Papers by Jiahong Yuan

4 papers
On Attention Redundancy: A Comprehensive Study (2021.naacl-main)

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Challenge: Attention redundancy has been observed among attention heads but has not been deeply studied in the literature.
Approach: They propose a multi-layer multi-head self-attention mechanism which is widely applied in modern neural language models.
Outcome: The proposed model is useful for interpretation and model compression.
Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Training (2020.findings-emnlp)

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Challenge: Current approaches to simultaneous speech-to-speech translation accumulate more and more latencies in later sentences when the speaker talks faster.
Approach: They propose a method which generates more fluent target speech latency than the baseline . they propose to use self-adaptive translation to adjust the length of translations to accommodate different source speech rates.
Outcome: Xiong et al., 2019) show that the proposed method generates more fluent target speech latency than baseline . authors say it provides more natural communication process than speech-to-text translation . xiong and colleagues say the proposed technique is more efficient than current approaches .
Automated Tone Transcription and Clustering with Tone2Vec (2024.findings-emnlp)

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Challenge: Lexical tones play a crucial role in Sino-Tibetan languages, but current phonetic fieldwork relies on manual effort.
Approach: They propose a pitch-based similarity representations for tone transcription called Tone2Vec . they propose an open-source package that facilitates automated fieldwork and analysis .
Outcome: Experiments on dialect clustering and variance show that Tone2Vec captures fine-grained tone variation.
Transformer-based Speech Model Learns Well as Infants and Encodes Abstractions through Exemplars in the Poverty of the Stimulus Environment (2025.coling-main)

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Challenge: Existing theories of language learning for infants are inadequate, according to Chomsky . infants learn language in impoverished environments, according a new study .
Approach: They designed a series of tasks, scenarios, and metrics to simulate the POS . they found that the emerging speech model wav2vec2.0 can learn well in noisy Mandarin environments.
Outcome: The proposed model can learn in noisy and sparse Mandarin environments.

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