Papers by Mireia Farrús

4 papers
TEMA: Token Embeddings Mapping for Enriching Low-Resource Language Models (2024.emnlp-main)

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Challenge: Low-resource languages, that is, languages that do not have a massive amount of text, risk being almost excluded from the possibility of having good NLP applications.
Approach: They propose an algorithm that maps token embeddings of a richly pre-trained model to a poorly trained model and creates a more complex model.
Outcome: The proposed model reduces perplexity and is competitive or better for the most semantic tasks.
Recycle Your Wav2Vec2 Codebook: A Speech Perceiver for Keyword Spotting (2022.coling-1)

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Challenge: Pretraining a keyword Spotting model with a pretraining encoder is expensive and requires a quadratic cost.
Approach: They propose to recycle phonetic information encoded in wav2vec2.0's latent codebook, which has been typically thrown away after pretraining.
Outcome: The proposed model can be initialized with phonetic embeddings, and it delivers accuracy gains at no latency costs.
Frequency Balanced Datasets Lead to Better Language Models (2023.findings-emnlp)

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Challenge: Existing evidence that high-frequency tokens in pretraining data might bias learning, causing undesired effects, is not clear.
Approach: They propose a sampling algorithm that iteratively assesses token frequencies and removes sentences that contain still high-frequency tokens, resulting in a balanced dataset.
Outcome: The proposed method reduces the amount of pre-training data required for training attention-based transformer language models by up to three times.
Compilation of Corpora for the Study of the Information Structure–Prosody Interface (L18-1)

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Challenge: empirical studies on the Information Structure-prosody interface are scarce . thematicity defines how content is packaged in terms of "what is being talked about" a different view on thematicality is advocated by I. Mel'uk in the context of the MTT.
Approach: They propose a method for the compilation of annotated corpora to study the correspondence between Information Structure and prosody.
Outcome: The proposed method is applied to a corpus of read speech in English annotated with hierarchical thematicity and automatically extracted prosodic parameters.

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