linguistic model. |
| Outcome: | The proposed module can be used to model human-like gaze shifts in language models. |
Emstremo: Adapting Emotional Support Response with Enhanced Emotion-Strategy Integrated Selection (2024.lrec-main)
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| Challenge: | Emstremo aims to achieve strategic control of emotional alignment by perceiving and responding to the user’s emotions. |
| Approach: | They propose to integrate strategies and emotions into a conversational emotional support agent called Emstremo that aims to achieve strategic control of emotional alignment by perceiving and responding to the user’s emotions. |
| Outcome: | Emstremo achieves strategic control of emotional alignment by perceiving and responding to the user’s emotions. |