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Ramón Fernández Astudillo

6 أوراق في مجموعة PaperMetrix

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أوراق هذا المؤلف

  1. Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces

    2016 · arXiv (Cornell University)

    Recent approaches for sentiment lexicon induction have capitalized on pre-trained word embeddings that capture latent semantic properties. However, embeddings obtained by optimizing performance of a given task (e.g. predicting contextual words) are sub-optimal for other …

  2. Eat: Enhanced ASR-TTS for Self-Supervised Speech Recognition

    2021

    Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here we propose an enhanced ASR-TTS (EAT) model that incorporates two main features: 1) The ASR→TTS direction is equipped with a language model reward to penalize the …

  3. Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency

    2023 · arXiv (Cornell University)

    We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser. Conceptually, LOCCO can be viewed as a form of self-learning where the semantic parser being trained is …

  4. Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversations

    2024 · arXiv (Cornell University)

    We introduce a structured chain-of-thought (SCoT) prompting approach to generating content-grounded multi-turn question-answer conversations using a pre-trained large language model (LLM). At the core of our proposal is a structured breakdown of the complex task …

  5. Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation

    2015 · arXiv (Cornell University)

    Wang Ling, Chris Dyer, Alan W Black, Isabel Trancoso, Ramón Fermandez, Silvio Amir, Luís Marujo, Tiago Luís. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015.

  6. Cycle-consistency Training for End-to-end Speech Recognition

    2019

    This paper presents a method to train end-to-end automatic speech recognition (ASR) models using unpaired data. Although the end-to-end approach can eliminate the need for expert knowledge such as pronunciation dictionaries to build ASR systems, …